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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 application of fruit fly optimization algorithm for traveling salesman problem(ELSEVIER SCIENCE BV, 2017) Iscan, Hazim; Gunduz, MesutIn this study, an application of fruit fly optimization algorithm (FOA) is presented. FOA is one of the recently proposed swarm intelligence optimization algorithms used to solve continuous complex optimization problems. FOA has been invented by Pan in 2011 and it is based on the food search behavior of fruit flies. The FOA has a simple framework and it is easy to implement for solving optimization problem with different characteristics. The FOA is also a robust and fast algorithm and some researchers used FOA to solve discrete optimization problems. In this study, a new modified FOA is proposed for solving the well-known traveling salesman problem (TSP) which is one of the most studied discrete optimization problems. In basic FOA, there are two basic phases, one of them is osphresis phase and the other is vision phase. In the modified version of FOA the ospherisis phases kept as it is and for vision phase two different methods developed. In vision phase, the first half of the city arrangement matrix is updated according to first %30 part of best solutions of the ospheresis phase. The other half of the city arrangement matrix is randomly reproduced because of the possibility that initial solutions are far from the optimum. According to the results, travelling salesman problem can be solved with FOA as an alternative method. For big scale problems, it needs some improvements. (c) 2017 The Authors. Published by Elsevier B.V.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 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 improvement in fruit fly optimization algorithm by using sign parameters(SPRINGER, 2018) Babalik, Ahmet; Iscan, Hazim; Babaoglu, Ismail; Gunduz, MesutThe fruit fly optimization algorithm (FOA) has been developed by inspiring osphresis and vision behaviors of the fruit flies to solve continuous optimization problems. As many researchers know that FOA has some shortcomings, this study presents an improved version of FOA to remove with these shortcomings in order to improve its optimization performance. According to the basic version of FOA, the candidate solutions could not take values those are negative as well as stated in many studies in the literature. In this study, two sign parameters are added into the original FOA to consider not only the positive side of the search space, but also the whole. To experimentally validate the proposed approach, namely signed FOA, SFOA for short, 21 well-known benchmark problems are considered. In order to demonstrate the effectiveness and success of the proposed method, the results of the proposed approach are compared with the results of the original FOA, results of the two different state-of-art versions of particle swarm optimization algorithm, results of the cuckoo search optimization algorithm and results of the firefly optimization algorithm. By analyzing experimental results, it can be said that the proposed approach achieves more successful results on many benchmark problems than the compared methods, and SFOA is presented as more equal and fairer in terms of screening the solution space.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 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.Öğe A Survey on Fruit Fly Optimization Algorithm(IEEE, 2015) Iscan, Hazim; Gunduz, MesutIn this study one of the recent swarm optimization algorithms namely Fruit Fly Optimization Algorithm (FOA) and some of its variants are investigated. FOA was suggested by PAN in 2011. It is a fast, easy to code and easy to understand metaheuristic algorithm having an effective search capability. Despite the advantages of the algorithm, FOA has some deficiencies which were encountered by the researchers during the implementations as shown in literature. To overcome the deficiencies of the algorithm various enhancements were applied by the researchers. This study represents and explains the FOA at first. Then, the improvements which were made on FOA are illustrated, and their implementations are given by examples. The study is concluded by illustrating the evolution and the hybrid variants of FOA.Öğe Swarm intelligence approaches to estimate electricity energy demand in Turkey(ELSEVIER SCIENCE BV, 2012) Kiran, Mustafa Servet; Ozceylan, Eren; Gunduz, Mesut; Paksoy, TuranThis 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. (C) 2012 Elsevier B.V. All rights reserved.Öğe XOR-based artificial bee colony algorithm for binary optimization(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2013) Kiran, Mustafa Servet; Gunduz, MesutThe 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.
