Multi-objective problems with two or more conflicting objectives are very common in every engineering fields, also for antenna optimization. Evolutionary optimization Algorithms are important tools due to their effectiveness, flexibility and applicability especially for multi-objective problems because they can provide directly the non-dominated set. Among Evolutionary Algorithms, Social Network optimization (SNO) shows very good optimization performance.In this paper three different approaches for solving a multiobjective problem are tested with SNO: the first one is the weighted sum method, the second is the epsilon-constrained method and the third one is the simultaneous search with a multiobjective implementation of SNO. The analysed application is the design of a sparse-array antenna.

Different Approaches to Multi-Objective Sparse Array Problem with Social Network optimization

Niccolai A.;Grimaccia F.;Mussetta M.;Zich R.
2020

Abstract

Multi-objective problems with two or more conflicting objectives are very common in every engineering fields, also for antenna optimization. Evolutionary optimization Algorithms are important tools due to their effectiveness, flexibility and applicability especially for multi-objective problems because they can provide directly the non-dominated set. Among Evolutionary Algorithms, Social Network optimization (SNO) shows very good optimization performance.In this paper three different approaches for solving a multiobjective problem are tested with SNO: the first one is the weighted sum method, the second is the epsilon-constrained method and the third one is the simultaneous search with a multiobjective implementation of SNO. The analysed application is the design of a sparse-array antenna.
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
978-1-7281-6929-3
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1152290
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