The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front. Considering this situation, we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front. An evolutionary population and an external archive are used in the search process, and information extracted from the external archive is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems, and all the subproblems are optimized in a collaborative manner. The external archive is updated with the method of shift-based density estimation. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with irregular Pareto front. Experimental results show that the proposed algorithm out-performs these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach, there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.

多目标优化问题已在许多领域遇到，例如高速火车头形设计，重叠社区检测，电力分配和无人机形成。为了解决这样的问题，当前的方法主要集中于规则的帕累托前沿问题，而不是解决不规则的帕累托前沿问题。考虑到这种情况，我们提出了一种基于分解和动态资源分配的多目标进化算法（MaOEA / D-DRA），用于不规则优化。所提出的算法可以根据问题的Pareto前沿的不同形状，动态地将计算资源分配给不同的搜索区域。搜索过程中使用了进化种群和外部档案，从外部档案库中提取的信息将用于指导进化种群进入不同的搜索区域。进化种群使用Tchebycheff方法进化，将一个问题分解为几个子问题，并且所有子问题都以协作方式进行了优化。外部档案库使用基于位移的密度估计方法进行更新。将所提出的算法与使用具有不规则Pareto前沿的各种测试问题的五个最新的多目标进化算法进行比较。实验结果表明，该算法在收敛速度和种群成员多样性方面均优于这五种算法。与加权和法和基于惩罚的边界相交法相比，