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| An Improved Self-Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition |
| PANG Rui , GAO Xingbao
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| School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China |
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Abstract In order to improve the search efficiency of particle swarm optimization and overcome the weakness of the decomposition method to deal with complex multi-objective problems, an improved self-adaptive multi-objective particle swarm optimization based on decomposition is proposed by considering the important influence of parent solutions selection and population updating on the convergence of algorithm and the distribution uniformity of solutions. To improve the convergence speed,a new fitness evaluation method is first designed to estimate solutions’ quality and the quality offspring solution won in the competition is added to the parent candidate solutions under the premise of ensuring diversity of evolutionary population by decomposition method. Next, to avoid the algorithm falling into local optimum, the personal optimal and global optimal positions are randomly selected from current particles’ neighbors or outside of neighbors when updating the particles. Last, to enhance the ability of algorithm to deal with complex problems, external archive is introduced as a candidate output population and crowding distance is used to maintain its diversity.The numerical experiments are carried out on twelve test functions and compared with five multi-objective optimization algorithms that can show the superiority of proposed algorithm.
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Received: 17 April 2018
Published: 09 January 2019
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