Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence
Alternative metrics PlumXhttp://hdl.handle.net/11012/57006
MetadataShow full item record
The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM). The hit rate and the quality of the Pareto front distribution are classified.
KeywordsMulti-objective optimization, binary genetic algorithm, particle swarm optimization, Pareto front, finite element method.
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2010, vol. 19, č. 3, s. 369-377. ISSN 1210-2512
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