A Modified Comprehensive Learning Particle Swarm Optimizer

doi:10.23940/ijpe.19.09.p29.25532562

Abstract

Abstract: To overcome premature convergence and falling into local optima of particle swarm optimization (PSO), a comprehensive learning particle swarm optimizer (CLPSO) has been proposed. However, it is not good at solving unimodal problems. In this paper, we propose a modified CLPSO (MCLPSO) with three improvements. Firstly, we use opposition-based learning (OBL) to improve the initial population. Secondly, we add the best solution of the population to the list of selected particles in order to improve the convergence ability while maintaining the population diversity. Finally, we use the mean velocity of the population with a linearly decreasing probability to update the particle velocity to further improve the performance of CLPSO. The MCLPSO algorithm is tested on CEC2005 in 30 dimensions. Furthermore, the MCLPSO is conducted to solve hydrothermal scheduling problems. The experimental results demonstrate that the solution accuracy of MCLPSO is overall better than those of comparison algorithms.

Key words: global optimization, CLPSO, OBL, algorithm design

Jinwei Pang, Hongbin Dong, Jun He, and Rui Ding. A Modified Comprehensive Learning Particle Swarm Optimizer [J]. Int J Performability Eng, 2019, 15(9): 2553-2562.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” inProceedings of IEEE International Conference on Neural Networks, pp. 1942-1948, Perth, Australia, November 1995
2. T. A. A.Victoire and A. E. Jeyakumar, “Hybrid PSO-SQP for Economic Dispatch with Valve-Point Effect,” Electric Power Systems Research, Vol. 71, No. 1, pp. 51-59, September 2004
3. C. Yogesh, M. Hariharan, R. Ngadiran, A. Adom, S. Yaacob, C. Berkai, et al., “A New Hybrid PSO Assisted Biogeography-based Optimization for Emotion and Stress Recognition from Speech Signal,” Expert Systems with Applications, Vol. 69, pp. 149-158, March 2017
4. M. Maitra and A. Chatterjee, “A Hybrid Cooperative-Comprehensive Learning based PSO Algorithm for Image Segmentation using Multilevel Thresholding,” Expert Systems with Applications, Vol. 34, No. 2, pp. 1341-1350, February 2008
5. X. Yuan, A. Su, Y. Yuan, H. Nie,L. Wang, “An Improved PSO for Dynamic Load Dispatch of Generators with Valve-Point Effects,” Energy, Vol. 34, No. 1, pp. 67-74, February 2008
6. J. Shi, W. Zhang, Y. Zhang, F. Xue,T. Yang, “Mppt for PV Systems based on a Dormant PSO Algorithm,” Electric Power Systems Research, Vol. 123, pp. 100-107, June 2015
7. V. Khanna, B. K. Das, P. K. Singh, V. Panwar,D. Bisht, “A Three Diode Model for Industrial Solar Cells and Estimation of Solar Cell Parameters using PSO Algorithm,” Renewable Energy, Vol. 78, pp. 105-113, June 2015
8. A. L. G.Noguera, L. S. M. Castellanos, E. E. S. Lora, and V. R. M. Cobas, “Optimum Design of a Hybrid Diesel-Orc/Photovoltaic System using PSO: Case Study for the City of Cujubim, Brazil,” Energy, Vol. 142, pp. 33-45, January 2018
9. H. Garg, “A Hybrid PSO-GA Algorithm for Constrained Optimization Problems,” Applied Mathematics and Computation, Vol. 274, pp. 292-305, February 2016
10. T. -S. Pan, D. Kien, T. -T. Nguyen, and S. C. Chu, “Hybrid Particle Swarm Optimization with Bat Algorithm,” Genetic and Evolutionary Computing, pp. 37-47, 2015
11. Z. H. Zhan, J. Zhang, Y. Li,Y. H. Shi, “Orthogonal Learning Particle Swarm Optimization,” IEEE Transactions on Evolutionary Computation, Vol. 15, No. 6, pp. 832-847, December 2011
12. B. Y. Qu, P. N. Suganthan,S. Das, “A Distance-based Locally Informed Particle Swarm Model for Multimodal Optimization,” IEEE Transactions on Evolutionary Computation, Vol. 17, No. 3, pp. 387-402, June 2013
13. J. J. Liang, A. K. Qin, P. N. Suganthan,S. Baskar, “Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 281-295, January 2006
14. Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimizer,” inProceedings of IEEE World Congress on Computational Intelligence, pp. 69-73, Anchorage, USA, May 1998
15. H. R. Tizhoosh, “Opposition-based Learning: A New Scheme for Machine Intelligence,” inProceedings of Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on IEEE, pp. 695-701, Vienna, Austria, November 2005
16. P. N. Suganthan, N. Hansen, J. Liang, K. Deb, Y. P. Chen, A. Auger, et.al., “Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization,” KanGAL Report, Vol. 2005005, No. 2005, January 2005
17. A. Ratnaweera, S. K. Halgamuge,H. C. Watson, “Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 240-255, June 2004
18. T. Peram, K. Veeramachaneni,C. K. Mohan, “Fitness-Distance-Ratio based Particle Swarm Optimization,” inProceedings of Swarm Intelligence Symposium SIS'03, pp. 174-181, Indianapolis, USA, April 2003
19. A. P. Engelbrecht, “Heterogeneous Particle Swarm Optimization”, in Proceedings of International Conference on Swarm Intelligence, pp. 191-202, Berlin, Heidelberg, September 2010
20. R. Mendes, J. Kennedy,J. Neves, “The Fully Informed Particle Swarm: Simpler, maybe Better,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 204-210, June 2004
21. J. Derrac, S. García, D. Molina,F. Herrera, “A Practical Tutorial on the use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms,” Swarm and Evolutionary Computation, Vol. 1, No. 1, pp. 3-18, March 2011
22. S. Das and P. N. Suganthan, “Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems,” Jadavpur University, Nanyang Technological University, Kolkata, December 2010
23. Y. Wang, H. X. Li, T. Huang,L. Li, “Differential Evolution based on Covariance Matrix Learning and Bimodal Distribution Parameter Setting,” Applied Soft Computing, Vol. 18, pp. 232-247, May 2014
24. R. V. Rao, V. J. Savsani,D. P. Vakharia, “Teaching-Learning-based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, Vol. 43, No. 3, pp. 303-315, March 2011
25. W. N. Chen, J. Zhang, Y. Lin, N. Chen, Z. H. Zhan, H. S.-H. Chung, et al., “Particle Swarm Optimization with an Aging Leader and Challengers,” IEEE Transactions on Evolutionary Computation, Vol. 17, No. 2, pp. 241-258, April 2013
26. H. Wang, H. Sun, C. Li, S. Rahnamayan,J. S. Pan, “Diversity Enhanced Particle Swarm Optimization with Neighborhood Search,” Information Sciences, Vol. 223, pp. 119-135, February 2013

