Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
The measure of Pareto optima applications to multi-objective metaheuristics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Optimal design of water distribution system by multiobjective evolutionary methods
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Articulating user preferences in many-objective problems by sampling the weighted hypervolume
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
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Purpose of the paper is to introduce a methodology for a parameter-free multi-criterion optimization of water distribution networks. It is based on a two-level approach, with a population of inner multi-objective genetic algorithms (MOGAs) and an outer simple GA (without crossover). The inner MOGAs represent the network optimizers, while the outer GA – the meta GA – is a supervisor process adapting mutation and crossover probabilities of the inner MOGAs. The hypervolume metric has been adopted as fitness for the individuals at the meta-level. The methodology has been applied to a small system often studied in the literature, for which an exhaustive search of the entire decision space has allowed the determination of all Pareto-optimal solutions of interest: the choice of this simple system was done in order to compare the hypervolume metric to two performance measures (a convergence and a sparsity index) introduced on purpose. Simulations carried out show how the proposed procedure proves robust, giving better results than a MOGA alone, thus allowing a considerable ease in the network optimization process.