<![CDATA[Metaheuristic algorithms are often used to tackle various optimization problems. In recent years, many new metaheuristic algorithms have been developed, such as the snow avalanches algorithm (SAA), which is inspired by natural snow avalanches. SAA consists of four avalanche phases: avalanches due to steep mountain slopes, human factors, local weather conditions, and it only has one control parameter. Like most metaheuristic algorithms, SAA has the potential to get trapped in local optima due to having only one control parameter. Therefore, this study presents a modification of SAA, called modified SAA (mSAA), which integrates the opposition-based learning (OBL) method with SAA to enhance the optimization process. To validate the performance of mSAA, tests were conducted on various OBL techniques to determine the best combination for solving complex and nonlinear problems, specifically the vehicle routing problem (VRP) on three types of VRP datasets (D01, D02, and D03 datasets). The results were then compared with the snow avalanches algorithm (SAA), hiking optimization algorithm (HOA), teaching learning-based optimization (TLBO), and grey wolf optimizer (GWO). Based on the average value, standard deviation, and best value, the mSAA method performed well and effectively in solving VRP using a combination of Quasi OBL and S_i=0.6+0.4 rand.]]>
Copyrights © 2024
