Abstract-In this paper, we propose an optimization method based on real-coded genetic algorithm (GA) with elitist strategy for thinning a large linear array of uniformly excited isotropic antennas to yield the maximum relative sidelobe level (SLL) equal to or below a fixed level. The percentage of thinning is always kept equal to or above a fixed value. Two examples have been proposed and solved with different objectives and with different value of percentage of thinning that will produce nearly the same sidelobe level. Directivities of the thinned arrays are found out and simulation results of different problems are also compared with published results to illustrate the effectiveness of the proposed method.

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In this paper, a new hybrid metaheuristic learning algorithm is introduced to choose the best parameters for the multi-criteria decision making algorithm (MCDA) called PROAFTN. PROAFTN requires values of several parameters to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attribute. The proposed learning algorithm, identified as PSOPRO-RVNS as it integrates particle swarm optimization (PSO) and Reduced Variable Neighborhood Search (RVNS), is used to automatically determine all PROAFTN parameters. The combination of PSO with RVNS allows to improve the exploration and exploitation capabilities of PSO by setting some search points to be iteratively re-exploited using RVNS. Based on the generated results, experimental evaluations show that PSOPRO-RVNS outperforms six well-known machine learning classifiers in a variety of problems.