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Algorithms 2015, 8(2), 157-176; doi:10.3390/a8020157

Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition

1
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China
2
Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an 710048, China
*
Author to whom correspondence should be addressed.
Academic Editor: George Karakostas
Received: 12 January 2015 / Revised: 17 March 2015 / Accepted: 17 April 2015 / Published: 23 April 2015
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Abstract

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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