IRCMO: Biologically Inspired Dual-Population Coevolution via Immune Tolerance and Homeostatic Resource Allocation for Constrained Multi-Objective Optimization

Constrained multiobjective optimization seeks Pareto optimal tradeoffs among conflicting objectives subject to prescribed constraints. Although the final solutions must be feasible, evolutionary variation can generate infeasible intermediate decision vectors during the search. One difficulty in this process is identifying mildly infeasible candidates that still provide valuable search directions, while another is distributing a fixed evaluation budget between cooperative populations whose contributions vary during evolution. To address these issues, this paper proposes IRCMO, a biologically inspired dual population constrained multiobjective evolutionary algorithm. The main population focuses on approximating the Pareto front formed by feasible solutions, while the auxiliary population preserves complementary search directions near constraint boundaries in the decision space. An Immune Tolerance and Niche Exclusion Selection strategy (ITNES) adaptively determines a tolerance boundary from the current feasibility status. It applies a squared response only to excess constraint violation and preserves sparse search directions in both the objective and decision spaces. This enables the auxiliary population to exploit mildly infeasible candidates without losing feasibility pressure, thereby improving convergence and front coverage under restrictive constraint structures. A Replicator Complementarity Homeostatic Resource Allocation strategy (RCHRA) evaluates offspring improvement and cross population complementarity. It assigns more offspring evaluations to the population making a stronger current contribution while maintaining a minimum resource share for both populations. This improves the utilization of the fixed evaluation budget and reduces persistent search imbalance between the two populations. Experiments on 47 benchmark problems and 12 real world CMOPs against seven representative algorithms show that IRCMO obtains the lowest overall average ranks for both IGD and HV. All pairwise Wilcoxon tests are significant at the 0.05 level.