Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems

This paper proposes an information sharing mechanism (ISM) to improve the performance of particle swarm optimization (PSO). The ISM allows each particle to share its best search information, so that all the other particles can take advantage of the shared information by communicating with it. In this way, the particles could enhance the mutual interaction with the others sufficiently and heighten their search ability greatly by using the search information of the whole swarm. Also, a competitive and cooperative (CC) operator is designed for a particle to utilize the shared information in a proper and efficient way. As the ISM share the search information among all the particles, it is an appropriate way to mix up information of the whole swarm for a better exploration of the landscape. Therefore, the competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems. The satisfactory performance of CCPSO-ISM is evaluated by comparing it with other variants of PSOs on a set of 16 global optimization functions. Moreover, the effectiveness and efficiency of CCPSO-ISM is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality.

[1]  William R. Charlesworth,et al.  Co-operation and Competition: Contributions to an Evolutionary and Developmental Model , 1996 .

[2]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[3]  Hao Gao,et al.  Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation , 2013, Inf. Sci..

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

[5]  Jing J. Liang,et al.  Niching particle swarm optimization with local search for multi-modal optimization , 2012, Inf. Sci..

[6]  Tak Kuen Siu,et al.  Credit portfolio management using two-level particle swarm optimization , 2013, Inf. Sci..

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

[8]  Siti Mariyam Hj. Shamsuddin,et al.  CAPSO: Centripetal accelerated particle swarm optimization , 2014, Inf. Sci..

[9]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[10]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[11]  Nasser R. Sabar,et al.  A harmony search algorithm for nurse rostering problems , 2013, Inf. Sci..

[12]  Xiao-Lin Li,et al.  A hybrid particle swarm optimization method for structure learning of probabilistic relational models , 2014, Inf. Sci..

[13]  Tad Hogg,et al.  Solving the Really Hard Problems with Cooperative Search , 1993, AAAI.

[14]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[15]  Paul S. Andrews,et al.  An Investigation into Mutation Operators for Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  R. Salomon.  Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[17]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[19]  Bo Yang,et al.  Improving particle swarm optimization using multi-layer searching strategy , 2014, Inf. Sci..

[20]  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.

[21]  J. Kennedy.  Stereotyping: improving particle swarm performance with cluster analysis , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[22]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[23]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[24]  Jinhua Zheng,et al.  Achieving balance between proximity and diversity in multi-objective evolutionary algorithm , 2012, Inf. Sci..

[25]  Mu-Yen Chen,et al.  A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering , 2013, Inf. Sci..

[26]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[27]  Meie Shen,et al.  Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks , 2014, IEEE Transactions on Industrial Electronics.

[28]  Yu-Lin He,et al.  Particle swarm optimization for determining fuzzy measures from data , 2011, Inf. Sci..

[29]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[30]  Andries Petrus Engelbrecht,et al.  Locating multiple optima using particle swarm optimization , 2007, Appl. Math. Comput..

[31]  Dun-Wei Gong,et al.  A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch , 2012, Inf. Sci..

[32]  Qing-Long Han,et al.  A finite-time particle swarm optimization algorithm for odor source localization , 2014, Inf. Sci..

[33]  Salwani Abdullah,et al.  A multi-population harmony search algorithm with external archive for dynamic optimization problems , 2014, Inf. Sci..

[34]  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).

[35]  Ruhul A. Sarker,et al.  Self-adaptive mix of particle swarm methodologies for constrained optimization , 2014, Inf. Sci..

[36]  Konstantinos P. Anagnostopoulos,et al.  A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem , 2014, Inf. Sci..

[37]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[38]  Jhih-Chung Chang,et al.  A robust adaptive array beamformer using particle swarm optimization for space-time code division multiple access systems , 2014, Inf. Sci..

[39]  P. N. Suganthan,et al.  A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization , 2012, Inf. Sci..

[40]  Jiannong Cao,et al.  Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems , 2013, IEEE Transactions on Cybernetics.

[41]  Hui Wang,et al.  Diversity enhanced particle swarm optimization with neighborhood search , 2013, Inf. Sci..

[42]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[43]  T. Ray,et al.  A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).