Biogeography-based optimization with adaptive migration and adaptive mutation with its application in sidelobe reduction of antenna arrays

Abstract

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems.