A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer

Hazim Iscan,Mesut Gunduz,Mustafa Servet Kiran

doi:10.1109/access.2019.2940104

Hazim Iscan, Mesut Gunduz + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2940104

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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.

Highlights

In recent years, the behaviors of the biological creatures found in the nature are inspiration of the researchers for development novel optimization algorithms and many novel optimization algorithms have been developed by considering these behaviors
The proposed pFOA versions have been compared with the basic fly optimization algorithm (FOA), modified FOA called SFOA and other intelligent optimization algorithms including SPSO2011, firefly algorithm (FA), tree seed algorithm (TSA), CS and JAYA to validate the effectiveness of pFOA versions
The proposed algorithms are compared with the basic FOA and state-of-art swarm intelligence algorithms

Summary

Introduction

The behaviors of the biological creatures found in the nature are inspiration of the researchers for development novel optimization algorithms and many novel optimization algorithms have been developed by considering these behaviors. Metaheuristic algorithms are mostly based on swarm intelligence or evolutionary computation and they are used to solve optimization problems with an alternative of classical optimization techniques. Due the structure of the optimization problems, the optimization can be a time consuming and complicated process, and classical techniques are generally problem-dependent. Some of the swarm intelligence algorithms are particle swarm optimization (PSO) [1], firefly algorithm (FA) [2], cuckoo algorithm (CS) [3], ant colony algorithm (ACO) [4], artificial bee colony algorithm (ABC) [5], fruit fly optimization algorithm (FOA) [6], tree seed algorithm (TSA) [7]

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