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In this paper, we propose an improved nondominated sorting genetic algorithm-II with scope constrained (INSGA-II/SC) with three modifications, which are dynamic nondomination strategy, scope-constrained strategy, and front uniformly distributed strategy. Here, the metric for multiobjective optimization mainly focuses on the computation complexity, convergence, and diversity of the final solutions. For a large search space in the initial process and a fast convergence in the last process, dynamic nondomination factor is considered in the rank operator. We can find a manageable number of Pareto solutions that are in the constrained scope instead of the entire Pareto front (PF) to reduce the computation complexity by scope-constrained strategy. In order to obtain a high performance for good representatives of the entire PF, the solutions closer to the uniformly distributed points on the current front will be chosen. In this paper, the proposed methods and two efficient multiobjective optimization methods are used for the optimization of mathematical problems and array pattern synthesis with lower side lobe level (SLL) and null. Numerical examples show that INSGA-II/SC has a high performance of diversity and convergence for the final solutions when compared with the other techniques published in the literature.

Linear Array Pattern Synthesis Using An Improved Multiobjective Genetic Algorithm