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Öğe The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem(SPRINGER LONDON LTD, 2013) Kiran, Mustafa Servet; Iscan, Hazim; Gunduz, MesutThe artificial bee colony (ABC) algorithm, inspired intelligent behaviors of real honey bee colonies, was introduced by Karaboga 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.Öğe An artificial algae algorithm for solving binary optimization problems(SPRINGER HEIDELBERG, 2018) Korkmaz, Sedat; Babalik, Ahmet; Kiran, Mustafa ServetThis 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.Öğe An artificial algae algorithm with stigmergic behavior for binary optimization(ELSEVIER, 2018) Korkmaz, Sedat; Kiran, Mustafa ServetIn 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. (C) 2018 Elsevier B.V. All rights reserved.Öğe Artificial bee colony algorithm with variable search strategy for continuous optimization(ELSEVIER SCIENCE INC, 2015) Kiran, Mustafa Servet; Hakli, Huseyin; Gunduz, Mesut; Uguz, HarunThe 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. (C) 2015 Elsevier Inc. All rights reserved.Öğe Boundary Conditions in Tree-Seed Algorithm Analysis of the success of search space limitation techniques in Tree-Seed Algorithm(IEEE, 2017) Cinar, Ahmet Cevahir; Kiran, Mustafa ServetSwarm intelligence or evolutionary computation algorithms search possible solutions in a predetermined search space of an optimization problem. In some cases candidate solutions go out of search space during the search. In such situations, search space limitation techniques or boundary conditions are used for sway up this outcast individual into search space. The boundary conditions are classified two main categories whose names are restricted and unrestricted techniques. Restricted boundary conditions forces outcast individual into search space but unrestricted boundary conditions does not force. In this work we use four restricted boundary conditions whose names are Absorbing, Reflecting, Damping and Randomly. Additionally, three unrestricted boundary conditions (Invisible, Invisible Reflecting and Invisible Damping) are used in the study. These boundary conditions are applied in Tree-Seed Algorithm (TSA). The test material is five standard benchmark functions and these arc Sphere, Rastrigin, Rosenbrock, Griewank and Ackley. The main idea of this study is to investigate whether there is a significant difference among the limitation methods in TSA. Experimental results show that there is no significant difference among the boundary conditions methods for TSA.Öğe The continuous artificial bee colony algorithm for binary optimization(ELSEVIER, 2015) Kiran, Mustafa ServetArtificial 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, ABC(bin) 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 ABC(bin), 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 ABC(bin) is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABC(bin) is an alternative and simple binary optimization tool in terms of solution quality and robustness. (C) 2015 Elsevier B.V. All rights reserved.Öğe A directed artificial bee colony algorithm(ELSEVIER, 2015) Kiran, Mustafa Servet; Findik, OguzArtificial 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. (C) 2014 Elsevier B.V. All rights reserved.Öğe A hierarchic approach based on swarm intelligence to solve the traveling salesman problem(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2015) Gunduz, Mesut; Kiran, Mustafa Servet; Ozceylan, ErenThe 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.Öğe An Implementation of Tree-Seed Algorithm (TSA) for Constrained Optimization(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Kiran, Mustafa ServetOne of the recent proposed population-based heuristic search algorithms is tree-seed optimization algorithm, TSA for short. TSA simulates the growing over on a land of trees and seeds and it has been proposed for solving unconstrained continuous optimization problems. The trees and their seeds on the D-dimensional solution space correspond to the possible solution for the optimization problem. At the beginning of the search, the trees are sowed to the land, and a number of seeds for each tree are produced during the iterations. The tree is removed from the stand and its best seed is added to the stand if the fitness of the best seed is better than the fitness of this tree. In the present study, a constraint optimization problem, the well-known pressure vessel design-PVD problem, is solved by using TSA. To overcome the constraints of the problem, a penalty function is used and the problem is considered as a single objective optimization problem. The experimental results obtained by the TSA are compared with the results of state-of-art methods such as artificial bee colony (ABC) and particle swarm optimization (PSO). Based on the solution quality and robustness, the promising and comparable results are obtained by the proposed approach.