School of Engineering and Built Environment, Griffith University, Gold Coast, Queensland, Australia.
Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran.
Environ Monit Assess. 2020 Apr 13;192(5):281. doi: 10.1007/s10661-020-8228-z.
Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.
粒子群优化（PSO）是一种受社会群体中个体相互作用启发的随机基于群体的优化算法。该算法广泛应用于水资源问题的不同领域。本文全面概述了基本 PSO 算法搜索策略以及 PSO 在水资源工程优化问题中的应用和性能分析。我们的文献综述揭示了 22 种不同类型的 PSO 算法。本文突出了每种 PSO 算法的特点及其在水资源工程不同领域（例如水库运行、降雨径流建模、水质建模和地下水建模）中的应用。还将不同 PSO 变体的性能与其他进化算法（EAs）和数学优化方法进行了比较。该综述从最优 Pareto 前沿的正确收敛、更快的收敛速度以及计算解决方案的多样性等方面评估了 PSO 变体相对于传统 EAs（例如模拟退火、差分进化、遗传算法和鲨鱼算法）和数学方法（例如支持向量机和微分动态规划）的能力和比较性能。
