Document Type: Regular Article
Irrigation &amp; Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Structural Engineering Dept., Fac. of Engrg., Mansoura Univ., Mansoura 35516, Egypt
Groundwater is one of the important sources of freshwater and accordingly, there is a need for optimizing its usage. In this paper, four multi-objective metaheuristic algorithms with new evolution strategy are introduced and compared for the optimal management of groundwater namely: Multi-objective genetic algorithms (MOGA), multi-objective memetic algorithms (MOMA), multi-objective particle swarm optimization (MOPSO), and multi-objective shuffled frog leaping algorithm (MOSFLA). The suggested evolution process is based on determining a unique solution of the Pareto solutions called the Pareto-compromise (PC) solution. The advantages of the current development stem from: 1) The new multiple objectives evolution strategy is inspired from the single objective optimization, where fitness calculations depend on tracking the PC solution only through the search history; 2) a comparison among the performance of the four algorithms is introduced. The development of each algorithm is briefly presented. A comparison study is carried out among the formulation and the results of the four algorithms. The developed four algorithms are tested on two multiple-objective optimization benchmark problems. The four algorithms are then used to optimize two-objective groundwater management problem. The results prove the ability of the developed algorithms to accurately find the Pareto-optimal solutions and thus the potential application on real-life groundwater management problems.