Journal of the ACM (JACM)
Shortest paths algorithms: theory and experimental evaluation
Mathematical Programming: Series A and B
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Optimal paths in graphs with stochastic or multidimensional weights
Communications of the ACM
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Ant Colony Optimization for Multi-Objective Optimization Problems
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
A constraint-based solver for the military unit path finding problem
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a family of Multi-Objective Ant Colony Optimization (MOACO) algorithms, globally identified as hCHAC, which have been designed to solve a pathfinding problem in a military context considering two objectives: maximization of speed and safety. Each one of these objectives include different factors (such as stealth or avoidance of resource-consuming zones), that is why in this paper we generate different members of the hCHAC family by aggregating the initial cost functions into a different amount of objectives (from one to four) and considering a different parametrization set in each case. The hCHAC algorithms have been tested in several different (and increasingly realistic) scenarios, modelled in a simulator and compared with some other well-known MOACOs. These latter algorithms have been adapted for the purpose of this work to deal with this problem, along with a new multi-objective greedy approach that has been included as baseline for comparisons. The experiments show that most of the hCHAC algorithms outperform the other approaches, yielding at the same time very good military behaviour in the tactical sense. Within the hCHAC family, hCHAC-2, an approach considering two objectives, yields the best results overall.