Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation

The paper gives an improved particle swarm optimal algorithm in which a kind of exponent decreasing inertia weights is given to improve the convergence speed and a kind of stochastic mutations is used to improve the diversity of the swarm in order to overcome the disadvantage of premature convergence and later period oscillatory occurrences. It is shown by five representative benchmarks function’s test that the improved algorithm is better than both a particle swarm optimization with linear decreasing inertia weight and a particle swarm optimization with exponent decreasing inertia weight in global searching and performance.

[1]  Yuelin Gao,et al.  An Adaptive Particle Swarm Optimization Algorithm with New Random Inertia Weight , 2007, ICIC.

[2]  Yuelin Gao,et al.  Adaptive Particle Swarm Optimization Algorithm With Genetic Mutation Operation , 2007, Third International Conference on Natural Computation (ICNC 2007).

[3]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[4]  Jun Tang,et al.  Particle Swarm Optimization with Adaptive Mutation , 2009, 2009 WASE International Conference on Information Engineering.

[5]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[6]  Hou Zhi-rong,et al.  Particle Swarm Optimization with Adaptive Mutation , 2006 .

[7]  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).

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Jian Wang,et al.  An Improved Particle Swarm Optimization Algorithm , 2011 .

[12]  X. Li,et al.  A New Adaptive Particle Swarm Optimization Algorithm , 2008, 2008 International Workshop on Modelling, Simulation and Optimization.

[13]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[14]  Yin Chuan,et al.  A Branch and Bound-PSO Hybrid Algorithm for Solving Integer Separable Concave Programming Problems 1 , 2007 .

[15]  Gao Yue-lin.  Adaptive Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight , 2008 .

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

[17]  David B. Fogel,et al.  Evolutionary programming , 2011, Scholarpedia.