Rahman Lingkon Md Limonur, Ahmmed Md Sazol
Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi- 6204, Bangladesh.
Heliyon. 2024 Nov 5;10(22):e40134. doi: 10.1016/j.heliyon.2024.e40134. eCollection 2024 Nov 30.
To balance the convergence speed and solution diversity and enhance optimization performance when addressing large-scale optimization problems, this research study presents an improved ant colony optimization (ICMPACO) technique. Its foundations include the co-evolution mechanism, the multi-population strategy, the pheromone diffusion mechanism, and the pheromone updating method. The suggested ICMPACO approach separates the ant population into elite and common categories and breaks the optimization problem into several sub-problems to boost the convergence rate and prevent slipping into the local optimum value. To increase optimization capacity, the pheromone update approach is applied. Ants emit pheromone at a certain spot, and that pheromone progressively spreads to a variety of nearby regions thanks to the pheromone diffusion process. Here, the real gate assignment issue and the travelling salesman problem (TSP) are chosen for the validation of the performance for the optimization of the ICMPACO algorithm. The experiment's findings demonstrate that the suggested ICMPACO method can successfully solve the gate assignment issue, find the optimal optimization value in resolving TSP, provide a better assignment outcome, and exhibit improved optimization ability and stability. The assigned efficiency is comparatively higher than earlier ones. With an assigned efficiency of 83.5 %, it can swiftly arrive at the ideal gate assignment outcome by assigning 132 patients to 20 gates of hospital testing rooms. To minimize the patient's overall hospital processing time, this algorithm was specifically employed with a better level of efficiency to create appropriate scheduling in the hospital.
为了在解决大规模优化问题时平衡收敛速度和解的多样性并提高优化性能，本研究提出了一种改进的蚁群优化（ICMPACO）技术。其基础包括协同进化机制、多种群策略、信息素扩散机制和信息素更新方法。所提出的ICMPACO方法将蚁群分为精英和普通两类，并将优化问题分解为几个子问题，以提高收敛速度并防止陷入局部最优值。为了提高优化能力，应用了信息素更新方法。蚂蚁在某个位置释放信息素，由于信息素扩散过程，该信息素会逐渐扩散到附近的各个区域。在此，选择实际的门分配问题和旅行商问题（TSP）来验证ICMPACO算法优化的性能。实验结果表明，所提出的ICMPACO方法能够成功解决门分配问题，在解决TSP时找到最优优化值，提供更好的分配结果，并表现出更高的优化能力和稳定性。分配效率相对高于早期方法。在分配效率为83.5%的情况下，通过将132名患者分配到医院检查室的20个门，可以迅速得出理想的门分配结果。为了最大限度地减少患者在医院的总处理时间，该算法以更高的效率专门用于在医院创建适当的调度。
