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A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

Papers in International Journals
Mustafa Servet Kıran, Eren Özceylan, Mesut Gündüz, Turan Paksoy
Energy Conversion and Management, Volume 53, Issue 1, January 2012, Pages 75-83

Abstract

This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.

Keywords

  • Ant colony optimization;
  • Energy demand;
  • Estimation;
  • Hybrid meta-heuritics
  • Particle swarm optimization
  • Turkey

The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem

Papers in International Journals
Mustafa Servet Kıran, Hazım İşcan, Mesut Gündüz
Neural Computing and Applications, Volume 23, Issue 1, July 2013, Pages 9-21

Abstract

The artificial bee colony (ABC) algorithm, inspired intelligent behaviors of real honey bee colonies, was introduced by Karaboğa for numerical function optimization. The basic ABC has high performance and accuracy, if the solution space of the problem is continuous. But when the solution space of the problem is discrete, the basic ABC algorithm should be modified to solve this class optimization problem. In this study, we focused on analysis of discrete ABC with neighborhood operator for well-known traveling salesman problem and different discrete neighborhood operators are replaced with solution updating equations of the basic ABC. Experimental computations show that the promising results are obtained by the discrete version of the basic ABC and which neighborhood operator is better than the others. Also, the results obtained by discrete ABC were enriched with 2- and 3-opt heuristic approaches in order to increase quality of the solutions.

Keywords

  • Artificial bee colony;
  • Discrete optimization;
  • Neighborhood operators;
  • Traveling salesman problem

A Novel Artificial Bee Colony-Based Algorithm for Solving the Numerical Optimization Problems

Papers in International Journals
Mustafa Servet Kıran, Mesut Gündüz
International Journal of Innovative Computing Information and Control, Volume 8, Issue 9, Semtember 2012, Pages 6107-6121

Abstract

Artificial Bee Colony (ABC) is one of the popular algorithms of swarm intelligence. The ABC algorithm simulates foraging and dance behaviors of real honey bee colonies. It has high performance and success for numerical benchmark optimization problems. Although solution exploration of ABC algorithm is good, exploitation to found food sources is poor. In this study, inspiring Genetic Algorithm (GA), we proposed a crossover operation-based neighbor selection technique for information sharing in the hive. Local search and exploitation abilities of the ABC were herewith improved. The experimental results show that the improved ABC algorithm generates the solutions that are significantly more closed to minimal ones than the basic ABC algorithm on the numerical optimization problems and estimation of energy demand problem.

Keywords

  • Swarm intelligence;
  • Artificial bee colony;
  • Numerical optimization,
  • Crossover operation;
  • Neighbor selection;
  • Estimation of energy demand

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Papers in International Journals
Mustafa Servet Kıran, Eren Özceylan, Mesut Gündüz, Turan PAksoy
Knowledge-based Systems, Volume 36, December 2012, Pages 93–103

Abstract

This paper proposes two new models based on artificial bee colony (ABC) and particle swarm optimization (PSO) techniques to estimate electricity energy demand in Turkey. ABC and PSO electricity energy estimation models (ABCEE and PSOEE) are developed by incorporating gross domestic product (GDP), population, import and export figures of Turkey as inputs. All models are proposed in two forms, linear and quadratic. Also different neighbor selection mechanisms are attempted for ABCEE model to increase convergence to minimum of the algorithm. In order to indicate the applicability and accuracy of the proposed models, a comparison is made with ant colony optimization (ACO) which is available for the same problem in the literature. According to obtained results, relative estimation errors of the proposed models are lower than ACO and quadratic form provides better-fit solutions than linear form due to fluctuations of the socio-economic indicators. Finally, Turkey’s electricity energy demand is projected until 2025 according to three different scenarios. 

Keywords

  • Ant colony optimization;
  • Artificial bee colony;
  • Particle swarm optimization;
  • Electricity energy estimation;
  • Swarm intelligence;
  • Artificial bee colony

A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum

Papers in International Journals
Mustafa Servet Kıran, Ömer Kaan Baykan, Mesut Gündüz
Applied Mathematics and Computation, Volume 219, Issue 4, 1 November 2012, Pages 1515–1521

Abstract

This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) and called hybrid ant particle optimization algorithm (HAP) to find global minimum. In the proposed method, ACO and PSO work separately at each iteration and produce their solutions. The best solution is selected as the global best of the system and its parameters are used to select the new position of particles and ants at the next iteration. The performance of proposed method is compared with PSO and ACO on the benchmark problems and better quality results are obtained by HAP algorithm.

