The ant colony optimization meta-heuristic
New ideas in optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
A constraint-based solver for the military unit path finding problem
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
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hCHAC, a MOACO implemented to solve the problem of finding the path that minimizes resources, while maximizing safety for a military unit in realistic battlefields, is compared with some other approaches: two extreme methods, which only considers one objective in the search, and a mono-objective algorithm, which combines the two objectives terms of the formulae in a single. In addition, two state transition rules (combined and dominance-based) have been used in some of the approaches.All of them have been tested in different difficulty maps and hCHAC using the combined state transition rule has been considered the best approach.