Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

TL;DR

This paper reviews nature-inspired metaheuristic algorithms, analyzes their core components, introduces a unified generalized evolutionary walk algorithm (GEWA), and discusses open research questions in the field.

Contribution

It provides a comprehensive overview of metaheuristics, proposes a novel unified framework with GEWA, and offers insights into their mechanisms and future challenges.

Findings

Metaheuristics are effective for global optimization.

The paper introduces the generalized evolutionary walk algorithm (GEWA).

Open questions in metaheuristic research are discussed.

Abstract

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.