融合分解和自适应邻域的多目标离散组合优化算法OA北大核心CSTPCD

In order to efficiently obtain solutions for large-scale multi-objective optimization problems in reality,to achieve a balance among convergence,diversity,and uniformity has gradually become one of the important goals in multi-objective optimization.This paper proposes a multi-objective discrete combinatorial optimization algorithm combining problem-decomposition and adaptive large neighborhood search(MOALNS)for complex multi-objective dis-crete combinatorial optimization problems.The algorithm introduces large neighborhood search strategies and adap-tive adjustment mechanisms for the optimization process of each sub-problem based on problem decomposition,forming a new set of convergence-guiding criteria to break through optimization barriers and achieve a balance be-tween global and local search in the process of searching the multi-dimensional solution space for each sub-problem.In addition,this paper proposes that configuring independent operator integration libraries for each sub-problem can effectively adjust the optimization direction of each sub-problem,solving the problem of solution direction devia-tion caused by different objective weights,and thus achieving a more efficient and stable multi-objective optimiza-tion process.Numerical experiments demonstrate that the proposed new multi-objective discrete combinatorial opti-mization algorithm exhibits good performance in terms of convergence,diversity,uniformity,and extensibility in mul-tiple sets of standard benchmark test cases and case studies,and holds advantages compared with other classical multi-objective optimization algorithms.