%0 Conference Proceedings
%T Two Improvement Strategies for PSO
%+ Key Laboratory of Intelligent Information Processing; Institute of Computing Technology
%+ School of Information and Electronics Engineering
%A Dou, Quansheng
%A Liu, Shasha
%A Jiang, Ping
%A Zhou, Xiuhua
%A Shi, Zhongzhi
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP)
%C Manchester, United Kingdom
%Y Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake
%I Springer
%3 Intelligent Information Processing V
%V AICT-340
%P 122-129
%8 2010-10-13
%D 2010
%R 10.1007/978-3-642-16327-2_17
%K Particle Swarm Optimization
%K Convergence Property
%K Improved Strategy
%Z Computer Science [cs]/Digital Libraries [cs.DL]Conference papers
%X This paper proposed an improved particle swarm optimization algorithm (IPSO) to solve continuous function optimization problems. Two improvement strategies named "Vector correction strategy" and "Jump out of local optimum strategy" were employed in our improved algorithm. The algorithm was tested using 25 newly proposed benchmark instances in Congress on Evolutionary Computation 2005 (CEC2005). The experimental results show that the search efficiency and the ability of jumping out from the local optimum of the IPSO have been significantly improved, and the improvement strategies are effective.
%G English
%2 https://inria.hal.science/hal-01055064/document
%2 https://inria.hal.science/hal-01055064/file/Two_Improvement_Strategies_for_PSO.pdf
%L hal-01055064
%U https://inria.hal.science/hal-01055064
%~ IFIP
%~ IFIP-AICT
%~ IFIP-AICT-340
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-IIP
%~ IFIP-2010