Journal
IEEE ACCESS
Volume 10, Issue -, Pages 29393-29405Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3158666
Keywords
Swarm evolutionary computing; particle swarm optimization; chaos theory; function optimization
Categories
Funding
- Shandong Provincial Natural Science Foundation, China [ZR2020MF006]
- Industry-University Research Innovation Foundation of Ministry of Education of China [2021FNA01001]
- Major Scientific and Technological Projects of China National Petroleum Corporation (CNPC) [ZD2019-183-006]
- Fundamental Research Funds for the Central Universities of China University of Petroleum (East China) [20CX05017A]
- Youth Fund for Science and Technology Research in Colleges and Universities of Hebei Province [QN2021066]
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This paper proposes a novel PSO algorithm called CAPSO, which adaptively adjusts parameters to achieve better global optimal solutions and escape local optimal solutions. Experimental verification proves that CAPSO performs exceptionally well in terms of stability, convergence speed, and accuracy.
As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter w and acceleration coefficients c(1), c(2), and introduces a controlling factor gamma based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.
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