A continuous-state cellular automata algorithm for global optimization

TL;DR

The paper introduces a novel continuous-state cellular automata algorithm (CCAA) that leverages diverse evolution rules to enhance global optimization by balancing exploration and exploitation, demonstrating superior performance on standard benchmarks and engineering problems.

Contribution

It presents the first direct use of cellular automata evolution rules in optimization, creating a flexible algorithm that adapts exploration and exploitation dynamically.

Findings

CCAA outperforms existing algorithms on 33 benchmark problems.

It effectively solves engineering applications and IIR filter design.

The algorithm's source code is publicly available for reproducibility.

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 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