Abstract
An improved coyote optimization algorithm (ICOA) is proposed to meet the requirements of global search capability, convergence speed, and stability for mobile robot path planning problems. Firstly, the population is divided into the elite group and the general groups after initialization, and the coyote individuals in the general groups are evolved by introducing the strategy of the best leading the poor. Secondly, after returning to the original position, coyotes adopt a new growth mode, and each individual makes a certain contribution to improve the level of the whole group. Thirdly, different from the original greedy algorithm, the survival of the fittest is carried out after each group of coyotes grows up, and the individuals do not affect each other. Finally, ICOA and the other seven optimization algorithms are simulated on four maps for mobile robot path planning. The simulation results show that ICOA can keep the diversity of the population, and has a strong global search ability, better stability, fast convergence speed, which reflects the strong optimization ability.
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