Surrogate-assisted evolutionary algorithms have become the mainstream approach for solving expensive constrained multi-objective optimization problems (ECMOPs). However, existing methods suffer from blind search issues, and their selection strategies fail to adapt to changes in evolutionary stages. To overcome these limitations, this paper proposes a Multi-directional Guided Dual-mode Kriging-assisted Competitive Particle Swarm Optimization (MGD-KCSO) algorithm. MGD-KCSO integrates three synergistic strategies: a multi-directional guided solution strategy that constructs four complementary search paths based on non-dominated solutions to effectively enhance convergence and diversity; a dual-population data selection strategy that separates unconstrained and constrained populations to perform objective-oriented and constraint-oriented optimization, respectively; and an adaptive infill sampling strategy that dynamically switches sampling modes by monitoring the change rate of the objective function of the ideal point. If this rate exceeds a predefined threshold, the algorithm executes unconstrained sampling to accelerate convergence; otherwise, it switches to constrained sampling to prioritize the exploration of feasible boundaries. To verify the effectiveness of MGD-KCSO, comprehensive experiments were conducted on 33 benchmark problems and two real-world engineering design problems (pressure vessel and disc brake design). MGD-KCSO was compared against eight classic algorithms and three state-of-the-art methods published in the past two years. Experimental results evaluated by inverted generational distance (IGD) and hypervolume (HV) metrics demonstrate that MGD-KCSO outperforms the comparative algorithms on most test instances, achieving superior performance in terms of convergence, diversity, and practical applicability.
Huang et al. (Tue,) studied this question.