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Open AccessArticle

Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition

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School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China
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Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an 710048, China
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Author to whom correspondence should be addressed.
Academic Editor: George Karakostas
Algorithms 2015, 8(2), 157-176; https://doi.org/10.3390/a8020157
Received: 12 January 2015 / Revised: 17 March 2015 / Accepted: 17 April 2015 / Published: 23 April 2015
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has received attention from researchers in recent years. This paper presents a new multiobjective algorithm based on decomposition and the cloud model called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). In the proposed method, the best solution found so far acts as a seed in each generation and evolves two individuals by cloud generator. A new individual is produced by updating the current individual with the position vector difference of these two individuals. The performance of the proposed algorithm is carried on 16 well-known multi-objective problems. The experimental results indicate that MOEA/D-CPDE is competitive. View Full-Text
Keywords: cloud particle differential evolution; MOEA/D; multiobjective optimization; evolutionary algorithm; cloud mutation cloud particle differential evolution; MOEA/D; multiobjective optimization; evolutionary algorithm; cloud mutation
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Li, W.; Wang, L.; Jiang, Q.; Hei, X.; Wang, B. Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition. Algorithms 2015, 8, 157-176.

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