A continuous-state cellular automata algorithm for global optimization

Abstract

Cellular automata are capable of developing complex behaviors based on simple local interactions between their elements. Some of these characteristics have been used to propose and improve metaheuristics for global optimization; however, the properties offered by the evolution rules in cellular automata have not yet been used directly in optimization tasks. Inspired by the complexity that various evolution rules of cellular automata can offer, the continuous-state cellular automata algorithm is proposed. In this way, the algorithm takes advantage of different evolution rules to maintain a balance that maximizes the exploration and exploitation properties in each iteration. The efficiency of the algorithm is proven with 48 test problems widely used in the literature, 4 engineering applications that were also used in recent literature, and the design of adaptive infinite-impulse response filters, with the reference functions of 10 full-order filters being tested. The numerical results prove its competitiveness in comparison with state-of-the-art algorithms. The source codes of the proposed algorithm are publicly available at https://github.com/juanseck/CCAA.git.