A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation
•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 optimiza...
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| Published in | Applied soft computing Vol. 34; pp. 526 - 538 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1568-4946 1872-9681 |
| DOI | 10.1016/j.asoc.2015.05.036 |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Zhang, Xiao Luo, Jungang Xie, Jiancang Qi, Yutao |
| Author_xml | – sequence: 1 givenname: Jungang surname: Luo fullname: Luo, Jungang email: jgluo@xaut.edu.cn organization: State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, China – sequence: 2 givenname: Yutao surname: Qi fullname: Qi, Yutao email: ytqi@xidian.edu.cn organization: School of Computer Science and Technology, Xidian University, Xi’an, China – sequence: 3 givenname: Jiancang surname: Xie fullname: Xie, Jiancang email: jcxie@xaut.edu.cn organization: State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, China – sequence: 4 givenname: Xiao surname: Zhang fullname: Zhang, Xiao organization: Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an, China |
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| Keywords | Reservoir flood control operation Multi-objective optimization Hybrid algorithm Estimation of distribution algorithm Particle swarm optimization algorithm |
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| SubjectTerms | Estimation of distribution algorithm Hybrid algorithm Multi-objective optimization Particle swarm optimization algorithm Reservoir flood control operation |
| Title | A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation |
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