Keywords

  • Particle swarm optimization;
  • Ant colony optimization;
  • Hybrid metaheuristic;
  • Global minimum

XOR-based artificial bee colony algorithm for binary optimization

Papers in International Journals
Mustafa Servet Kıran, Mesut Gündüz
Turkish Journal of Electrical Engineering & Computer Sciences, Volume 21, October 2013, Pages 2307-2328

Abstract

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

Keywords

  • Swarm intelligence;
  • Artificial bee colony;
  • Binary optimization;
  • Logic operators;
  • Uncapacitated facility location

A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems

Papers in International Journals
Mustafa Servet Kıran, Mesut Gündüz
Applied Soft Computing, Volume 13, Issue 4, April 2013, Pages 2188–2203

Abstract

This paper presents a hybridization of particle swarm optimization (PSO) and artificial bee colony (ABC) approaches, based on recombination procedure. The PSO and ABC are population-based iterative methods. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents (employed, onlooker and scout bees) of the ABC do not directly use this information but the global best solution in the ABC is stored at the each iteration. The global best solutions obtained by the PSO and ABC are used for recombination, and the solution obtained from this recombination is given to the populations of the PSO and ABC as the global best and neighbor food source for onlooker bees, respectively. Information flow between particle swarm and bee colony helps increase global and local search abilities of the hybrid approach which is referred to as Hybrid approach based on Particle swarm optimization and Artificial bee colony algorithm, HPA for short. In order to test the performance of the HPA algorithm, this study utilizes twelve basic numerical benchmark functions in addition to CEC2005 composite functions and an energy demand estimation problem. The experimental results obtained by the HPA are compared with those of the PSO and ABC. The performance of the HPA is also compared with that of other hybrid methods based on the PSO and ABC. The experimental results show that the HPA algorithm is an alternative and competitive optimizer for continuous optimization problems.

Keywords

  • Artificial bee colony;
  • Particle swarm optimization;
  • Recombination procedure;
  • Hybridization;
  • Continuous optimization;

A hierarchic approach based on swarm intelligence to solve the traveling salesman problem

Papers in International Journals
Mustafa Servet Kıran, Mesut Gündüz, Eren Özceylan
Turkish Journal of Electrical Engineering & Computer Sciences, Volume 23, January 2015, Pages 103-117

Abstract

The purpose of this paper is to present a new hierarchic method based on swarm intelligence algorithms for solving the well-known traveling salesman problem. The swarm intelligence algorithms implemented in this study are divided into 2 types: path construction-based and path improvement-based methods. The path construction-based method (ant colony optimization (ACO)) produces good solutions but takes more time to achieve a good solution, while the path improvement-based technique (artificial bee colony (ABC)) quickly produces results but does not achieve a good solution in a reasonable time. Therefore, a new hierarchic method, which consists of both ACO and ABC, is proposed to achieve a good solution in a reasonable time. ACO is used to provide a better initial solution for the ABC, which uses the path improvement technique in order to achieve an optimal or near optimal solution. Computational experiments are conducted on 10 instances of well-known data sets available in the literature. The results show that ACO-ABC produces better quality solutions than individual approaches of ACO and ABC with better central processing unit time.

Keywords

  • Ant colony optimization;
  • Artificial bee colony;
  • Path construction;
  • Path improvement;
  • Hierarchic approach;
  • Traveling salesman problem

A directed artificial bee colony algorithm

Papers in International Journals
Mustafa Servet Kıran, Oğuz Fındık
Applied Soft Computing Volume 26, January 2015, Pages 454–462

Abstract

Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees. This updating has caused the slow convergence to global or near global optimum for the algorithm. In order to accelerate convergence of the method, using a control parameter (modification rate-MR) has been proposed for ABC but this approach is based on updating more design parameters than one. In this study, we added directional information to ABC algorithms, instead of updating more design parameters than one. The performance of proposed approach was examined on well-known nine numerical benchmark functions and obtained results are compared with basic ABC and ABCs with MR. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality, robustness and convergence to global optimum.

Keywords

  • Swarm intelligence;
  • Artificial bee colony;
  • Direction information;
  • Numerical optimization

Artificial bee colony algorithm with variable search strategy for continuous optimization

Papers in International Journals
Mustafa Servet Kıran, Hüseyin Haklı, Mesut Gündüz, Harun Uğuz
Information Sciences Volume 300, 10 April 2015, Pages 140–157

Abstract

The artificial bee colony (ABC) algorithm is a swarm-based optimization technique proposed for solving continuous optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments.

