IEEE Transactions on Magnetics · 2014 · 17 citations · 10 references
ComputingComputer EngineeringElectrical EngineeringAlgorithmsOptimizationParallel AlgorithmsEvolutionary ComputationEvolutionary OptimizationGenetic AlgorithmsMetaheuristic OptimizationMulti-objective OptimizationMultiobjective OptimizationEvolutionary AlgorithmsLocal OptimaMulti-objective Evolutionary AlgorithmsMultimodal OptimizationParallel Evolutionary AlgorithmsParallel Evolutionary ComputationSelf-adaptive Evolutionary AlgorithmsSingle-objective Optimization
The self-adaptive low-high evaluation evolutionary-algorithm (SALHE-EA) is used to solve multimodal optimization problems. SALHE-EA is able to find the multiple optima of a single objective function (OF) and to give an idea of the fitness landscape in the neighborhood of these optima. This aspect is of crucial importance when the single OF is obtained using the weighted sum of the OFs, each related to a different goal of the optimization problem. This paper presents an improved version of SALHE-EA. This new version has several new features and, mainly, the suitability for parallelization.
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