scholarly journals A novel ant colony optimization algorithm for the shortest-path problem in traffic networks

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1619-1628 ◽  
Author(s):  
Shuijian Zhang ◽  
Xuejun Liu ◽  
Meizhen Wang
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Single Pair ◽  
Traffic Networks ◽  
Shortest Path Problems ◽  
Combinatorial Optimization Problems ◽  
Pheromone Trail

The Ant Colony Optimization (ACO) algorithm is a metaheuristic nature-inspired technique for solving various combinatorial optimization problems. The shortest-path problem is an important combinatorial optimization problem in network optimization. In this paper, a novel algorithm based on ACO to solve the single-pair shortest-path problem in traffic networks is introduced. In this algorithm, a new strategy is developed to find the best solution in a local search, by which the ants seek the shortest path using both a pheromone-trail-following mechanism and an orientation-guidance mechanism. A new method is designed to update the pheromone trail. To demonstrate the good performance of the algorithm, an experiment is conducted on a traffic network. The experimental results show that the proposed algorithm produces good-quality solutions and has high efficiency in finding the shortest path between two nodes; it proves to be a vast improvement in solving shortest-path problems in traffic networks. The algorithm can be used for vehicle navigation in intelligent transportation systems.

Shortest Path Problem Solving Based on Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 17 (1-2) ◽  
pp. 7-17 ◽  
Author(s):  
Mariusz Gła̢bowski ◽  
Bartosz Musznicki ◽  
Przemysław Nowak ◽  
Piotr Zwierzykowski
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Path Problem ◽  
Combinatorial Problems ◽  
Ant Colony ◽  
Optimization Approach ◽  
Algorithmic Optimization ◽  
Network Utilization ◽  
Shortest Route ◽  
Hard Problems

Abstract The Ant Colony Optimization (ACO) metaheuristic is a versatile algorithmic optimization approach based on the observation of the behaviour of ants. As a result of numerous analyses, ACO has been applied to solving various combinatorial problems. The ant colony metaheuristic proves itself to be efficient in solving NP-hard problems, often generating the best solution in the shortest amount of time. However, not enough attention has been paid to ACO as a means of solving problems that have optimal solutions which can be found using other methods. The shortest path problem is undoubtedly one of the aspects of great significance to navigation and telecommunications. It is used, amongst others, for determining the shortest route between two geographical locations, for routing in packet networks, and to balance and optimize network utilization. Thus, this article introduces ShortestPathACO, an Ant Colony Optimization based algorithm designed to find the shortest path in a graph. The algorithm consists of several subproblems that are presented successively. Each subproblem is discussed from many points of view to enable researchers to find the most suitable solutions to the problems they investigate.

scholarly journals Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 286
Author(s):  
Ali Ahmid ◽  
Thien-My Dao ◽  
Ngan Van Le
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
The Right ◽  
Design Cost ◽  
Laminated Structures

Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17.

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