Keywords

  • Artificial bee colony;
  • Continuous optimization;
  • Search strategy;
  • Integration

The continuous artificial bee colony algorithm for binary optimization

Papers in International Journals
Mustafa Servet Kıran
Applied Soft Computing, Volume 33, August 2015, Pages 15–23

Abstract

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABCbin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABCbin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABCbin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABCbin is an alternative and simple binary optimization tool in terms of solution quality and robustness.

Keywords

  • Artificial bee colony;
  • Binary optimization;
  • Conversion of continuous values;
  • Uncapacitated facility location problem

TSA: Tree-seed algorithm for continuous optimization

Papers in International Journals
Mustafa Servet Kıran
Expert Systems with Applications, Volume 42, Issue 19, 1 November 2015, Pages 6686–6698

Abstract

This paper presents a new intelligent optimizer based on the relation between trees and their seeds for continuous optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n-dimensional search space corresponds with the possible solution of an optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function optimization and is an alternative optimization method for solving multilevel thresholding problem.

Keywords

  • Heuristic search;
  • Tree and seed;
  • Numeric optimization;
  • Multilevel thresholding

Diagnosis of Coronary Artery Disease Using Artificial Bee Colony and K-Nearest Neighbor Algorithms

Papers in International Journals
İsmail Babaoğlu, Mustafa Servet Kıran, Erkan Ülker, Mesut Gündüz
International Journal of Computer and Communication Engineering, Volume 2, Issue 1, January 2013, Pages 56-59

Abstract

Artificial bee colony (ABC) is one of the swarm intelligence optimization algorithms, inspired by foraging and dance behaviors of real honey bee colonies. This study is an instance of a hybrid algorithm using ABC together with k-nearest neighbor algorithm on diagnosis of coronary artery disease employing exercises stress test data. The study dataset is composed of 134 healthy and 346 unhealthy totally 480 patients. On the proposed algorithm two centroid vectors are obtained concerning one for healthy patients and the other for unhealthy patients utilizing ABC for the training part of the dataset. Then, the test part of the dataset is classified using k-nearest neighbor algorithm. The results obtained by the proposed technique show that this hybrid algorithm could be used as an alternative classifier on diagnosis of coronary artery disease employing exercise stress test data.

Keywords

  • Coronary artery disease;
  • exercise stress testing;
  • artificial bee colony

The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems

Papers in International Journals
Mustafa Servet Kıran, Mesut Gündüz
Journal of Computers and Communication, Volume 2, Issue 4, March 2014, Pages 27-136

Abstract

Artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention in 2005, many ABC models have been proposed in order to solve different optimization problems. In all the models proposed, there are only one scout bee and a constant limit value used as control parameters for the bee population. In this study, the performance of ABC algorithm on the numeric optimization problems was analyzed by using different number of scout bees and limit values. Experimental results show that the results obtained by using more than one scout bee and different limit values, are better than the results of basic ABC. Therefore, the control parameters of the basic ABC should be tuned according to given class of optimization problems. In this paper, we propose reasonable value ranges of control parameters for the basic ABC in order to obtain better results on the numeric optimization problems.

Keywords

  • Artificial Bee Colony;
  • Effects of the Parameters;
  • Parameter Tuning;
  • Number of Scout Bee;
  • Limit Value;

Investigation Effects of Selection Mechanisms for Gravitational Search Algorithm

Papers in International Journals
Oğuz Fındık, Mustafa Servet Kıran, İsmail Babaoğlu
Journal of Computers and Communication, Volume 2, Issue 4, March 2014, Pages 117-126

Abstract

The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality. 

