CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems

• Published in: PloS one Vol. 17; no. 5; p. e0263387
• Main Authors: Zhang, Yu-Jun, Yan, Yu-Xin, Zhao, Juan, Gao, Zheng-Ming
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.05.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animals; Benchmarking; Benchmarks; Biology and Life Sciences; Birds; Computer Simulation; Convergence; Energy; Engineering and Technology; Evaluation; Experiments; Exploitation; Falconiformes; Global optimization; Humans; Hybridization; Mathematical analysis; Mathematical optimization; Mathematics; Optimization; Optimization algorithms; Physical Sciences; Problem Solving; R&D; Rabbits; Research & development; Research and Analysis Methods; Simulation; Trigonometric functions; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0263387

Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho .