Optimal Synthesis of Linear Antenna Arrays Using Modified Spider Monkey Optimization

This paper presents a novel optimization technique named as modified spider monkey optimization (MSMO) for the synthesis of linear antenna array (LAA). The proposed method is inspired from a recently developed spider monkey optimization (SMO) swarm intelligent technique. The competitiveness of SMO has been already proved using numerical optimization functions. To improve the performance of SMO, a MSMO algorithm based on dual-search strategy is proposed in this paper. This approach generates a new solution using a search equation selected randomly from a candidate pool consisting of two search strategies. The performance of the proposed method is tested by applying it to find the optimal solutions for standard benchmark functions. Further, the capability and effectiveness is also proved by using it for practical optimization problem, i.e., synthesis of LAA for three different cases. Experimental results show that MSMO outperforms other popular algorithms like particle swarm optimization, cuckoo search, firefly algorithm, biogeography based optimization, differential evolution, tabu search and Taguchi method in terms of reduced side lobe level and faster convergence speed.

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