Keywords

  • Gravitational Search Algorithm;
  • Roulette-Wheel Selection;
  • Tournament Selection;
  • Rank-Based Selection;
  • Random Selection;
  • Continuous Optimization

Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems

Papers in International Journals
Mustafa Servet Kıran, Ahmet Babalık
Journal of Computers and Communication, Volume 2, Issue 4, March 2014, Pages 108-116

Abstract

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certain time, it becomes a scout bee. This alteration is done in the scout bee phase. The onlooker bee phase is placed where information sharing is done. Although a candidate solution improved by onlookers is chosen among the employed bee population according to fitness values of the employed bees, neighbor of candidate solution is randomly selected. In this paper, we propose a selection mechanism for neighborhood of the candidate solutions in the onlooker bee phase. The proposed selection mechanism was based on information shared by the employed bees. Average fitness value obtained by the employed bees is calculated and those better than the aver- age fitness value are written to memory board. Therefore, the onlooker bees select a neighbor from the memory board. In this paper, the proposed ABC-based method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand problem. Obtained results for the problems show that iABC is better than the basic ABC in terms of solution quality.

Keywords

  • Artificial Bee Colony;
  • Selection Mechanism;
  • Memory Board;
  • Numerical Optimization;
  • Energy Estimation

Artificial Bee Colony Approach to Estimate CO2 Emission of Turkey

Conference paper

Kıran M.S.Turanoğlu E., Ozceylan E., 2011, Artificial Bee Colony Approach to Estimate CO2 Emission of Turkey, Proceedings of the 41stInternational Conference on Computers & Industrial Engineering, pp.536-541, October 23-26, Los Angeles-USA.

Arı Kolonisi OptimizasyonAlgoritması Kullanarak Şofor-Hat-Zaman Cizelgeleme

Papers in National Journals

Kıran M.S., Gunduz M., 2012, Arı Kolonisi OptimizasyonAlgoritması Kullanarak Şofor-Hat-Zaman Cizelgeleme, Selcuk Teknik-Online Dergi,vol.11(2-2012), pp. 71-81.

A New Hybrid Heuristic Approachfor Solving Green Traveling Salesman Problem

Conference paper

Ozceylan E., Kıran M.S., Atasagun Y., 2011, A New Hybrid Heuristic Approach for Solving Green Traveling Salesman Problem, Proceedings of the 41st International Conference on Computers & Industrial Engineering, pp.720-725, October 23-26, Los Angeles-USA.

Particle Swarm Optimization and Artificial Bee Colony Approaches to Optimize of Single Input-Output Fuzzy Membership Functions

Conference paper

Turanoğlu E., Ozceylan E., Kıran M.S., 2011, Particle Swarm Optimization and Artificial Bee Colony Approaches to Optimize of Single Input-Output Fuzzy Membership Functions, Proceedings of the 41st International Conference on Computers & Industrial Engineering, pp. 542-547, October 23-26, LosAngeles, USA.

Supply Chain Optimizationusing Ant System

Conference paper

Iscan H., Kıran M.S.Gunduz M., 2011, Supply Chain Optimizationusing Ant System, The International Conference on Computing and InformationTechnology, pp.1-5 , 11-12 May, Bangkok, Thailand.

Artificial Bee Colony Algorithm for Solving Uncapacitated Facility Location Problems

Conference paper

Kıran M.S., Ozceylan E., Paksoy T., 2012, Artificial Bee Colony Algorithm forSolving Uncapacitated Facility Location Problems, 25th European Conference onOperational Research, pp. 165 (abstract), July 8-11, Vilnius, Lithuania.

Diagnosis of Coronary Artery Disease using Artificial Bee Colony and K-nearest Neighbor Algorithms

Conference paper

Babaoglu İ., Kıran M.S., Ulker E., Gunduz M., 2012, Diagnosis ofCoronary Artery Disease using Artificial Bee Colony and K-nearest Neighbor Algorithms, Presented in International Conference on Software and Computer Engineering, October 5-7, Singapore, “Published in InternationalJournal of Computer and Communication Engineering, vol. 2(1), pp.56-59,2013”.

A Hybridization of Artificial Bee Colony and Gravitational Search Algorithms for Nonlinear Global Optimization

Conference paper

Kıran M.S., Gunduz M., Haklı H. ve Şahman M.A. (2013) “A Hybridization of Artificial Bee Colony and Gravitational Search Algorithms for Nonlinear Global Optimization” , 26th European Conference On Operational Research, p. 222, July 1-4, Rome, Italy.

An Application Showing The Impact of the Travelling Sales Man Problem Based on Logistic Costs Through Heuristic Hybrid Methods

Conference paper

Dundar A.O., Şahman M.A., Tekin M., Kıran M.S.,2013, An Application Showing The Impact of the Travelling Sales Man Problem Based on Logistic Costs Through Heuristic Hybrid Methods, 26th European Conference On Operational Research, p.104 (abstract), July 1-4, Rome, Italy.

