Hybrid Nature-Inspired Algorithms: Methodologies, Architecture, and Reviews

Abstract

Evolutionary computation has turned into a significant problem-solving approach among several researchers. As compared to other existing techniques of global optimization, the population-based combined learning procedure, robustness, and self-adaptation are some of the vital topographies of evolutionary algorithms. In spite of evolutionary algorithms has been broadly acknowledged for resolving numerous significant real applications in various areas; however in practice, occasionally they carry only fringe performance. There is slight motivation to assume that one can discover an unvaryingly finest optimization algorithm for resolving all optimization problems. Evolutionary algorithm depiction is resolute by the manipulation and survey liaison retained during the course. All this evidently elucidates the necessity for fusion of evolutionary methodologies, and the aim is to enhance the performance of direct evolutionary approach. Fusion of evolutionary algorithms in recent times is gaining popularity owing to their proficiencies to resolve numerous legitimate problems such as, boisterous environment, fuzziness, vagueness, complexity, and uncertainty. In this paper, first we highlight the necessity for fusion of evolutionary algorithms and then we explain the several potentials of an evolutionary algorithm hybridization and also discuss the general architecture of evolutionary algorithm’s fusion that has progressed all through the recent years.