A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization

TL;DR

This paper introduces a generalized evolutionary metaheuristic (GEM) algorithm that unifies over 20 nature-inspired algorithms to better understand their mechanisms and improve engineering optimization solutions.

Contribution

The paper proposes a unified framework (GEM) that consolidates multiple existing metaheuristic algorithms, facilitating analysis and comparison of their search mechanisms.

Findings

GEM successfully unifies over 20 algorithms.

Validated GEM performance on 15 benchmark problems.

Provides insights into similarities among nature-inspired algorithms.

Abstract

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.