Comparison of Linear Programming and Heuristic Hybrid Methods in Preparing Flour Blend

Conference paper

Şahman M.A., Dundar A.O.,  Kıran M.S., Altun A.A., 2013, Comparison of Linear Programming and Heuristic Hybrid Methods in Preparing Flour Blend, 26th European Conference On Operational Research, p. 229 (abstract), July 1-4, Rome,Italy.

The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems

Conference paper

Kıran M.S., Gunduz M., 2014, The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems, Presented in “The 2nd Conference on Artificial Intelligence and Data Mining, 10-12 March, Suzhou, China”, Published in “Journal of Computers and Communication, 2/4, 127-136”.

Investigation Effects of Selection Mechanisms for Gravitational Search Algorithm

Conference paper

Fındık O., Kıran M.S., Babaoğlu İ., 2014,  Investigation Effects of Selection Mechanisms for Gravitational Search Algorithm, Presented in “The 2nd Conference on Artificial Intelligence and Data Mining, 10-12March, Suzhou, China”, Published in “Journal of Computers and Communication,2/4, 117-126”.

Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems

Conference paper

Kıran M.S., Babalık A., 2014, Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems, Presented in “The 2nd Conference on Artificial Intelligence and Data Mining, 10-12 March, Suzhou,China”, Published in “Journal of Computers and Communication, 2/4,108-116”.

An Implementation of Tree-Seed Algorithm (TSA) for Constrained Optimization

Conference paper

Kıran M.S., 2015, An Implementation of Tree-Seed Algorithm (TSA) for Constrained Optimization, 19th Asia Pacific Symposium on Intelligent and Evolutionary Systems, November 2015, Bangkok, Thailand, pp. 189-197, Proceeding in Adaptation, Learning and Optimization 5, Springer.

Search Experience-based adaptation in Artificial Bee Colony Algorithm

Conference paper

Li X., Yang G., Kıran M.S., 2016, Search Experience-based adaptation in Artificial Bee Colony Algorithm, IEEE Congress on Evolutionary Computation (CEC’2016), July 2016, Vancouver Canada, pp. 2524-2531.

A parallel version of Tree-Seed Algorithm within CUDA platform

Conference paper

Çınar A.C., Kıran M.S., A parallel version of Tree-Seed Algorithm within CUDA platform, International Scientific Conference on Applied Sciences, September 2016, Antalya, Turkey, pp.174-178.

Tree-Seed Algorithm for Binary Optimization

Conference paper

Kıran M.S., Tree-Seed Algorithm for Binary Optimization, International Scientific Conference on Applied Sciences, Poster, September 2016, Antalya, Turkey

Withering Process for Tree-Seed Algorithm

Conference paper

Kıran M.S., Withering Process for Tree-Seed Algorithm, 8th International Conference on Advances in Information Technology, Macau, China, 2016

The Performance Analysis of Extreme Learning Machines on Odour Recognition

Conference paper
Engin Eşme, Mustafa Servet iran

Esme, Engin, and Mustafa Servet Kiran. “The Performance Analysis of Extreme Learning Machines on Odour Recognition.” Proceedings of the 2018 2nd International Conference on Cloud and Big Data Computing. ACM, 2018.

An artificial algae algorithm for solving binary optimization problems

Papers in International Journals
Sedat Korkmaz, Ahmet Babalık, Mustafa Servet Kıran
International Journal of Machine Learning and Cybernetics July 2018, Volume 9, Issue 7, pp 1233–1247

Abstract

This paper focuses on modification of basic artificial algae algorithm (AAA) for solving binary optimization problems by using a new solution update rule because the agents in AAA work on continuous solution space. The candidate solution generation process of algorithm in the basic version of AAA is replaced with a mechanism that use a neighbor solution randomly selected from the population and three decision variables of this solution. The current solution is taken from the population and randomly selected three dimensions of this solution are changed using the neighbor solution. The agents of AAA work on continuous solution space and this modification for AAA is required for solving a binary optimization problem because a binary optimization problems have decision variables which are element of set {0, 1}. The performance of the proposed algorithm, binAAA for short, is investigated on the uncapacitated facility location problems which are pure binary optimization problem and there is no integer or real valued decision variables in this problem. The results obtained by binAAA are compared with the results of state-of-art algorithms such as artificial bee colony, particle swarm optimization, and genetic algorithms. Experimental results and comparisons show that the binAAA is better than the other algorithm almost all cases in terms of solution quality and robustness based on the mean and standard deviations, respectively.

