Li Ma¹, Cai Dai^1,*, Xingsi Xue², Cheng Peng³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 997-1026, 2025, DOI:10.32604/cmc.2024.057168 - 03 January 2025

Abstract The multi-objective particle swarm optimization algorithm (MOPSO) is widely used to solve multi-objective optimization problems. In the article, a multi-objective particle swarm optimization algorithm based on decomposition and multi-selection strategy is proposed to improve the search efficiency. First, two update strategies based on decomposition are used to update the evolving population and external archive, respectively. Second, a multi-selection strategy is designed. The first strategy is for the subspace without a non-dominated solution. Among the neighbor particles, the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle… More >

Dazhi Wang^*, Pengyi Pan, Bowen Niu

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1535-1555, 2023, DOI:10.32604/cmc.2023.042286 - 29 November 2023

Abstract The permanent magnet eddy current coupler (PMEC) solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems. It provides torque to the load and generates heat and losses, reducing its energy transfer efficiency. This issue has become an obstacle for PMEC to develop toward a higher power. This paper aims to improve the overall performance of PMEC through multi-objective optimization methods. Firstly, a PMEC modeling method based on the Levenberg-Marquardt back propagation (LMBP) neural network is proposed, aiming at the characteristics of… More >

Haitao Yue¹, Chenguang Guo^1,*, Qiang Li¹, Lijuan Zhao¹, Guangbo Hao²

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 937-952, 2020, DOI:10.32604/cmes.2020.010565 - 21 August 2020

Abstract To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy consumption. Experimental research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based on Response Surface Methodology was carried out. The single factor and interaction of milling parameters on surface roughness and specific cutting energy were analyzed, and the multi-objective optimization model was constructed. The Multiobjective Particle Swarm Optimization algorithm introducing the Chaos Local Search algorithm and the adaptive inertial weight was applied to determine the optimal combination of milling parameters. It was observed that surface roughness… More >