论文标题
基于共识的多目标问题的优化：一种多重方法
Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach
论文作者
论文摘要
我们提出了一种基于基于共识的优化方法（CBO）的一般多目标优化问题的帕累托阵线的多种方法。该算法是从基于固定标量重量的CBO开始的简单扩展开始的逐步动机。为了克服选择权重的问题，我们在第二个建模步骤中提出了一种自适应重量策略。建模过程结论是结合了一种惩罚策略，该策略避免了沿帕累托前沿的聚类，并避免了防止群体崩溃的扩散项。总体而言，提出的$ K $ -SWARM CBO算法是针对帕累托前部的各种近似而定制的，同时是一系列一般的非凸线多目标问题。该方法的可行性是通过分析结果（包括收敛的证明）和与众所周知的非主导分类遗传算法（NSGA2）的性能进行比较证明的。
We propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the Consensus-based Optimization method (CBO). The algorithm is motivated step by step beginning with a simple extension of CBO based on fixed scalarization weights. To overcome the issue of choosing the weights we propose an adaptive weight strategy in the second modelling step. The modelling process is concluded with the incorporation of a penalty strategy that avoids clusters along the Pareto front and a diffusion term that prevents collapsing swarms. Altogether the proposed $K$-swarm CBO algorithm is tailored for a diverse approximation of the Pareto front and, simultaneously, the efficient set of general non-convex multi-objective problems. The feasibility of the approach is justified by analytic results, including convergence proofs, and a performance comparison to the well-known non-dominated sorting genetic algorithm (NSGA2) and the recently proposed one-swarm approach for multi-objective problems involving Consensus-based Optimization.
