Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems

•Use an external archive to guide the chicken swarm to find the Pareto optimal solutions.•Use an aggregate function to define the social hierarchy of chickens during the exploration of search spaces.•Adapt the chicken’s movements to explore a multi-objective search space.•Use the epsilon dominance a...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computers & industrial engineering Ročník 129; s. 377 - 391
Hlavní autoři: Zouache, Djaafar, Ould Arby, Yahya, Nouioua, Farid, Ben Abdelaziz, Fouad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2019
Témata:
Chicken swarm optimization
Crowding distance
Multi-objective optimization
Pareto dominance
Swarm intelligence
Crowding distance
Swarm intelligence
Multi-objective optimization
Chicken swarm optimization
Pareto dominance
ISSN:0360-8352, 1879-0550
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•Use an external archive to guide the chicken swarm to find the Pareto optimal solutions.•Use an aggregate function to define the social hierarchy of chickens during the exploration of search spaces.•Adapt the chicken’s movements to explore a multi-objective search space.•Use the epsilon dominance and the crowding distance to preserve the diversity of chicken’s population. In this paper, we extend the chicken swarm optimization (CSO) to solve multi-objective optimization problems. Our extention aims to balance between diversity and convergence when searching for the optimal Pareto solutions. We use aggregation function to define the social hierarchy and simulate the behavior of chickens during the search for food in the objective search space while applying epsilon dominance and crowding distance to preserve the diversity of the solutions population. We also address the integration of the archive population that guides the chicken swarm towards the Pareto optimal solutions. The proposed algorithm is validated on twelve test functions and compared with five well-known meta-heuristics. The results show the ability of MOCSO algorithm to provide a better spread of solutions with faster convergence.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2019.01.055