A multi-objective artificial algae algorithm

Papers in International Journals
Ahmet Özkış, Ahmet Babalık, Sait Ali Uymaz, Mustafa Servet Kıran
Applied Soft Computing Volume 68, July 2018, Pages 377-395

Abstract

In this study, the authors focus on modification of the artificial algae algorithm (AAA), for multi-objective optimization. Basically, AAA is a population-based optimization algorithm inspired by the behavior of microalgae cells. In this work, a modified AAA with appropriate strategies is proposed for multi-objective Artificial Algae Algorithm (MOAAA) from the first AAA that was initially presented to solve single-objective continuous optimization problems. To the best of our knowledge, the MOAAA is the first modification of the AAA for solving multi-objective problems. Performance of the proposed algorithm is examined on a benchmark set consisting of 36 different multi-objective optimization problems and compared with four different swarm intelligence or evolutionary algorithms that are well-known in literature. The MOAAA is highly successful in solving multi-objective problems, and it has been demonstrated that the MOAAA is an alternative competitive algorithm in multi-objective optimization according to experimental results and comparisons presented in this research topic.

An artificial algae algorithm with stigmergic behavior for binary optimization

Papers in International Journals
Sedat Korkmaz, Mustafa Servet Kıran
Applied Soft Computing Volume 64, March 2018, Pages 627-640

Abstract

In this study, we focus on modification of the artificial algae algorithm (AAA), proposed for solving continuous optimization problems, for binary optimization problems by using exclusive-or (xor) logic operator and stigmergic behavior. In the algorithm, there are four processes sequentially realized for solving continuous problems. In the binary version of the algorithm, three of them are adapted in order to overcome the structure of binary optimization problems. In the initialization, the colonies of AAA are set to either zero or one with equal probability. Secondly, helical movement phase is used for obtaining candidate solutions and in this phase, the xor operator and stigmergic behavior are utilized for obtaining binary candidate solutions. The last modified phase is adaptation, and randomly selected binary values in the most starved solution are likened to biggest colony obtained so far. The proposed algorithm is applied to solve well-known uncapacitated facility location problems and numeric benchmark problems. Obtained results are compared with state-of-art algorithms in swarm intelligence and evolutionary computation field. Experimental results show that the proposed algorithm is superior to other techniques in terms of solution quality, convergence characteristics and robustness.

A modification of tree-seed algorithm using Deb’s rules for constrained optimization

Papers in International Journals
Ahmet Babalık, Ahmet Cevahir Çınar, Mustafa Servet Kıran
Applied Soft Computing Volume 63, February 2018, Pages 289-305

Abstract

This study focuses on the modification of Tree-Seed Algorithm (TSA) to solve constrained optimization problem. TSA, which is one of the population-based iterative search algorithms, has been developed by inspiration of the relations between trees and seeds grown on a land, and the basic version of TSA has been first used to solve unconstrained optimization problems. In this study, the basic algorithmic process of TSA is modified by using Deb’s rules to solve constrained optimization problems. Deb’s rules are based on the objective function and violation of constraints and it is used to select the trees and seeds that will survive in next iterations. The performance of the algorithm is analyzed under different conditions of control parameters of the proposed algorithm, CTSA for short, and well-known 13 constrained maximization or minimization standard benchmark functions and engineering design optimization problems are employed. The results obtained by the CTSA are compared with the results of particle swarm optimization (PSO), artificial bee colony algorithm (ABC), genetic algorithm (GA) and differential evolution (DE) algorithm on the standard benchmark problems. The results of state-of-art methods are also compared with the proposed algorithm on engineering design optimization problems. The experimental analysis and results show that the proposed method produces promising and comparable results for the constrained optimization benchmark set in terms of solution quality and robustness.

Tree-Seed algorithm for large-scale binary optimization

Conference paper

Cinar, Ahmet Cevahir, Hazim Iscan, and Mustafa Servet Kiran. “Tree-Seed algorithm for large-scale binary optimization.” KnE Social Sciences 3.1 (2018): 48-64.

