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Implementation of an Enhanced Crayfish Optimization Algorithm
Yi Zhang1, Pengtao Liu1, Yanhong Li2
1College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130000, China.
Biomimetics (Basel, Switzerland)
|June 26, 2024
Summary
This study introduces an enhanced crayfish optimization algorithm (ECOA) with novel strategies for improved performance. The ECOA demonstrates superior convergence, stability, and local optima avoidance in engineering optimization tasks.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Metaheuristic Computing
Background:
- Crayfish optimization algorithm (COA) is a metaheuristic algorithm inspired by crayfish behavior.
- Existing COA variants may suffer from slow convergence and premature local optima entrapment.
- Enhancing COA is crucial for improving its efficiency in complex optimization problems.
Purpose of the Study:
- To introduce an enhanced crayfish optimization algorithm (ECOA) with four novel improvement strategies.
- To evaluate the performance of ECOA against other popular algorithms using the IEEE CEC2019 test suite.
- To validate the applicability of ECOA to real-world engineering optimization problems.
Main Methods:
- Population initialization improvement using Halton sequence.
- Quasi opposition-based learning for enhanced searchability.
- Elite factor guidance during the predation stage.
- Fish aggregation device effect for improved local optima escape.
Main Results:
- ECOA exhibited faster convergence speed compared to other algorithms.
- ECOA demonstrated superior performance stability across various test functions.
- ECOA showed a stronger ability to escape local optima.
- ECOA proved effective in solving real-world engineering optimization problems.
Conclusions:
- The proposed ECOA significantly enhances the original COA's performance.
- ECOA offers a robust and efficient solution for complex optimization challenges.
- The integration of novel strategies makes ECOA a competitive alternative in the field of optimization algorithms.
