Augmenting the Crayfish Optimization with Gaussian Distribution Parameter for Improved Optimization Efficiency

Daulat, Himani; Varma, Tarun; Chauhan, Krishna

doi:10.1109/icc-robins60238.2024.10533985

2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS) 2024

DOI: 10.1109/icc-robins60238.2024.10533985

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Augmenting the Crayfish Optimization with Gaussian Distribution Parameter for Improved Optimization Efficiency

Himani Daulat,

Tarun Varma,

Krishna Chauhan

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2024

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Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

Fakhouri,

Ishtaiwi,

Makhadmeh

et al. 2024

Symmetry

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This study presents the Hybrid COASaDE Optimizer, a novel combination of the Crayfish Optimization Algorithm (COA) and Self-adaptive Differential Evolution (SaDE), designed to address complex optimization challenges and solve engineering design problems. The hybrid approach leverages COA’s efficient exploration mechanisms, inspired by crayfish behaviour, with the symmetry of SaDE’s adaptive exploitation capabilities, characterized by its dynamic parameter adjustment. The balance between these two phases represents a symmetrical relationship wherein both components contribute equally and complementary to the algorithm’s overall performance. This symmetry in design enables the Hybrid COASaDE to maintain consistent and robust performance across a diverse range of optimization problems. Experimental evaluations were conducted using CEC2022 and CEC2017 benchmark functions, demonstrating COASaDE’s superior performance compared to state-of-the-art optimization algorithms. The results and statistical analyses confirm the robustness and efficiency of the Hybrid COASaDE in finding optimal solutions. Furthermore, the applicability of the Hybrid COASaDE was validated through several engineering design problems, where COASaDE outperformed other optimizers in achieving the optimal solution.

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Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

Fakhouri,

Ishtaiwi,

Makhadmeh

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

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Augmenting the Crayfish Optimization with Gaussian Distribution Parameter for Improved Optimization Efficiency

Cited by 1 publication

References 24 publications

Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

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