Development of a traditional transport system based on the bee colony algorithm

Abstract

At present, a significant part of optimization problems, particularly questions of combinatorial optimization, are considered NP-complete problems. When solving optimization problems, the neural network approach increases the probability of obtaining an optimal solution. The traveling salesman problem is considered a test optimization problem. This problem was solved using the Hopfield neural network. In solving optimization problems, numerous computation processes and computation time are required. To improve performance and increase the program's speed, there are cases of inappropriate purchase of additional programs and tools, and involvement of additional services. In these cases, parallel computing technologies are used to give an effective result. Based on the developed algorithms, several computational experiments were carried out. The analysis of the obtained results showed that the algorithms of artificial neural networks proposed by us, in comparison with the algorithms created based on Hopfield neural networks, are characterized by low resource consumption and efficiency in terms of high speed. But, it should be noted that if the volume of tasks is very large, neural network algorithms may become less efficient due to longer computation. In such cases, it is usually advisable to use evolutionary algorithms. In particular, the study considers using the bee swarm algorithm for parallel computing technologies. Solving optimization problems using the bee swarm algorithm in parallel computing technologies can be significantly efficient and fast.