NDLI: Fitness proportional hypermutations within memory-based colonization in particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Acan, A. Unveren, A.
Copyright Year	2009
Description	Author affiliation: Eastern Mediterranean University, Computer Engineering Department, Gazimagusa, TRNC, Mersin 10, TURKEY (Acan, A.; Unveren, A.)
Abstract	In this paper, fitness proportional hypermutations inspired from studies on artificial immune systems are used to improve the performance of a memory-based colonization scheme implemented in PSO. For each particle, externally implemented global- (shared) and particle-based (local) memories are considered for the generation of a colony of velocity and position vectors. In this respect, a set of velocities is computed for each particle using each of the personal best positions within its local memory and each of the global positions from the shared memory. This way, a colony of new positions is obtained for each particle and the one with the best fitness is selected and put within the new swarm. Global and local memories are also updated using the solutions within each colony. Experimental evaluations demonstrated that the proposed strategy outperformed the conventional and other known memory-based PSO algorithms for all problem instances within the CEC2005 test suit.
Starting Page	1
Ending Page	4
File Size	352534
Page Count	4
File Format	PDF
ISBN	9781424434299
DOI	10.1109/ICSCCW.2009.5379416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-02
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Numerical optimization Artificial immune systems Optimization methods Hypercubes Particle measurements Acceleration Memory-methods Particle swarm optimization Power generation Testing Power engineering and energy Genetic algorithms
Content Type	Text
Resource Type	Article

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