Abstract
Various intelligent algorithms are applied in optimization design, and the differential evolution (DE) algorithm is widely applied with its excellent convergence speed and convergence precision. This study analyzed the advantages and disadvantages of the existing multi-objective differential evolution (MODE) algorithm, and developed a MODE algorithm based on the adaptive weight and the multi-population strategy (MODE/AWMS). The proposed algorithm was verified using test functions. MODE/AWMS exhibited certain advantages compared with several other multi-objective optimization algorithms. Taking a polarized magnetic relay as an example, MODE/AWMS was used to optimize its key parameters by establishing a rapid calculation model of its electromagnetic mechanism. The electromagnetic force (EMF) of the release position was improved, which verified the validity of MODE/AWMS.
Highlights
For most electromagnetic devices, is it necessary to simultaneously optimize multiple targets within a given interval, but there is often a possibility of conflicting and mutually constrained relationships among the objectives
Aimed at the shortcomings of most MODEAs, such as easy convergence to local optimal solution and poor convergence precision, in this paper, this paper proposed an improved differential evolution algorithm (DEA) based on an adaptive weight and a multi-population strategy, which was compared with some other algorithms by using several performance parameters, and was verified by using an instance
The results were as follows: (1) In the process of the mutation operation and the cross operation, the multi-objective differential evolution (MODE)/AWMS selected different mutation numbers and mutation strategies to guide the mutation process according to the current population iteration number and the Pareto dominant relationship of each individual
Summary
Is it necessary to simultaneously optimize multiple targets within a given interval, but there is often a possibility of conflicting and mutually constrained relationships among the objectives. N. Baatar et al proposed an adaptive parameter controlling non-dominated ranking differential evolution (A-NRDE) algorithm for the multi-objective optimal design of electromagnetic problems [14]. The original DE algorithm was improved, and a DEA with an adaptive weight and a multipopulation strategy (MODE/AWMS) was developed.
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