A parallel implementation of Tree-Seed Algorithm on CUDA-supported graphical processing unit

Papers in International Journals
Ahmet Cevahir Çınar, Mustafa Servet Kıran
Journal of the Faculty of Engineering and Architecture of Gazi University 33:4 (2018) 1397-1409

Abstract

In recent years, while the collected data are increased, we need effective computation methods to process these data. Due to the fact that most of the real world problems are difficult to solve, swarm intelligence and evolutionary computation algorithms are interested because they guarantee the near optimal solution for the problem in a reasonable time but not guarantee the optimal solution. In another perspective, if the data or process can be parallelized, the parallel computation is a good choice instead of serial programming approaches in terms of time effectiveness. In this study, the tree-seed algorithm, which is a recently proposed population-based iterative search algorithm, is implemented within CUDA platform in parallel. The performance of the parallel version of the algorithm has been investigated on the benchmark functions and compared with the performance of the serial version of the algorithm. The dimensionality of the problems is taken as 10 and the performance analysis and comparisons have been conducted under the condition of different sizes of the population. Experimental studies show that the parallel version of the algorithm is accelerated to 184.65 times in accordance with the serial version of the algorithm on some problems.

Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images

Papers in International Journals
Pradeep K. GuptaShyam LalEmail authorMustafa Servet Kiran, Farooq Husain
Journal of Ambient Intelligence and Humanized Computing pp 1–22

Abstract

A clinical ultrasound imaging plays a significant role in the proper diagnosis of patients because, it is a cost-effective and non-invasive technique in comparison with other methods. The speckle noise contamination caused by ultrasound images during the acquisition process degrades its visual quality, which makes the diagnosis task difficult for physicians. Hence, to improve their visual quality, despeckling filters are commonly used for processing of such images. However, several disadvantages of existing despeckling filters discourage the use of existing despeckling filters to reduce the effect of speckle noise. In this paper, two dimensional cuckoo search optimization algorithm based despeckling filter is proposed for avoiding limitations of various existing despeckling filters. Proposed despeckling filter is developed by combining fast non-local means filter and 2D finite impulse response (FIR) filter with cuckoo search optimization algorithm. In the proposed despeckling filter, the coefficients of 2D FIR filter are optimized by using the cuckoo search optimization algorithm. The quantitative results comparison between the proposed despeckling filter and other existing despeckling filters are analyzed by evaluating PSNR, MSE, MAE, and SSIM values for different real ultrasound images. Results reveal that the visual quality obtained by the proposed despeckling filter is better than other existing despeckling filters. The numerical results also reveal that the proposed despeckling filter is highly effective for despeckling the clinical ultrasound images.

Similarity and Logic Gate-Based Tree-Seed Algorithms for Binary Optimization

Papers in International Journals
Ahmet Cevahir Çınar
Computers & Industrial Engineering Volume 115, January 2018, Pages 631-646

Abstract

This paper focuses on solving binary optimization problems by using Tree-Seed Algorithm, TSA for short. While TSA is firstly proposed for solving optimization problems with continuously-structured solution space, TSA is modified to solve binary optimization problems, which is a subfield of discrete optimization, by using logic gates (LogicTSA) and similarity measurement techniques (SimTSA). In order to improve performance of these methods, a hybrid variant (SimLogicTSA) is also proposed. The performance of the proposed algorithms is investigated on uncapacitated facility location problems (UFLPs), which are pure binary optimization problems. The experimental results on 15 test instances are compared with each other and state-of-art algorithms. The comparisons demonstrate that hybrid variant of the algorithm is better than the other variants of the algorithm and state-of-art algorithms in terms of solution quality and robustness.

An improved binary artificial bee colony algorithm

Conference paper

Kaya, Ersin, and Mustafa Servet Kiran. “An improved binary artificial bee colony algorithm” ICT and Knowledge Engineering (ICT&KE), 2017 15th International Conference on. IEEE, 2017.

Particle swarm optimization with a new update mechanism

Papers in International Journals
Mustafa Servet Kıran
Applied Soft Computing Volume 60, November 2017, Pages 670-678

Abstract

Particle swarm optimization (PSO) has been invented by inspiring social behaviors of fish or birds to solve nonlinear global optimization problems. Since its invention, many PSO variants have been proposed by modifying its solution update rule to improve its performance. The social component of update rule of PSO is based on subtraction between current position of particle and global best information. Similarly, the cognitive component works by using subtraction between the current position of particle and personal best information. The subtraction-based solution update mechanism has caused premature convergence and stagnation in particle population during the iterations. To overcome these issues, this study presents a distribution-based update rule for PSO algorithm. The performance of proposed approach named as PSOd is investigated on solving 13 nonlinear global optimization benchmark functions and three constrained engineering optimization problems. Obtained results are compared with standard PSO algorithm, its classical variants and some state-of-art swarm intelligence algorithms. The experimental results and comparisons show that PSOd outperforms PSO and its variants on solving the numerical benchmark functions in terms of solution quality and robustness.

