Hybrid Artificial Bee Colony and JAYA Algorithm for Linear Antenna Array Synthesis

Abstract

Artificial bee colony (ABC) is a very popular algorithm and has been widely used in the literature. The algorithm has some inherent drawbacks, including slow convergence, and poor exploration versus exploitation, among others. In order to deal with these problems, we are hybridizing the ABC algorithm with the JAYA algorithm. The proposed algorithm has the added properties of both ABC and JAYA and has been named as JABC algorithm. The key idea is to add prospective equations of JAYA into ABC for better exploitation, and adding new mutation operators for enhanced exploration and better convergence. The proposed algorithm is tested on CEC 2005 benchmark problems and real world synthesis of linear antenna array (LAA). SLL reduction in five different LAA’s is done, for position, amplitude, and phase optimization. We use 10-element, 24-element, 28 element and 40-element LLA for optimization. For performance evaluation, the algorithm is tested with respect to basic ABC, JAYA, spider monkey optimization (SMO), Moth flame optimization (MFO), Chameleon Swarm Algorithm (CSA) and other algorithms. Experimental results on both benchmarks and LAA problems, show that JABC is significantly better as compared to others. Also, statistical analysis using Wilcoxon’s test and Friedman tests shows the superior performance of JABC algorithm with respect to other algorithms.