Dynamic and adjustable particle swarm optimization

Authors:
Chen-Yi Liao;Wei-Ping Lee;Xianghan Chen;Cheng-Wen Chiang
Affiliations:
Department of Management Information System, Chung Yuan University, Taoyuan, Taiwan, R.O.C.;Department of Management Information System, Chung Yuan University, Taoyuan, Taiwan, R.O.C.;Department of Management Information System, Chung Yuan University, Taoyuan, Taiwan, R.O.C.;Department of Management Information System, Chung Yuan University, Taoyuan, Taiwan, R.O.C.
Venue:
EC'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Evolutionary Computing - Volume 8
Year:
2007

Citing 4
Cited 0

Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Scheduling tasks in real-time systems using evolutionary strategies

WPDRTS '95 Proceedings of the 3rd Workshop on Parallel and Distributed Real-Time Systems
Exploring extended particle swarms: a genetic programming approach

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Particle Swarm Optimization (PSO) is a stochastic, population-based evolutionary search technique. It has difficulties in controlling the balance between exploration and exploitation. In order to improve the performance of PSO and maintain the diversities of particles, we propose a novel algorithm called Dynamic and Adjustable Particle Swarm Optimization (DAPSO). The distance from each particle to the global best position is calculated in order to adjust the velocity suitably of each particle. Four benchmark functions such as Sphere, Rosenbrock, Rastrigrin, Griewank are used for the comparison of DAPSO with the Standard PSO. The experiments prove that DAPSO has better performance than the Standard PSO.