Abstract
Particle swarm optimization (PSO) is widely implemented as an optimizer due its characteristics of simple implementation and fast convergence speed. However, it has tendency to cause premature convergence and population diversity loss if the population is not well-initialized due to its randomness. In this research, a PSO with Multi-Chaotic Scheme (PSOMCS) is introduced to generate promising initial population to improve the population diversity and fitness of the candidate solution. Multiple chaotic system is dependent on the initial condition that can cover a broader region of the search space. By using multiple chaotic maps, the introduced method is able to solve different types of problems effectively as each type of chaotic maps has better performance in solving a specific problem. The performance comparisons of PSOMCS and the existing PSO variants are conducted by using the test functions of CEC 2014. The simulation analyses reported that PSOMCS outperforms its competitors with respect to total mean fitness.
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Acknowledgements
This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme under Project Proj-FRGS/1/2019/TK04/UCSI/02/1.
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Cheng, WL. et al. (2022). Particle Swarm Optimization with Multi-Chaotic Scheme for Global Optimization. In: Khairuddin, I.M., et al. Enabling Industry 4.0 through Advances in Mechatronics. Lecture Notes in Electrical Engineering, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-19-2095-0_14
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