Boundary conditions in Tree-Seed Algorithm: Analysis of the success of search space limitation techniques in Tree-Seed Algorithm

Conference paper

Çınar, Ahmet Cevahir, and Mustafa Servet Kıran. “Boundary conditions in Tree-Seed Algorithm: Analysis of the success of search space limitation techniques in Tree-Seed Algorithm.” Computer Science and Engineering (UBMK), 2017 International Conference on. IEEE, 2017.

Crude oil price forecasting using XGBoost

Conference paper

Gumus, Mesut, and Mustafa S. Kiran. “Crude oil price forecasting using XGBoost.” Computer Science and Engineering (UBMK), 2017 International Conference on. IEEE, 2017.

A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images

Papers in International Journals
Shilpa Suresh, Shyam Lal, Chintala Sudhakar Reddy, Mustafa Servet Kıran
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume: 10 , Issue: 8 , Aug. 2017

Abstract

Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive Lévy flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images.

A stigmergic binary differential evolution algorithm

Conference paper

Kıran Mustafa Servet, 2018,  A stigmergic binary differential evolution algorithm.  4th International Conference on Engineering Science and Technology (ICEST 2018).

The Binary Salp Swarm Algorithm with Using Transfer Function

Conference paper

Kaya Ersin, Çınar Ahmet Cevahir, Uymaz Oğuzhan, Korkmaz Sedat, Kıran Mustafa Servet, 2018, The Binary Salp Swarm Algorithm with Using Transfer Function.  International Conference on Advanced Technologies, Computer Engineering and Science

MOTSA: A Multi-Objective Tree-Seed Algorithm

Conference paper

Özcan Gül,Özkış Ahmet,Kıran Mustafa Servet, 2018, MOTSA: A Multi-Objective Tree-Seed Algorithm.  International Conference on Advanced Technologies, Computer Engineering and Science

Solving of constrained problems via multi-objective vortex search algorithm

Conference paper

Özkış Ahmet,Özcan Gül,Babalık Ahmet,Kıran Mustafa Servet, 2018, Solving of constrained problems via multi-objective vortex search algorithm.  International Conference on Advanced Technologies, Computer Engineering and Science

Performance analysis of Galactic Swarm Optimization with Tree Seed Algorithm

Conference paper

Kaya Ersin,Uymaz Oğuzhan,Korkmaz Sedat,Sıramkaya Eyüp,Kıran Mustafa Servet, 2018), Performance analysis of Galactic Swarm Optimization with Tree Seed Algorithm.  International Conference on Advanced Technologies, Computer Engineering and Science

An analysis of solution update equations for artificial bee colony algorithm with variable search strategy

Conference paper

Haklı Hüseyin,Korkmaz Sedat,Kıran Mustafa Servet, 2018, An analysis of solution update equations for artificial bee colony algorithm with variable search strategy.  7th International Conference on Advanced Technologies (ICAT’18), 46

An improved version of tree seed algorithm for continuous optimization

Conference paper

Haklı Hüseyin,Korkmaz Sedat,Kıran Mustafa Servet, 2018, An improved version of tree seed algorithm for continuous optimization. 7th International Conference on Advanced  Technologies (ICAT’18), 47

A Discrete Variant of Tree-Seed Algorithm

Conference paper

Korkmaz Sedat,Çınar Ahmet Cevahir,Seyfi Gökhan,Kıran Mustafa Servet, 2017, A Discrete Variant of Tree-Seed Algorithm.  International Conference on Engineering Technologies-ICENTE

An analysis of the performance of metaheuristic algorithms on exam scheduling problem

Conference paper

Seyfi Gökhan,Korkmaz Sedat,Kıran Mustafa Servet, 2017, An analysis of the performance of metaheuristic algorithms on exam scheduling problem.  International Conference on Engineering Technologies-ICENTE

Tree-Seed Algorithm for Identifying Communities in Various Real-World Networks

Conference paper

Kıran Mustafa Servet,Atay Yılmaz,Kodaz Halife, 2017, Tree-Seed Algorithm for Identifying Communities in Various Real-World Networks.  International Advanced Researches  Engineering Congress-2017