Cock-hen-chicken Optimizer: A Nature-inspired Algorithm for Real-world Engineering Optimization
Keywords:
Nature-inspired algorithms, Global optimization, Swarm intelligence, Benchmark functions, Real- world engineering problems, Simulation experiments, Evolutionary computationAbstract
Nature-inspired algorithms have been a hot spot and proved to be a successive way to handle optimization problems. Due to the No Free Lunch (NFL) theorem, all of the algorithms might fail to solve some of the problems and consequently need to be improved. In order to find a better and efficient way to solve the real-world engineering problems, an algorithm called the Cock-Hen-Chicken (CHC) optimizer was proposed with the inspiration of hunting behaviors of the cocks, hens, and chickens. Simulation experiments on either unimodal, multimodal, IEEE Congress on Evolutionary Computation 2017 (CEC17), or CEC2011 competitive problems were carried out and finally, it was applied to solve five real-world engineering problems. Most of the simulation results except for the CEC17 confirmed the better performance, superiority, and capability of the proposed CHC optimizer comparing with other well-known optimization algorithms such as the ant lion optimizer (ALO), the equilibrium optimizer (EO), the grey wolf optimizer (GWO), the mayfly optimization algorithm (MOA), the particle swarm optimization (PSO), the sine-cosine algorithm (SCA), and the whale optimization algorithm (WOA). Results of real-world engineering problems were also promising. The proposed CHC optimizer reported in this paper would be a better choice for future applications and the code is shared with https://github.com/gaozming/CHCoptimizer for possible future efforts.
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