AI 中文总结
针对结构优化问题收敛难、计算成本高的挑战，提出双种群约束多目标进化算法DPCME，通过种群交互和基于修复的约束处理技术，在多个工程问题测试中展现出优越或有竞争力的收敛性与多样性。
AI 中文摘要
结构优化问题常包含大量决策变量和高度非凸可行域，收敛到真实帕累托前沿极具挑战且计算成本高。本研究引入新型约束多目标进化算法DPCME，它采用两个交互种群交换信息，有效进行全局探索并降低收敛到局部最优的风险。还结合基于修复的约束处理技术并提出替代修复方法。该算法在三个工程问题上测试，与最新PlatEMO包中的先进算法对比，结果表明DPCME在所有测试案例中收敛性和多样性优越或具竞争力，基于修复的约束处理进一步提升了性能。
英文摘要
Structural optimization problems often involve a large number of decision variables and highly non-convex feasible regions, making convergence to the true Pareto front extremely challenging. Even when convergence is achievable, it typically requires thousands of function evaluations, resulting in significant computational cost. This highlights the need for efficient and robust optimization algorithms for real-world engineering applications. In this study, we introduce a novel constrained multi-objective evolutionary algorithm, termed DPCME. The algorithm employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima. To further enhance performance, a recent repair-based constraint-handling technique is incorporated, and alternative repair approaches are proposed and systematically evaluated. The proposed algorithm is tested on three engineering problems: the 72-bar truss, the 120-bar truss, and a chemical tanker structure, each involving hundreds of nonlinear failure constraints. Its performance is evaluated against state-of-the-art constrained multi-objective optimization algorithms from the latest PlatEMO package. A total of 43 algorithms are initially tested, from which the 12 best-performing methods are selected for detailed comparison. The results demonstrate that DPCME achieves superior or competitive convergence and diversity across all test cases, and that the inclusion of repair-based constraint handling further improves its performance.
Comments31 pages, 12 figures, 5 tables
