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

Abstract

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.

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

“…Many studies have optimized the local search stage of the Harris Hawk to accelerate the convergence speed of the Harris Hawk algorithm. Zhang et al [43] introduced the sine and cosine algorithm into the Harris Hawk algorithm, using the oscillating optimization process of the sine and cosine to accelerate the convergence speed of the Harris Hawk. This paper adds a nonlinear inertia weight factor to the local search process of the Harris Hawk algorithm, which originates from the acceleration of the particle swarm algorithm by the inertia weight factor in the inertia weight particle swarm algorithm.…”

Implementation of an Enhanced Crayfish Optimization Algorithm

“…They include advanced standard optimization algorithms: the particle swarm optimization algorithm (PSO), the Aquila Optimizer [ 31 ] (AO), the Beluga Optimization algorithm [ 32 ] (BWO), the golden jackal optimization algorithm [ 33 ] (GJO), and the crayfish optimization algorithm (COA). Recently, advanced optimization algorithms have been proposed: the sine-cosine chaotic Harris Eagle Optimization Algorithm [ 34 ] (CSCAHHO), the Adaptive slime fungus algorithm [ 35 ] (AOSMA), the mixed arithmetic-trigonometric optimization algorithm [ 36 ] (ATOA), and the Adaptive Gray Wolf Optimizer [ 37 ] (AGWO). These algorithms include the most widely used algorithms, recently proposed algorithms, and four highly advanced improved algorithms.…”