A novel swarm intelligence algorithm inspired by the grazing of sheep

Autor: Vahid Majidnezhad, Mahdi Esmailnia Kivi
Rok vydání: 2021
Předmět:
Zdroj: Journal of Ambient Intelligence and Humanized Computing. 13:1201-1213
ISSN: 1868-5145
1868-5137
DOI: 10.1007/s12652-020-02809-y
Popis: Nowadays, efficient solve of optimization problems is a vital challenge, and computational cost in complicated optimization problems is a motivation to use meta-heuristic algorithms. Nature-inspired algorithms have had good results in these problems. In this paper, a novel nature-inspired meta-heuristic algorithm, namely Sheep Flock Optimization Algorithm (SFOA) is proposed, that mimics shepherd and sheep behaviors in the pasture. The move section of SFOA is consist of three move type (1) shepherd’s guidance, (2) sheep’s interest in previous best experience, (3) sheep’s interest in approaching to other sheep. The grazing section is repeated periodically after several Iterations of the move section. A sheep is a solution, and pasture is the problem’s domain, food measure in each point is the fitness function of algorithm, and target is access to leading food sources. Algorithm efficiency is evaluated by common 23 optimization mathematical problems, including unimodal and multimodal cases and two engineering design problems. The experimental results have proven that SFOA is significantly superior compared to the state-of-the-art meta-heuristic algorithms such as SSA, SBO, GWO, as well as conventional meta-heuristic methods like GA, PSO, ICA.
Databáze: OpenAIRE
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