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Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization

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5 Author(s)
Chaoli Sun ; Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China ; Jianchao Zeng ; Shuchuan Chu ; Roddick, J.F.
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Constrained optimization problems compose a large part of real-world applications. More and more attentions have gradually been paid to solve this kind of problems. An improved particle swarm optimization (IPSO) algorithm based on feasibility rules is presented in this paper to solve constrained optimization problems. The average velocity of the swarm and the best history position in the particle's neighborhood are introduced as two turbulence factors, which are considered to influence the fly directions of particles, into the algorithm so as not to converge prematurely. The performance of IPSO algorithm is tested on 13 well-known benchmark functions. The experimental results show that the proposed IPSO algorithm is simple, effective and highly competitive.

Published in:

Innovations in Bio-inspired Computing and Applications (IBICA), 2011 Second International Conference on

Date of Conference:

16-18 Dec. 2011