Ji Chen, Wu Linjie, Zhao Tianhao, Cai Xingjuan
Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, ShanXi, China.
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China.
PeerJ Comput Sci. 2024 Jul 22;10:e2102. doi: 10.7717/peerj-cs.2102. eCollection 2024.
Constrained many-objective optimization problems (CMaOPs) have gradually emerged in various areas and are significant for this field. These problems often involve intricate Pareto frontiers (PFs) that are both refined and uneven, thereby making their resolution difficult and challenging. Traditional algorithms tend to over prioritize convergence, leading to premature convergence of the decision variables, which greatly reduces the possibility of finding the constrained Pareto frontiers (CPFs). This results in poor overall performance. To tackle this challenge, our solution involves a novel dual-population constrained many-objective evolutionary algorithm based on reference point and angle easing strategy (dCMaOEA-RAE). It relies on a relaxed selection strategy utilizing reference points and angles to facilitate cooperation between dual populations by retaining solutions that may currently perform poorly but contribute positively to the overall optimization process. We are able to guide the population to move to the optimal feasible solution region in a timely manner in order to obtain a series of superior solutions can be obtained. Our proposed algorithm's competitiveness across all three evaluation indicators was demonstrated through experimental results conducted on 77 test problems. Comparisons with ten other cutting-edge algorithms further validated its efficacy.
约束多目标优化问题（CMaOPs）已在各个领域逐渐出现，并且对该领域具有重要意义。这些问题通常涉及复杂的帕累托前沿（PFs），这些前沿既精细又不均匀，从而使其求解困难且具有挑战性。传统算法往往过度优先考虑收敛性，导致决策变量过早收敛，这大大降低了找到约束帕累托前沿（CPFs）的可能性。这导致整体性能不佳。为应对这一挑战，我们的解决方案涉及一种基于参考点和角度缓和策略的新型双种群约束多目标进化算法（dCMaOEA - RAE）。它依赖于一种宽松的选择策略，利用参考点和角度，通过保留当前可能表现不佳但对整体优化过程有积极贡献的解来促进双种群之间的合作。我们能够引导种群及时移动到最优可行解区域，以便获得一系列优越的解。通过对77个测试问题进行的实验结果证明了我们提出的算法在所有三个评估指标上的竞争力。与其他十种前沿算法的比较进一步验证了其有效性。
