An improved crayfish optimization algorithm for solving engineering optimization problems

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization
• Swarm Intelligence

Background:

• The Crayfish Optimization Algorithm (COA) balances global search and local exploration.
• COA faces challenges including diversity degradation, limited exploration, and premature convergence to local minima.

Purpose of the Study:

• To introduce an improved Crayfish Optimization Algorithm (ICOA) addressing the limitations of the original COA.
• To enhance optimization efficiency, population diversity, and exploration capabilities in metaheuristic algorithms.

Main Methods:

• Population initialization using Sobol sequence mapping to increase diversity.
• Integration of Lévy flight strategy in the foraging phase for enhanced exploration.
• Euclidean distance-fitness balanced competition strategy during the competition phase for simultaneous exploitation and exploration.

Main Results:

• ICOA demonstrated superior performance on IEEE CEC2019 and CEC2020 benchmark functions across various dimensions.
• Validation on five engineering optimization problems showed significant improvements over COA, with gains up to 88.8%.
• Sensitivity, quantitative, and nonparametric statistical analyses confirmed ICOA's robustness and effectiveness.

Conclusions:

• ICOA effectively overcomes the limitations of COA, offering enhanced optimization capabilities for complex problems.
• The proposed strategies significantly improve the efficacy of the Crayfish Optimization Algorithm.
• ICOA shows strong potential for application in diverse engineering optimization challenges.