Enhanced crayfish optimization algorithm with differential evolution’s mutation and crossover strategies for global optimization and engineering applications DOI Creative Commons
Biswajit Maiti, Saptadeep Biswas, Absalom E. Ezugwu

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 6, 2025

Abstract Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces novel hybrid optimization algorithm, the Hybrid Crayfish Algorithm with Differential Evolution (HCOADE), which addresses limitations of premature convergence inadequate exploitation traditional (COA). By integrating COA (DE) strategies, HCOADE leverages DE’s mutation crossover mechanisms to enhance global performance. The COA, inspired by foraging social behaviors crayfish, provides flexible framework for exploring solution space, while robust strategies effectively exploit this space. To evaluate HCOADE’s performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 2017, as well six engineering design problems. results compared ten leading algorithms, classical Particle Swarm (PSO), Grey Wolf Optimizer (GWO), Whale (WOA), Moth-flame (MFO), Salp (SSA), Reptile Search (RSA), Sine Cosine (SCA), Constriction Coefficient-Based Gravitational (CPSOGSA), Biogeography-based (BBO). average rankings Wilcoxon Rank Sum Test provide comprehensive comparison clearly demonstrating its superiority. Furthermore, performance is assessed on 2020 2022 test suites, further confirming effectiveness. A comparative analysis against notable winners competitions, LSHADEcnEpSin, LSHADESPACMA, CMA-ES, CEC-2017 suite, revealed superior HCOADE. underscores advantages DE offers valuable insights addressing

Language: Английский

Enhanced crayfish optimization algorithm with differential evolution’s mutation and crossover strategies for global optimization and engineering applications DOI Creative Commons
Biswajit Maiti, Saptadeep Biswas, Absalom E. Ezugwu

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 6, 2025

Abstract Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces novel hybrid optimization algorithm, the Hybrid Crayfish Algorithm with Differential Evolution (HCOADE), which addresses limitations of premature convergence inadequate exploitation traditional (COA). By integrating COA (DE) strategies, HCOADE leverages DE’s mutation crossover mechanisms to enhance global performance. The COA, inspired by foraging social behaviors crayfish, provides flexible framework for exploring solution space, while robust strategies effectively exploit this space. To evaluate HCOADE’s performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 2017, as well six engineering design problems. results compared ten leading algorithms, classical Particle Swarm (PSO), Grey Wolf Optimizer (GWO), Whale (WOA), Moth-flame (MFO), Salp (SSA), Reptile Search (RSA), Sine Cosine (SCA), Constriction Coefficient-Based Gravitational (CPSOGSA), Biogeography-based (BBO). average rankings Wilcoxon Rank Sum Test provide comprehensive comparison clearly demonstrating its superiority. Furthermore, performance is assessed on 2020 2022 test suites, further confirming effectiveness. A comparative analysis against notable winners competitions, LSHADEcnEpSin, LSHADESPACMA, CMA-ES, CEC-2017 suite, revealed superior HCOADE. underscores advantages DE offers valuable insights addressing

Language: Английский

Citations

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