A Non Dominant Sorting Algorithm with Dual Population Dynamic Collaboration

Solving multi-objective optimization problems with constraints is a complex task, involving the delicate balance of interrelated and conflicting objective functions and constraint values. While numerous evolutionary algorithms have been developed to tackle this issue, they often struggle to effectively balance population feasibility, convergence, and diversity. In this paper, we introduce the bi-population dynamic coordinated non-dominated sorting algorithm (DDCNDS), which maintains two dynamically coordinated populations and can adjust population size at different evolution stages to balance feasibility and convergence. During the early stages of evolution, the focus is primarily on convergence, with the first population having a larger size to facilitate convergence to the feasible domain across infeasible regions. In the later stages, the emphasis shifts to feasibility, with the second population having a larger size and utilizing local search to explore the feasible domain. Additionally, we propose a non-dominated criterion sorting method to select better individuals, adjusting the non-dominated level based on the proportion of feasible solutions in the input population. Experimental results on three well-known benchmark suites demonstrate the competitiveness of the proposed algorithm.