School of Engineering, University of Guelph, Guelph, ON, Canada.
Evol Comput. 2010 Spring;18(1):127-56. doi: 10.1162/evco.2010.18.1.18105.
This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
本文提出了一种有效的粒子群优化（PSO）技术，可用于处理多目标优化问题。它基于最初用于进化算法（EA）的强度 Pareto 方法。所提出的改进粒子群算法用于构建三种混合 EA-PSO 算法来解决不同的多目标优化问题。该算法及其混合形式使用文献中的七个基准进行了测试，并使用多种指标与强度 Pareto 进化算法（SPEA2）和具有竞争力的多目标 PSO 进行了比较。与其他算法相比，所提出的算法的收敛速度较慢，但所需的 CPU 时间较少。将 PSO 和进化算法相结合可以得到优于 SPEA2、具有竞争力的多目标 PSO（MO-PSO）和基于不同指标的基于强度 Pareto 的 PSO 的混合算法。
