A Modified Dynamic Particle Swarm Optimization Algorithm

Inspired from social behavior of organisms such as bird flocking, particle swarm optimization(PSO) has rapid convergence speed and has been successfully applied in many optimization problems. in this paper, we present a dynamic particle swarm optimization algorithm to enhance the performance of stan...

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Veröffentlicht in:2012 fifth International Symposium on Computational Intelligence and Design : 28-29 October 2012 Jg. 1; S. 432 - 435
1. Verfasser: Liu Wen
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2012
Schlagworte:
Algorithm design and analysis
Convergence
global solution
Heuristic algorithms
inertia weight
local solution
Optimization
Particle swarm optimization
Search problems
ISBN:1467326461, 9781467326469
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Zusammenfassung:Inspired from social behavior of organisms such as bird flocking, particle swarm optimization(PSO) has rapid convergence speed and has been successfully applied in many optimization problems. in this paper, we present a dynamic particle swarm optimization algorithm to enhance the performance of standard PSO. We design a novel function to compute the initial dynamic inertia weight, and then calculate inertia weight through a nonlinear function. Afterwards, searching process is repeated until the max iteration number is reached or the minimum error condition is satisfied. to testify the effectiveness of the proposed algorithm, we conduct two experiments. Experimental results show that our algorithm performs better than FPSO and standard PSO in best fitness and convergence speed.
ISBN:1467326461
9781467326469
DOI:10.1109/ISCID.2012.114