School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China.
Math Biosci Eng. 2020 Sep 30;17(6):6756-6774. doi: 10.3934/mbe.2020352.
Under the condition of known static environment and dynamic environment, an improved ant colony optimization is proposed to solve the problem of slow convergence, easily falling into local optimal solution, deadlock phenomenon and other issues when the ant colony optimization is constructed. Based on the traditional ant colony optimization, the ant colony search ability at the initial moment is strengthened and the range is expanded to avoid falling into the local optimal solution by adaptively changing the volatility coefficient. Secondly, the roulette operation is used in the state transition rule which improves the quality of the solution and the convergence speed of the algorithm effectively. Finally, through the elite selection and the node crossover operation of the better path, the global search efficiency and convergence speed of the algorithm are effectively improved. Several experimental results have also been obtained by applying the improved ant colony optimization to obstacle avoidance. The experimental results demonstrate the feasibility and effectiveness of the algorithm.
在已知静态环境和动态环境的条件下，提出了一种改进的蚁群优化算法，以解决蚁群优化算法在构建时收敛速度慢、容易陷入局部最优解、死锁现象等问题。在传统蚁群优化算法的基础上，自适应地改变挥发系数，增强了初始时刻蚂蚁的搜索能力，扩大了搜索范围，避免陷入局部最优解。其次，在状态转移规则中采用轮盘赌操作，有效地提高了解的质量和算法的收敛速度。最后，通过对较好路径的精英选择和节点交叉操作，有效地提高了算法的全局搜索效率和收敛速度。通过将改进的蚁群优化算法应用于避障问题，得到了几个实验结果。实验结果表明了该算法的可行性和有效性。
