Entropy-based Adaptive Chaotic Ant Colony Algorithm And Its Application

With the rapid development of economy and technology,the development of mobile robots is becoming more and more rapid,and path planning is one of the important technologies of mobile robots.The ant colony algorithm is a classic group intelligence algorithm,which was first applied to the TSP problem.In order to solve the problem better,the scientists have made various improved ant colony algorithms to improve the performance of the basic ant colony algorithm.Based on the basic ant colony algorithm,an adaptive chaotic ant colony algorithm based on entropy is proposed and the path planning problem is successfully processed.The main research contents of this paper are as follows:Firstly,in order to improve the population diversity of the algorithm,a dynamic chaotic ant colony algorithm is proposed.After discussing several classical chaotic maps,we choose to introduce Logistic chaotic map into the basic ant colony system.The ergodicity of chaos can improve the population diversity of the ant colony system,and the positive feedback characteristics of the ant colony system can reduce the blindness and uncertainty of chaos,and the two promote each other.In order to solve the problem of the decrease in convergence speed caused by the increase in diversity,this topic dynamically introduces chaotic mapping.The dynamic chaotic ant colony system solves the TSP standard test sets of 8 different scales and proves the effectiveness of the dynamic chaotic ant colony system.Secondly,in order to improve the adaptive performance of the algorithm,an adaptive fuzzy ant colony algorithm is proposed.Fuzzy rules are introduced into the ant colony system,and the global pheromone update rules of the basic ant colony system are improved by fuzzy rules to improve the quality of the solution;and the concept of information entropy is introduced to describe the population diversity of the algorithm.According to the information entropy,it is judged whether fuzzy rules are introduced to increase the adaptability of the algorithm,thereby increasing the convergence speed of the algorithm.The adaptive fuzzy ant colony algorithm solves the TSP standard test sets of 14 different scales and proves the effectiveness of the adaptive fuzzy ant colony algorithm.Then,in order to balance the relationship between algorithm population diversity and convergence speed,an adaptive chaotic ant colony algorithm based on entropy is proposed.Firstly,the chaotic map is introduced into the basic ant colony system to increase the population diversity of the algorithm.Then the information entropy is introduced into the algorithm,and the information entropy is used to judge whether the algorithm introduces chaotic map to increase the convergence speed of the algorithm.Finally,the adaptive fuzzy ant colony algorithm solves the TSP standard test sets of nine different scales,and proves that the adaptive chaotic ant colony algorithm based on entropy can balance the relationship between the population diversity and the convergence speed of the algorithm.Finally,the improved algorithm is used to solve different robot path planning problems.The simulation scenario shows the feasibility and effectiveness of the improved algorithm in practical application.