Abstract: Aiming to address the problems of insufficient global search capability, easily falling into local optima, and slow convergence speed of the coati optimization algorithm(COA), an improved coati optimization algorithm based on nonlinear adaptation(NACOA)was proposed. A Logistic-Tent mapping was used to initialize the coati population, which improved the initial search space coverage of the algorithm and generated more dispersed and high-quality initial solutions. The Levy flight strategy was introduced, which made use of its long-jump characteristic to enhance the global search capability of the algorithm and effectively avoided the algorithm from falling into local optima. The nonlinearly diminishing inertia weight was used to increase the adaptability of the population and the search efficiency, balance the global and local search capabilities, and improve the population convergence accuracy through the golden sine strategy. Comparative simulation experiments were conducted on benchmark test functions, and the results showed that NACOA had better convergence speed and optimization accuracy. The NACOA was applied to the design of engineering problems, which proved the effectiveness and practicality of this algorithm.

Key words: coati optimization algorithm, Logistic-Tent mapping, nonlinearly diminishing inertia weight, golden sine strategy, engineering application