Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. (2018)

• Record Type: Journal Article
• Authors: Wang, Gai-Ge; Deb, Suash; Coelho, Leandro Dos Santos
• Is Part Of: International journal of bio-inspired computation. Volume 12:Number 1(2018)
• Page Start: 1
• Page End: 22
• Publication Date: 2018
• Subjects: earthworm optimisation algorithm -- EWA -- evolutionary computation -- benchmark functions -- improved crossover operator -- Cauchy mutation -- CM -- bio-inspired metaheuristic -- global optimisation -- swarm intelligence -- evolutionary algorithms; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimisation algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully be done by nine improved crossover operators. In addition, Cauchy mutation (CM) is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.