A new Multi Sine-Cosine algorithm for unconstrained optimization problems.

Muhammad Zubair Rehman,Nazri Mohd Nawi,Rozaida Ghazali,Abdullah Khan,Muhammad Aamir,Seyedali Mirjalili

doi:10.1371/journal.pone.0255269

Abstract

The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.

Highlights

In modern times, optimization has become pertinent to the development of reliable and robust solutions in the field of science and engineering
Experimental results reveal that the Multi Sine-Cosine Algorithm (MSCA) exhibits competitive performance as compared to the Sine-Cosine algorithm (SCA) and other eight meta-heuristic algorithms
During the update, MSCA checks for better search clusters that offer convergence to global minima effectively

Summary

Introduction

Optimization has become pertinent to the development of reliable and robust solutions in the field of science and engineering. Satisfied with NFL theory, Mirjalili et al proposed a Sine-Cosine algorithm (SCA) in 2015 that uses Sine and Cosine functions for improved metaheuristic search [22]. To address the issues faced by the Sine-Cosine algorithm (SCA), this paper proposes an improved Multi Sine Cosine Algorithm (MSCA) that will avoid local optima convergence and improves exploitation. This characteristic makes MSCA suitable for solving optimization problems with multiple local minimums because it maintains a balance between exploration and exploitation with clustered population of solutions.

Literature review

Results and discussions

Conclusions