Improved global-best-guided particle swarm optimization with learning operation for global optimization problems |
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| Affiliation: | 1. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, China;2. College of Information & Science, Northeastern University, Shenyang 110819, China;3. Graduate School of Business and Law, RMIT University, Melbourne 3000, Australia;4. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China;1. Department of Mathematics, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta, North Cyprus, Mersin-10, Turkey;2. Department of Computer Science and Information Technology, Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania;1. School of Computer Science, Laboratory of Cognitive Modeling and Algorithms, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China;2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China;1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;3. Guilin University of Electronic Technology, Guilin, China;4. School of Engineering, University of Glasgow, Glasgow, UK;1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116,China;2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100090,China;3. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China;1. School of Aerospace, Transport Systems and Manufacturing, Cranfield University, College Road, Bedfordshire MK43 0AL, UK;2. College of Engineering, Mathematics and Physical Systems, University of Exeter, EX4 4SB, UK |
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| Abstract: | In this paper, an improved global-best-guided particle swarm optimization with learning operation (IGPSO) is proposed for solving global optimization problems. The particle population is divided into current population, historical best population and global best population, and each population is assigned a corresponding searching strategy. For the current population, the global neighborhood exploration strategy is employed to enhance the global exploration capability. A local learning mechanism is used to improve local exploitation ability in the historical best population. Furthermore, stochastic learning and opposition based learning operations are employed to the global best population for accelerating convergence speed and improving optimization accuracy. The effects of the relevant parameters on the performance of IGPSO are assessed. Numerical experiments on some well-known benchmark test functions reveal that IGPSO algorithm outperforms other state-of-the-art intelligent algorithms in terms of accuracy, convergence speed, and nonparametric statistical significance. Moreover, IGPSO performs better for engineering design optimization problems. |
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| Keywords: | Particle swarm optimization Global exploration capability Convergence speed Accuracy |
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