A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization
Xin-She Yang
TL;DR
The paper addresses the fragmentation of nature-inspired optimization methods by introducing the Generalized Evolutionary Metaheuristic (GEM), a unified framework that can represent more than 20 algorithms through a compact set of updating rules. GEM employs guided randomization and centrality-based position updates, enabling it to replicate diverse search behaviors and adapt to nonlinear, constrained engineering problems. Extensive experiments on 15 benchmarks, including 10 standard functions and 5 engineering case studies, show that GEM can achieve true global optima with small populations and competitive design solutions, sometimes surpassing existing best-known results. The work offers a foundational step toward a theoretical and practical consolidation of swarm-inspired optimization, with future directions in parameter-tuning, theoretical analysis, and broader algorithm coverage.
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.