Abstract
Water resources management presents a large variety of multiobjective problems that require powerful optimization tools in order to fully characterize the existing trade-offs. Different optimization methods, based on mathematical programming at first and on evolutionary computation more recently, have been applied with various degrees of success. This paper explores the use of a relatively recent heuristic technique called particle swarm optimization (PSO), which has been found to perform very well in a wide spectrum of optimization problems. Many extensions of the single-objective PSO to handle multiple objectives have been proposed in the evolutionary computation literature. This paper presents an implementation of multiobjective particle swarm optimization (MOPSO) that evaluates alternative solutions based on Pareto dominance, using an external repository to store nondominated solutions, a fitness sharing approach to promote diversity, and a mutation operator to improve global search. The MOPSO solver ...
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