Extended Grey Wolf Optimization Algorithm and Its Application for the Synthesis of Non-Uniformly Spaced Antenna Arrays

Abstract

Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. The algorithm is very competitive and has been applied to various fields of research, but has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative ability of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications. Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition based learning method has been used in the initial half of iterations to provide diversity among the search agents. This algorithm has been tested on nineteen standard benchmarking functions to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than grey wolf optimization (GWO), bat algorithm (BA), bat flower pollinator (BFP), differential evolution (DE), firefly algorithm (FA) and flower pollination algorithm (FPA). Statistical testing of GWO-E has been done to prove its significance over other compared algorithm. GWO-E is also tested on real world application of design of the non-uniformly spaced linear antenna array (NUSLA). Designing of antenna array is classical electromagnetic problem. This problem is highly complex and non-linear in nature. Hence, nature-inspired meta-heuristic algorithms are preferred for its synthesis. In this thesis, the NUSLA is designed with an aim to reduce the side lobe level (SLL) and placement of null in desired direction by controlling the inter-element spacing. Performance of GWO-E is evaluated by considering the several case studies of NUSLA that exists in literature and the results are compared with the results of other popular meta-heuristic algorithms. These results show the better performance of the proposed algorithm.