A Memetic Multi-objective Immune Algorithm for Reservoir Flood Control Operation

Yutao Qi (), Liang Bao, Yingying Sun, Jungang Luo and Qiguang Miao
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Yutao Qi: Xidian University
Liang Bao: Xidian University
Yingying Sun: Xidian University
Jungang Luo: Xi’an University of Technology
Qiguang Miao: Xidian University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 9, No 5, 2957-2977

Abstract: Abstract Reservoir flood control operation (RFCO) is a challenging optimization problem with multiple conflicting decision goals and interdependent decision variables. With the rapid development of multi-objective optimization techniques in recent years, more and more research efforts have been devoted to optimize the conflicting decision goals in RFCO problems simultaneously. However, most of these research works simply employ some existing multi-objective optimization algorithms for solving RFCO problem, few of them considers the characteristics of the RFCO problem itself. In this work, we consider the complexity of the RFCO problem in both objective space and decision space, and develop an immune inspired memetic algorithm, named M-NNIA2, to solve the multi-objective RFCO problem. In the proposed M-NNIA2, a Pareto dominance based local search operator and a differential evolution inspired local search operator are designed for the RFCO problem to guide the search towards the and along the Pareto set respectively. On the basis of inheriting the good diversity preserving in immune inspired optimization algorithm, M-NNIA2 can obtain a representative set of best trade-off scheduling plans that covers the whole Pareto front of the RFCO problem in the objective space. Experimental studies on benchmark problems and RFCO problem instances have illustrated the superiority of the proposed algorithm.

Keywords: Multi-objective optimization; Artificial immune algorithm; Memetic algorithm; Reservoir flood control operation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-016-1317-7

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