Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybri...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Evolutionary computation Ročník 18; číslo 1; s. 127
Hlavní autori: Elhossini, Ahmed, Areibi, Shawki, Dony, Robert
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.03.2010
Predmet:
Algorithms
Computer Simulation
Models, Theoretical
Search Engine - methods
ISSN:1530-9304, 1530-9304
On-line prístup:Zistit podrobnosti o prístupe
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1530-9304
1530-9304
DOI:10.1162/evco.2010.18.1.18105