[1]	Shaili Mishra and Anuja Arora. Double Deep Q Network with Huber Reward Function for Cart-Pole Balancing Problem [J]. Int J Performability Eng, 2022, 18(9): 644-653.
[2]	Anita Agárdi, László Kovács, and Tamás Bányai. Neutrality of Vehicle Routing Problem [J]. Int J Performability Eng, 2021, 17(10): 848-857.
[3]	Anita Agárdi, László Kovács, and Tamás Bányai. Using Time Series and Classification in Vehicle Routing Problem [J]. Int J Performability Eng, 2021, 17(1): 14-25.
[4]	Jinchao Zhao, Yinping Jin, Xi Lin, and Xiao Wang. A Novel Submitochondrial Localization Predictor based on Gradient Boosting Algorithm and Dataset Balancing Treatment [J]. Int J Performability Eng, 2020, 16(7): 1038-1045.
[5]	You Zhou, Lecheng Sun, Xu Zhou, Milan Parmar, and Liupu Wang. An Improved Genetic Algorithm to Optimize Vehicle Scheduling for Relief Efforts [J]. Int J Performability Eng, 2019, 15(9): 2356-2363.
[6]	Lei Wu and Weijian Wang. Resource Allocation Optimization of UAVs-Enabled Air-Ground Collaborative Emergency Network in Disaster Area [J]. Int J Performability Eng, 2019, 15(8): 2133-2144.
[7]	Ashutosh Sharma, Rajiv Kumar, and Pradeep Kumar Singh. SLA Constraint Quickest Path Problem for Data Transmission Services in Capacitated Networks [J]. Int J Performability Eng, 2019, 15(4): 1061-1072.
[8]	Guozhong Dong, Bei Li, Xinhong Wei, and Tao Qin. Mining Key Users of Microblog Topics based on Trust Model [J]. Int J Performability Eng, 2019, 15(11): 3024-3030.
[9]	Yu Li, Qian Guo, and Jingsen Liu. Improved Bat Algorithm for Vehicle Routing Problem [J]. Int J Performability Eng, 2019, 15(1): 317-325.
[10]	Weiwei Zhang, Menghua Zhang, Weizheng Zhang, Yinghui Meng, and Huaiguang Wu. Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization [J]. Int J Performability Eng, 2018, 14(9): 2048-2055.