A continuous-state cellular automata algorithm for global optimization
This work addresses global optimization problems for researchers and practitioners, offering a novel meta-heuristic with incremental improvements in performance.
The authors tackled global optimization by proposing a continuous-state cellular automata algorithm (CCAA) that uses evolution rules to balance exploration and exploitation, achieving competitive results on 33 test problems, 4 engineering applications, and 10 IIR filter designs compared to state-of-the-art algorithms.
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 meta-heuristics 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 (CCAA) is proposed. In this way, the CCAA takes advantage of different evolution rules to maintain a balance that maximizes the exploration and exploitation properties in each iteration. The efficiency of the CCAA is proven with 33 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 (IIR) filters, testing 10 full-order IIR reference functions. The numerical results prove its competitiveness in comparison with state-of-the-art algorithms. The source codes of the CCAA are publicly available at https://github.com/juanseck/CCAA.git