A novel hybrid invasive weed optimization algorithm for pattern synthesis of array antennas

By introducing novel strategies in Invasive Weed Optimization IWO, a hybrid algorithm called IWO-simplified quadratic approximation SQA is proposed, in which an adaptive standard deviation is designed to improve the convergence performances of the original IWO, and SQA is embedded into IWO as a local search operator to enhance the overall search capability of the algorithm. Simulated results for six benchmark functions show that the proposed algorithm performs better than the original IWO algorithm. In addition, the proposed algorithm is used to the pattern synthesis of array antennas. Compared to the genetic algorithm GA and particle swarm optimization PSO, the advantages of IWO-SQA algorithm are shown. As another application, the phase-only pattern reconfigurable arrays are synthesized by IWO-SQA algorithm, and the numerical results show that IWO-SQA algorithm is superior to GA. All the testing results show that it is an effective improvement to embed SQA into IWO algorithm. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:154-163, 2015.

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