Multiconstraint Evolutionary Algorithm Based on Decomposition for the Synthesis of Concentric Ring Sparse Array

Le résumé fourni par la source

For the local convergence phenomenon in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) when applied to multiconstraint sparse array optimization, a multiconstraint evolutionary algorithm based on decomposition (MCEA/D) is proposed in this communication. Unlike MOEA/D, which constructs subproblems through weighted aggregation of multiple objectives, MCEA/D converts objective functions into constraints while retaining a single objective function, generating subproblems via constraint value space decomposition. Consequently, MCEA/D performs optimization within different constraint subspaces, with each subproblem operating in its own search space. This novel approach effectively mitigates the diversity loss and premature convergence issues of MOEA/D caused by the shared search space among subproblems. The results of uniformly excited concentric ring sparse array optimization demonstrate that MCEA/D can successfully identify feasible Pareto fronts, outperforming MOEA/D algorithm in this capability. Electromagnetic simulations validate the results.

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