Synthesis of unequally-spaced linear array using modified central force optimisation algorithm

摘要

In this study, a modified version of the central force optimisation (MCFO) algorithm is presented. This modified version is based on the idea of combining the ability of social thinking in particle swarm optimisation (PSO) with the search capability of the original CFO, along with the addition of time-varying acceleration coefficients, to effectively control the global search and enhance the CFO convergence capability. The convergence capability of the MCFO approach is compared with that of other recent evolutionary-based algorithms, using 12 benchmark functions grouped into unimodal and multimodal functions. Furthermore, the MCFO algorithm is considered for the synthesis of unequally-spaced linear arrays with minimum sidelobe levels and/or null placement in certain directions as well as a specified maximum null-to-null beamwidth. The comparison of the simulations of different algorithms shows that the MCFO technique is superior to other evolutionary algorithms such as the genetic algorithm, ant colony optimisation, PSO algorithm, and gravitational search algorithm, as well as other improved CFO algorithms.