Multi Objective Optimization with a New Evolutionary Algorithm

Samaneh Seifollahi-Aghmiuni () and Omid Bozorg Haddad ()
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Samaneh Seifollahi-Aghmiuni: Stockholm University
Omid Bozorg Haddad: University of Tehran

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 12, No 13, 4013-4030

Abstract: Abstract Various objectives are mainly met through decision making in real world. Achieving desirable condition for all objectives simultaneously is a necessity for conflicting objectives. This concept is called multi objective optimization widely used nowadays. In this study, a new algorithm, comprehensive evolutionary algorithm (CEA), is developed based on general concepts of evolutionary algorithms that can be applied for single or multi objective problems with a fixed structure. CEA is validated through solving several mathematical multi objective problems and the obtained results are compared with the results of the non-dominated sorting genetic algorithm II (NSGA-II). Also, CEA is applied for solving a reservoir operation management problem. Comparisons show that CEA has a desirable performance in multi objective problems. The decision space is accurately assessed by CEA in considered problems and the obtained solutions’ set has a great extent in the objective space of each problem. Also, CEA obtains more number of solutions on the Pareto than NSGA-II for each considered problem. Although the total run time of CEA is longer than NSGA-II, solution set obtained by CEA is about 32, 4.4 and 1.6% closer to the optimum results in comparison with NSGA-II in the first, second and third mathematical problem, respectively. It shows the high reliability of CEA’s results in solving multi objective problems.

Keywords: Evolutionary algorithm; Multi colony; Multi objective; Optimization; Pareto (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-018-2034-1

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