Improved particle swarm optimization based on greedy and adaptive features

From the inception of Particle Swarm Optimization (PSO) technique, a lot of work has been done by researchers to enhance its efficiency in handling optimization problems. However, one of the general operations of the algorithm still remains - obtaining global best solution from the personal best solutions of particles in a greedy manner. This is very common with many of the existing PSO variants. Though this method is promising in obtaining good solutions to optimization problems, it could make the technique susceptible to premature convergence in handling some multimodal optimization problems. In this paper, the basic PSO (Linear Decreasing Inertia Weight PSO algorithm) is used as case study. An adaptive feature is introduced into the algorithm to complement the greedy method towards enhancing its effectiveness in obtaining optimal solutions for optimization problems. The enhanced algorithm is labeled Greedy Adaptive PSO (GAPSO) and some typical continuous global optimization problems were used to validate its effectiveness through empirical studies in comparison to the basic PSO. Experimental results show that GAPSO is more efficient.

[1]  Aderemi Oluyinka Adewumi,et al.  On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization , 2013, TheScientificWorldJournal.

[2]  Kusum Deep,et al.  Novel inertia weight strategies for particle swarm optimization , 2013, Memetic Comput..

[3]  Aderemi Oluyinka Adewumi,et al.  Three new stochastic local search algorithms for continuous optimization problems , 2013, Computational Optimization and Applications.

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

[5]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[6]  Aderemi Oluyinka Adewumi,et al.  Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems , 2014, TheScientificWorldJournal.

[7]  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.

[8]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[9]  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).

[10]  Jing Bai,et al.  Different inertia weight PSO algorithm optimizing SVM kernel parameters applied in a speech recognition system , 2009, 2009 International Conference on Mechatronics and Automation.

[11]  Zhihua Cui,et al.  Individual Parameter Selection Strategy for Particle Swarm Optimization , 2009 .

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

[13]  Bijaya K. Panigrahi,et al.  Hybrid Bacterial Foraging with parameter free PSO , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

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

[15]  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).

[16]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[17]  Hifza Afaq.  On the Solutions to the Travelling Salesman Problem using Nature Inspired Computing Techniques , 2011 .

[18]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2011, IEEE Trans. Evol. Comput..

[19]  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.

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