Inertia Weight strategies in Particle Swarm Optimization

Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.

[1]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  Yong Feng,et al.  Chaotic Inertia Weight in Particle Swarm Optimization , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  M. Rao,et al.  On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems , 2006 .

[6]  Xinbo Huang,et al.  Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[7]  Reza Firsandaya Malik,et al.  New particle swarm optimizer with sigmoid increasing inertia weight , 2007 .

[8]  Guimin Chen,et al.  A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[9]  Yuelin Gao,et al.  A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation , 2008, 2008 International Conference on Computational Intelligence and Security.

[10]  S.I. Shaheen,et al.  PSOSA: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem , 2006, 2006 International Conference on Computer Engineering and Systems.

[11]  Kyriakos Kentzoglanakis,et al.  Particle swarm optimization with an oscillating inertia weight , 2009, GECCO.

[12]  Yuelin Gao,et al.  Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation , 2009, 2009 Second International Conference on Information and Computing Science.

[13]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).