A Novel CarPooling Optimization Method Using Ant Colony Optimization Based on Network Analysis (Case Study: Tehran) The dramatic increment in the number of cars in cities makes numerous challenges including either air or noise pollution and traffic congestion. Outweighing these problems needs essential planning in urban management such as using new procedures in transportation systems. Sharing vehicles is one of the most useful methods which some countries have been experiencing it. In this issue, 2 or 3 people share a car, and it decreases running vehicles, and the urban traffic will be declined. This article uses a novel method to share cars based on Ant Colony Optimization Algorithm. The proposed model tries to find the best matching of passengers in cars so that the maximum of shared cars is found. Consequently, some vehicles will be switched off, and this helps to decrease the traffic. The results depict the proposed method turns off 41.8% of cars; besides, 27.8% of them carry 2, 3 or 4 passengers.

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.