NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm.

ABSTRACT: Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchma

SUBMITTER: Huang H 

PROVIDER: S-EPMC10881516 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature