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title"Enhanced Coot optimization algorithm and its application"
Abstract: In response to the limitations of weak global search capability, slow convergence speed, and susceptibility to local optima in the Coot optimization algorithm(COA), this paper proposes an enhanced Coot optimization algorithm (ECOA).First, Latin hypercube sampling method is used to uniformly initialize the population, which improves the diversity of the initial population and the global performance of the algorithm. Second, the leader-following position update formula of the original algorithm is modified by integrating a dropout mechanism to avoid excessive concentration, which enhances the ability to escape local optima. Finally, a quadratic interpolation strategy is used to improve the algorithm's convergence speed and optimization accuracy. CEC2017 test functions are utilized to evaluate the performance of ECOA, COA, and other six popular swarm intelligence algorithms. Experimental results show that ECOA outperforms the original algorithm and comparison algorithms in terms of accuracy, convergence speed, and stability. In addition, the practical applicability of ECOA is demonstrated by three engineering optimization problems.
