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Solving multiobjective problems using cat swarm optimization
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DOI：10.1016/j.eswa.2011.08.157.png)
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Abstract
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This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs). (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Multiobjective problems
Evolutionary algorithm
Swarm optimization
Cat swarm optimization
Multiobjective cat swarm optimization
Pareto dominance
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10.2W
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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach
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