A Hybrid Method Based on Genetic Algorithm and Ant Colony System for Traffic Routing Optimization

Abstract

TThis paper presents a hybrid method that combines the genetic algorithm (GA) and the ant colony system algorithm (ACS), namely GACS, to solve the traffic routing problem. In the proposed framework, we use the genetic algorithm to optimize the ACS parameters in order to attain the best trips and travelling time through several novel functions to help ants to update the global and local pheromones. The GACS framework is implemented using the VANETsim package and the real city maps from the open street map project. The experimental results show that our framework achieves a considerably higher performance than A-Star and the classical ACS algorithms in terms of the length of the global best path and the time for trips. Moreover, the GACS framework is also efficient in solving the congestion problem by online monitoring the conditions of traffic light systems.
 KeywordsTraffic routing; Ant colony system; Genetic algorithm; VANET simulator.
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Matching with community sports facilities in a national fitness environment based on ant colony algorithm

“…update is also required to reduce the probability of the visited path being reselected, increase the exploration of the unvisited area, and avoid falling into local optimum. When an ant selects a new node, it needs to update the local pheromone according to formula (9). (9) In the formula, ξ is the local pheromone volatilization coefficient…”

“…When an ant selects a new node, it needs to update the local pheromone according to formula (9). (9) In the formula, ξ is the local pheromone volatilization coefficient…”

“…from 2015 to 2020. people's attention to sports is also increasing [9] . the determination of my country's "National Fitness Day" also fully shows the country's attention to sports [10] .…”