Enhanced Opposition Differential Evolution Algorithm for Multimodal Optimization

TL;DR

This paper introduces the Enhanced Opposition Differential Evolution (EODE) algorithm designed to efficiently find multiple optima in multimodal optimization problems, outperforming existing methods on benchmark functions.

Contribution

The paper proposes a novel EODE algorithm that improves the search efficiency and accuracy of locating multiple optima in multimodal functions.

Findings

EODE achieves competitive results on CEC 2013 benchmark functions.

The algorithm effectively locates multiple global and local optima.

EODE outperforms several existing state-of-the-art approaches.

Abstract

Most of the real-world problems are multimodal in nature that consists of multiple optimum values. Multimodal optimization is defined as the process of finding multiple global and local optima (as opposed to a single solution) of a function. It enables a user to switch between different solutions as per the need while still maintaining the optimal system performance. Classical gradient-based methods fail for optimization problems in which the objective functions are either discontinuous or non-differentiable. Evolutionary Algorithms (EAs) are able to find multiple solutions within a population in a single algorithmic run as compared to classical optimization techniques that need multiple restarts and multiple runs to find different solutions. Hence, several EAs have been proposed to solve such kinds of problems. However, Differential Evolution (DE) algorithm is a population-based heuristic method that can solve such optimization problems, and it is simple to implement. The potential challenge in Multi-Modal Optimization Problems (MMOPs) is to search the function space efficiently to locate most of the peaks accurately. The optimization problem could be to minimize or maximize a given objective function and we aim to solve the maximization problems on multimodal functions in this study. Hence, we have proposed an algorithm known as Enhanced Opposition Differential Evolution (EODE) algorithm to solve the MMOPs. The proposed algorithm has been tested on IEEE Congress on Evolutionary Computation (CEC) 2013 benchmark functions, and it achieves competitive results compared to the existing state-of-the-art approaches.