Diversification and Entropy Improvement on the DPSO Algorithm for DTSP

This paper introduces a new Discrete Particle Swarm Optimization (DPSO) algorithm for solving the Dynamic Traveling Salesman Problem (DTSP) with entropy diversity control. An experimental environment is stochastic and dynamic. Changeability requires the algorithm to have the ability to quickly adapt. Most scientists draw attention to the correlation between the population diversity and the convergence to the optimum. Controlling population variation allows for the control of a stable convergence of the algorithm to the optimum and provides a good mechanism for avoiding stagnation. This article describes the control of this parameter by examining the pheromone matrix by using the entropy measure. The results of the research on the different variants of the measure in the context of a dynamic TSP are presented.

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