TY - JOUR ID - 64764 TI - Developing Four Metaheuristic Algorithms for Multiple-Objective Management of Groundwater JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - El-Ghandour, Hamdy Ahmed AU - Elbeltagi, Emad AD - Associate Professor, Irrigation & Hydraulics Department, Faculty of Engineering Mansoura University, Mansoura 35516, Egypt AD - Professor, Structural Engineering Department, Faculty of Engineering Mansoura University, Mansoura 35516, Egypt Y1 - 2018 PY - 2018 VL - 2 IS - 4 SP - 1 EP - 22 KW - Genetic Algorithms KW - Memetic algorithms KW - Particle swarm KW - Shuffled frog leaping KW - Compromise solution KW - Multiple objectives optimization DO - 10.22115/scce.2018.128344.1057 N2 - 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. UR - https://www.jsoftcivil.com/article_64764.html L1 - https://www.jsoftcivil.com/article_64764_ef703d1195151f57ae64cd2e327790b5.pdf ER -