A novel dynamic multi-objective optimization algorithm based on EFO and quantum mechanics.

Considering the extensive application of dynamic multi-objective optimization problems (DMOPs) and the significance of the quality of solutions, developing optimization methods to find the finest solutions takes a privileged position, attracting considerable interest. Most optimization methods involve multiple conflicting objectives that change over time. The present article develops an electromagnetic field optimization (EFO) using decomposition, crowding distance, and the quantum behavior of particles techniques to solve multi-objective problems. In the proposed algorithm, the position of new particles is determined between the neighbors within the MOEA/D by drawing inspiration from the quantum delta potential well model, the nonlinear trajectory of quantum-behaved particles, and the interactions of electromagnetic particles introduced from positive and negative fields, which can offer superior exploration and exploitation. To develop the proposed algorithm for solving dynamic problems, the mean difference between particles' center of mass in the two latest changes to predict the extent of change is applied along with polynomial mutation and random reproduction. A total of 9 benchmarks from the set of DF functions and two metrics, i.e., MIGD and MHV, are used to assess the performance of the proposed algorithm. The results from 20 independent runs of the proposed algorithm on each benchmark function are compared with the results from other algorithms. The Wilcoxon Rank-Sum non-parametric statistical test is applied at the significance level of 5% to compare the mean results. The experimental results indicated that the proposed algorithm gains a significant superiority in metrics MIGA and MHV in most experiments. The simultaneously great results of these two metrics indicate a superior distribution and approximation of the Pareto front.