TimeLimited Tour Planning Based on Ant Colony Optimization Algorithm In this paper, Ant Colony Optimization algorithm is updated with multiple rules to solve the Timelimited tour planning problem. The precision of entertaining time in each sight can significantly increase the time complexity of an algorithm. With tourist attractions opening hour varies from one another, the shortest path will no longer suit the maximum entertaining time. In order to obtain the comprehensive optimal result of both travel time and travel length, the problem is divided into two major steps. The idea of crossover in Genetic Algorithm is introduced here to jumping out of the local loop of solution. Then, the local optimization of time planning is carried out for reducing the time complexity. Time optimizing values are then embedded into an overall travel distance optimization algorithm to acquire final results. By using the method of traversal, the results are verified which are identical to the traversal answer. To test our model, take Pan An Lake scenic spot, Xuzhou, China as an example to give specific results.

Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem Research Frontier Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporationbased and populationbased. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.