Exploring the Effects of Constraints on Multiobjective Evolutionary Algorithm Performance in Water Resources.

摘要

This study explores how user-defined constraints affect the effectiveness, efficiency and consistency of multi-objective evolutionary algorithm (MOEA) optimization in water resources. Constraints in MOEA optimization commonly represent limits on acceptable performance, but their effect on MOEA performance has not been extensively researched. The study considers two water resources case studies: a water supply portfolio planning model and an economic development and environmental water quality model. These models are optimized in two different cases: one with constraints -- as the models were originally formulated -- and one without constraints. The original set of constraints are then applied a posteriori to all solutions, representing a typical decision making process. For each model, the effectiveness and efficiency of search on the constrained and unconstrained problems are compared. Results show that constraints aid in the search process by favoring selection of solutions that meet the decision maker's preferences, resulting in more solutions for the decision maker to consider. Although in some cases constraints can make a problem harder to solve, they allow the search process to more efficiently and effectively discover acceptable solutions.