Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/734
Title: A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer
Authors: İşcan, Hazım
Kıran, Mustafa Servet
Gündüz, Mesut
Keywords: Fruit Fly Algorithm
Best-Worst Strategy
Continuous Optimization
Numeric Benchmark Problem
Regression Neural-Network
Pid Controller
Algorithm
Model
Satisfaction
Perform
Colony
Foa
Issue Date: 2019
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract: 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.
URI: https://doi.org/10.1109/ACCESS.2019.2940104
https://hdl.handle.net/20.500.13091/734
ISSN: 2169-3536
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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