TY - JOUR
T1 - A New Algorithm Inspired on Reversible Elementary Cellular Automata for Global Optimization
AU - Seck-Tuoh-Mora, Juan Carlos
AU - Lopez-Arias, Omar
AU - Hernandez-Romero, Norberto
AU - Martinez, Genaro J.
AU - Volpi-Leon, Valeria
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - This work presents a new global optimization algorithm of functions inspired by the dynamic behavior of reversible cellular automata, denominated Reversible Elementary Cellular Automata Algorithm (RECAA). This algorithm adapts the reversible evolution rules in elementary cellular automata (in one dimension and only with two states) to work with vectors of real values to realize optimization tasks. The originality of RECAA lies in adapting the dynamic of the reversible elementary cellular automata to perform exploration and exploitation actions in the optimization process. This work shows that diversity in cellular automata behaviors (in this case, reversibility) is useful to define new metaheuristics to solve optimization problems. The algorithm is compared with 15 recently published metaheuristics that recognized for their good performance, using 50 test functions in 30, 500, and with a fixed number of dimensions, and the CEC 2022 benchmark suit. Additionally, it is shown that RECAA has been applied in 3 engineering problems. In all the experiments, RECAA obtained satisfactory results. RECAA was implemented in MATLAB, and its source code can be consulted in GitHub. https://github.com/juanseck/RECAA
AB - This work presents a new global optimization algorithm of functions inspired by the dynamic behavior of reversible cellular automata, denominated Reversible Elementary Cellular Automata Algorithm (RECAA). This algorithm adapts the reversible evolution rules in elementary cellular automata (in one dimension and only with two states) to work with vectors of real values to realize optimization tasks. The originality of RECAA lies in adapting the dynamic of the reversible elementary cellular automata to perform exploration and exploitation actions in the optimization process. This work shows that diversity in cellular automata behaviors (in this case, reversibility) is useful to define new metaheuristics to solve optimization problems. The algorithm is compared with 15 recently published metaheuristics that recognized for their good performance, using 50 test functions in 30, 500, and with a fixed number of dimensions, and the CEC 2022 benchmark suit. Additionally, it is shown that RECAA has been applied in 3 engineering problems. In all the experiments, RECAA obtained satisfactory results. RECAA was implemented in MATLAB, and its source code can be consulted in GitHub. https://github.com/juanseck/RECAA
KW - Engineering applications
KW - global optimization
KW - metaheuristics
KW - reversible cellular automata
UR - http://www.scopus.com/inward/record.url?scp=85140749501&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3216321
DO - 10.1109/ACCESS.2022.3216321
M3 - Artículo
AN - SCOPUS:85140749501
SN - 2169-3536
VL - 10
SP - 112211
EP - 112229
JO - IEEE Access
JF - IEEE Access
ER -