Abstract

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally.

Highlights

  • Metaheuristic algorithms have been shown to be effective due to complex characteristics of difficult optimization problems such as dimensionality, multimodality, and non-differentiability [1]

  • The results are compared with the state-ofthe-art and recent nature-inspired algorithms, including krill herd (KH) [42], grey wolf optimizer (GWO) [43], moth-flame optimization (MFO) [30] algorithm, whale optimization algorithm (WOA) [7], salp swarm algorithm (SSA) [9], butterfly optimization algorithm (BOA) [44], henry gas solubility optimization (HGSO) [45], and Archimedes optimization algorithm (AOA) [46]

  • Matlab R2018a was used for implementing the multi-trial vector-based moth-flame optimization (MTV-MFO), and the experiments were run on a CPU, Intel Core(TM) i7-3770 3.4GHz and

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Summary

Introduction

Metaheuristic algorithms have been shown to be effective due to complex characteristics of difficult optimization problems such as dimensionality, multimodality, and non-differentiability [1]. Due to the growing complexity of optimization problems and compared to conventional optimization algorithms, metaheuristics have proven their ability to solve complex problems by providing feasible solutions in a reasonable time [2]. There is an increasing trend to propose new algorithms and enhance the performance of existing algorithms using improvement strategies [3]. Several new optimization algorithms have been proposed recently due to the No-free-lunch (NFL) theorem [4], which states that no particular optimization algorithm can solve all problems of all kinds of complexities. It is observed that depending on the set of parameter values, the same algorithm produces different solutions to the same problem

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