A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

• Published in: Applied soft computing Vol. 34; pp. 526 - 538
• Main Authors: Luo, Jungang, Qi, Yutao, Xie, Jiancang, Zhang, Xiao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2015
• Subjects: Estimation of distribution algorithm; Hybrid algorithm; Multi-objective optimization; Particle swarm optimization algorithm; Reservoir flood control operation; Reservoir flood control operation; Multi-objective optimization; Hybrid algorithm; Estimation of distribution algorithm; Particle swarm optimization algorithm
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.05.036

•A hybrid multi-objective optimization algorithm is proposed.•It combines particle swarm optimization with estimation of distribution algorithm.•The algorithm is applied to solve reservoir flood control operation problem. Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RFCO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RFCO (MO-RFCO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO–EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RFCO problem. MO-PSO–EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO–EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO–EDA is suitable for solving MO-RFCO problems.