Öğe An Improved Binary Artificial Bee Colony Algorithm(IEEE, 2017) Kaya, Ersin; Kiran, Mustafa ServetThe xor-based artificial bee colony algorithm, called as binABC, is a novel variant of basic artificial bee colony (ABC) algorithm, which is proposed for solving binary optimization problems. This algorithm uses xor logic operator to search solution space instead of subtraction-based solution update rule of basic ABC due to discrete nature of the binary optimization. Similar to basic version of the algorithm, only one decision variable (dimension) is updated by the artificial agents of binABC. This approach causes slow convergence in the algorithm, and a proportional changing, which is depended on the number of decision variable of the optimization problem, is proposed in this study. The proposed approach is applied to solve a well-known binary optimization problem whose name is uncapacitated facility location problem (UFLP). Twelve instances of this problem are used in the experiments and obtained results are compared with the binABC algorithm in terms of solution quality, robustness and convergence characteristics. Experimental results show that the proposed approach is useful for controlling convergence characteristics and obtaining better quality of solution.Öğe A modification of tree-seed algorithm using Deb's rules for constrained optimization(ELSEVIER SCIENCE BV, 2018) Babalik, Ahmet; Cinar, Ahmet Cevahir; Kiran, Mustafa ServetThis 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. (C) 2017 Elsevier B.V. All rights reserved.Öğe A multi-objective artificial algae algorithm(ELSEVIER, 2018) Babalik, Ahmet; Ozkis, Ahmet; Uymaz, Sait Ali; Kiran, Mustafa ServetIn 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. (C) 2018 Elsevier B.V. All rights reserved.Öğe A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017) Suresh, Shilpa; Lal, Shyam; Reddy, Chintala Sudhakar; Kiran, Mustafa ServetOwing 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 Levy 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.Öğe A NOVEL ARTIFICIAL BEE COLONY-BASED ALGORITHM FOR SOLVING THE NUMERICAL OPTIMIZATION PROBLEMS(ICIC INTERNATIONAL, 2012) Kiran, Mustafa Servet; Gunduz, MesutArtificial 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.Öğe A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019) Iscan, Hazim; Kiran, Mustafa Servet; Gunduz, MesutFruit fly optimization algorithm (FOA) is one of the swarm intelligence algorithms proposed for solving continuous optimization problems. In the basic FOA, the best solution is always taken into consideration by the other artificial fruit flies when solving the problem. This behavior of FOA causes getting trap into local minima because the whole population become very similar to each other and the best solution in the population during the search. Moreover, the basic FOA searches the positive side of solution space of the optimization problem. In order to overcome these issues, this study presents two novel versions of FOA, pFOA_v1 and pFOA_v2 for short, that take into account not only the best solutions but also the worst solutions during the search. Therefore, the proposed approaches aim to improve the FOA's performance in solving continuous optimizations by removing these disadvantages. In order to investigate the performance of the novel proposed FOA versions, 21 well-known numeric benchmark functions are considered in the experiments. The obtained experimental results of pFOA versions have been compared with the basic FOA, SFOA which is an improved version of basic FOA, SPSO2011 which is one of the latest versions of particle swarm optimization, firefly algorithm called FA, tree seed algorithm TSA for short, cuckoo search algorithm briefly CS, and a new optimization algorithm JAYA. The experimental results and comparisons show that the proposed versions of FOA are better than the basic FOA and SFOA, and produce comparable and competitive results for the continuous optimization problems.Öğe A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum(ELSEVIER SCIENCE INC, 2012) Kiran, Mustafa Servet; Gunduz, Mesut; Baykan, Omer KaanThis 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. (C) 2012 Elsevier Inc. All rights reserved.Öğe A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey(PERGAMON-ELSEVIER SCIENCE LTD, 2012) Kiran, Mustafa Servet; Ozceylan, Eren; Gunduz, Mesut; Paksoy, TuranThis 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. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Particle swarm optimization with a new update mechanism(ELSEVIER SCIENCE BV, 2017) Kiran, Mustafa ServetParticle 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. (C) 2017 Elsevier B.V. All rights reserved.Öğe The Performance Analysis of Extreme Learning Machines on Odour Recognition(ASSOC COMPUTING MACHINERY, 2018) Esme, Engin; Kiran, Mustafa ServetExtreme Learning Machine (ELM) is a single hidden layer feed-forward neural network learning method, which has a high generalization performance as well as faster. In this paper, odour data is discriminated based on the sensor response curve by using ELM, and the main objective is to investigate the optimum number of nodes in the hidden layer of ELM for olfactory detection. The relationship between the number of nodes in the hidden layer and the number of attributes or classes of dataset is queried to achieve the goal. Three odour datasets taken from different sources in literature and two transfer functions for the ELM are used to verify the results of the study. The backpropagation (BP) algorithm is also used for training an artificial neural network for comparison purposes. The analysis is performed for the three datasets by using ELM and BP and obtained results present that the time consumption of ELM is too small to be compared with BP even though the number of nodes is high and better accuracy rates are obtained by ELM.Öğe A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems(ELSEVIER, 2013) Kiran, Mustafa Servet; Gunduz, MesutThis 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. (C) 2012 Elsevier B.V. All rights reserved.
