Approximation of Pareto optima in multiple-objective, shortest-path problems
Operations Research
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Approximation schemes for the restricted shortest path problem
Mathematics of Operations Research
Ant algorithms for discrete optimization
Artificial Life
An improved FPTAS for restricted shortest path
Information Processing Letters
Bi-Criterion Optimization with Multi Colony Ant Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Information Processing Letters
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A simple efficient approximation scheme for the restricted shortest path problem
Operations Research Letters
GRACE: a generational randomized ACO for the multi-objective shortest path problem
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Expert Systems with Applications: An International Journal
Ant colony optimization for the pareto front approximation in vehicle navigation
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
TSOIA: An efficient node selection algorithm facing the uncertain process for Internet of Things
Journal of Network and Computer Applications
A biologically inspired solution for fuzzy shortest path problems
Applied Soft Computing
Ant colony optimisation for vehicle traffic systems: applications and challenges
International Journal of Bio-Inspired Computation
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Multi-objective shortest path problem (MOSP) is an extension of a traditional single objective shortest path problem that seeks for the efficient paths satisfying several conflicting objectives between two nodes of a network. MOSP is one of the most important problems in network optimization with wide applications in telecommunication industries, transportation and project management. This research presents an algorithm based on multi-objective ant colony optimization (ACO) to solve the bi-objective shortest path problem. To analyze the efficiency of the algorithm and check for the quality of solutions, experimental analyses are conducted. Two sets of small and large sized problems that generated randomly are solved. Results on the set problems are compared with those of label correcting solutions that is the most known efficient algorithm for solving MOSP. To compare the Pareto optimal frontiers produced by the suggested ACO algorithm and the label correcting algorithm, some performance measures are employed that consider and compare the distance, uniformity distribution and extension of the Pareto frontiers. The results on the set of instance problems show that the suggested algorithm produces good quality non-dominated solutions and time saving in computation of large-scale bi-objective shortest path problems.