Quantum wavefunction optimization algorithm: application in solving traveling salesman problem

In this paper, we present a new optimization algorithm based on the properties of quantum particles represented by their wavefunctions. This algorithm is called the "quantum wavefunction optimization algorithm (QWOA)". We demonstrate the application of the QWOA to determine the optimal minimum distance for the traveling salesman problem (TSP). Specifically, we address the problem of traversing between cities in different countries using Google Maps, aiming to promote a real-time application of the proposed algorithm. To this end, we select cities from six different countries: Japan, India, Canada, China, Russia, and the United States of America. We use the QWOA to simulate and uncover the optimal shortest paths between these selected cities. The results of the QWOA are compared with those obtained using several well-known optimization algorithms, including the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and grey wolf optimizer (GWO). The experimental results, supported by statistical analysis, demonstrate the efficiency of the QWOA relative to these established optimization algorithms.