Comparisons study of APSO OLPSO and CLPSO on CEC2005 and CEC2014 test suits

Particle swarm optimization (PSO) is originally designed to solve continuous optimization problems. Recently, lots of improved PSO variants with different features have been proposed, such as Adaptive particle swarm optimization (APSO), Orthogonal Learning particle swarm optimization (OLPSO) and Comprehensive Learning particle swarm optimization (CLPSO). In order to find out whether these PSOs have any particular difficulties or preference and whether one of them would outperform the others on a majority of the tested problems, we analyze the performance of different PSOs on various tested problems. In this paper, we evaluate the performance of APSO, OLPSO, and CLPSO on more complex benchmark functions. The comparison is performed on a large amount of real-parameter optimization problems, including the CEC 2005 and the CEC 2014 benchmark functions. Finally, we find out that the OLPSO achieves higher solution quality than the other two PSOs on most problems based on the simulation results on benchmark functions.

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

[2]  Ponnuthurai N. Suganthan,et al.  A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[3]  Jun Zhang,et al.  Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems , 2015, 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]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[7]  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.

[8]  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).

[9]  The application of improved particle swarm optimization algorithm involtage stability constrained optimal power flow , 2013, Proceedings of 2013 2nd International Conference on Measurement, Information and Control.

[10]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[12]  Mingyue Ding,et al.  Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

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

[14]  Angel Eduardo Muñoz Zavala,et al.  A Hybrid System Approach to Determine the Ranking of a Debutant Country in Eurovision , 2009, J. Comput..

[15]  Du Ya-ping,et al.  Research on airport apron service based on particle swarm optimization , 2013, 2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings.

[16]  Paola Batistoni,et al.  International Conference , 2001 .

[17]  Angel Eduardo Muñoz Zavala,et al.  Constrained optimization via particle evolutionary swarm optimization algorithm (PESO) , 2005, GECCO '05.

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

[19]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[20]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

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

[22]  Alberto Ochoa,et al.  A Hybrid System Approach to Determine the Ranking of a Debutant Country in Eurovision , 2009 .