Kriging Surrogate Model-Based Constraint Multiobjective Particle Swarm Optimization Algorithm
针对高维复杂约束多目标优化问题中搜索区域不规则、可行解分布不均的挑战，提出一种基于克里金代理模型的约束多目标粒子群优化算法，通过设计两种新型交叉算子来提升搜索效率和收敛速度。
The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method.