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CHAC, A MOACO algorithm for computation of bi‐criteria military unit path in the battlefield: Presentation and first results
Author(s) -
Mora A. M.,
Merelo J. J.,
Laredo J. L. J.,
Millan C.,
Torrecillas J.
Publication year - 2009
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20362
Subject(s) - ant colony optimization algorithms , computer science , path (computing) , greedy algorithm , mathematical optimization , dominance (genetics) , computation , military organization , algorithm , artificial intelligence , mathematics , history , ancient history , biochemistry , chemistry , gene , programming language
In this paper, we present a multiobjective ant colony optimization (MOACO) algorithm, called CHAC, designed to solve the problem of finding the path for a military unit that minimizes the cost in resources while maximizing safety. Unlike previous MOACO algorithms, CHAC uses a single colony and two different state transition rules: One that combines the heuristic and pheromone information of both objectives and another based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in different scenarios (maps with different degrees of difficulty), outperforming a greedy algorithm (taken as baseline), and yielding a good military behavior in the tactical sense. In comparison, the combined rule is slightly better than the rule based on dominance. © 2009 Wiley Periodicals, Inc.