Deep sea anglerfish symbiosis optimization for heterogeneous role structured engineering optimization

This paper proposes Deep-Sea Anglerfish Symbiosis Optimization (DASO), a heterogeneous metaheuristic for complex engineering optimization. DASO addresses premature convergence and population diversity loss in high-dimensional, non-convex landscapes by assigning differentiated search roles to male and female agents. Its framework integrates functional dimorphism, asymmetrical attachment, local field-guided movement, and nonlinear high-pressure adaptation to coordinate global exploration and local refinement. DASO was evaluated on the CEC2017 and CEC2022 benchmark suites in 10-, 30-, and 50-dimensional settings, with 30 independent runs for each configuration, and was further tested on constrained engineering design problems. The results show that DASO achieves competitive performance in solution quality, convergence behavior, and robustness compared with representative metaheuristic algorithms. Statistical analysis using the Wilcoxon rank-sum test at the 0.05 significance level further confirms the consistency of the results. These findings demonstrate the potential of heterogeneous role-structured search for solving complex engineering optimization problems.