A Novel Multi-Objective Competitive Swarm Optimization Algorithm

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive sce...

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Vydáno v:International journal of applied metaheuristic computing Ročník 11; číslo 4; s. 114 - 129
Hlavní autoři: Dey, Nilanjan, Kumar, Ram, Mohapatra, Prabhujit, Das, Kedar Nath, Roy, Santanu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hershey IGI Global 01.10.2020
Témata:
Algorithms
Analysis
Archives & records
Business metrics
Competition
Decomposition
Efficiency
Genetic algorithms
Mathematical optimization
Multiple objective analysis
Objectives
Optimization algorithms
Optimization techniques
Statistical tests
India
ISSN:1947-8283, 1947-8291
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Shrnutí:In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1947-8283
1947-8291
DOI:10.4018/IJAMC.2020100106