Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

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.

14 pages

Cited by in corpus (5)

• Bat Algorithm for Multi-objective Optimisation
• No Free Lunch Theorem and Bayesian probability theory: two sides of the same coin. Some implications for black-box optimization and metaheuristics
• Trends in the optimal location and sizing of electrical units in smart grids using meta-heuristic algorithms
• A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
• Bio-Inspired Computation: Success and Challenges of IJBIC