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

Yu-Jun Zhang1, Yu-Xin Yan2, Juan Zhao1

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|May 19, 2022
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Summary
This summary is machine-generated.

A new hybrid optimization algorithm, CSCAHHO, combines Sine Cosine Algorithm (SCA) and Harris Hawk Optimization (HHO) to enhance performance. This novel approach demonstrates superior convergence and accuracy on benchmark and engineering problems.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The No Free Lunch (NFL) theorem necessitates continuous development of novel optimization algorithms.
  • Existing algorithms like Sine Cosine Algorithm (SCA) and Harris Hawk Optimization (HHO) have distinct strengths in convergence and speed.
  • Hybridization offers a promising avenue to leverage and enhance these individual algorithm capabilities.

Purpose of the Study:

  • To propose a novel hybrid optimization algorithm, CSCAHHO, by integrating SCA and HHO.
  • To enhance the exploration-exploitation balance and randomness in optimization through an energy parameter and chaos theory.
  • To validate the efficacy of the proposed CSCAHHO algorithm against existing state-of-the-art methods.

Main Methods:

  • Hybridization of Sine Cosine Algorithm (SCA) and Harris Hawk Optimization (HHO) into the CSCAHHO algorithm.
  • Introduction of an energy parameter to balance exploration and exploitation.
  • Incorporation of chaos theory to improve population randomness.
  • Redefinition of updating equations by combining SCA and HHO principles.

Main Results:

  • The CSCAHHO algorithm demonstrated superior performance across 27 benchmark functions, CEC 2014 functions, and 3 engineering problems.
  • Comparative analysis showed CSCAHHO outperformed original SCA, HHO, and other algorithms like AOA, SOA, STOA, AO, and ChOA.
  • Key performance indicators included faster convergence rates, reduced residual errors, and improved stability.

Conclusions:

  • The proposed CSCAHHO algorithm effectively overcomes limitations of individual algorithms, offering enhanced optimization capabilities.
  • The hybridization strategy, incorporating an energy parameter and chaos, significantly improves convergence speed and solution accuracy.
  • CSCAHHO presents a robust and efficient metaheuristic for complex optimization tasks, with Matlab code available for reproducibility.