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
翻译:细胞自动数据能够根据各元素之间简单的局部相互作用发展复杂的行为。这些特征中有一些被用来提出和改进元湿度学,以便进行全球优化;然而,细胞自动数据中的演进规则所提供的特性尚未直接用于优化任务。受细胞自动数据的各种演进规则的启发,提出了连续状态细胞自动数据算法(CAA),从而利用不同的演进规则来保持平衡,使每个循环中的勘探和开发特性最大化。CAA的效率得到了证明,文献中广泛使用了33个测试问题,最近文献中也使用了4个工程应用,并设计了适应性无孔反应过滤器,测试了10个全序 IIR参考功能。数字结果证明了它与州级算法相比的竞争力。CCA的来源代码在https://github.com/juanseck/CAAA.git上公开提供。