Track: Artificial Intelligence
In this research, the shortest path problem (SPP) is investigated to evaluate a new competent heuristic strategy. The SPP is a key interested issue in operational research. Industrial experts frequently should tackle this problem in diverse procedures such as transportation, routing and communications. Due to the inherent complexity of SPP, metaheuristics can find satisfactory trajectories in a rational time. For this purpose, an attuned chaotic particle swarm optimizer, which is entitled CPSO+, is proposed here to realize the SPP in a more operational way. In addition, the influence of chaos is evaluated and discussed specifically to investigate the quality of obtained paths. Hence, some chaos-based signals such as Zaslavskii, Tinkerbell, Lozi and Burgers are investigated based on qualitative comparisons. Then, the best chaotic pattern is identified and picked out to provision the disordered particle motions in PSO approach. These patterns are utilized to enhance the exploration also exploitation styles of the normal PSO. The CPSO+ technique is evaluated against PSO and several methods from literature. The Obtained computational outcomes exemplify that the effectiveness of the suggested methodology is desirable based on quality measurements. The offered PSO approach with Zaslavskii sequence can effectively obtain the optimal routes compared to other implemented strategies.