Yazar "Iscan, Hazim" seçeneğine göre listele

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    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, Mesut
    The 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.
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    An application of fruit fly optimization algorithm for traveling salesman problem
    (ELSEVIER SCIENCE BV, 2017) Iscan, Hazim; Gunduz, Mesut
    In 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.
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    An improvement in fruit fly optimization algorithm by using sign parameters
    (SPRINGER, 2018) Babalik, Ahmet; Iscan, Hazim; Babaoglu, Ismail; Gunduz, Mesut
    The 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.
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    A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019) Iscan, Hazim; Kiran, Mustafa Servet; Gunduz, Mesut
    Fruit 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.
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    A Survey on Fruit Fly Optimization Algorithm
    (IEEE, 2015) Iscan, Hazim; Gunduz, Mesut
    In 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.