Abstract
AbstractRecently, a new negative learning variant of ant colony optimization (ACO) has been used to successfully tackle a range of combinatorial optimization problems. For providing stronger evidence of the general applicability of negative learning ACO, we investigate how it can be adapted to solve the Maximum Satisfiability problem (MaxSAT). The structure of MaxSAT is different from the problems considered to date and there exists only a few ACO approaches for MaxSAT. In this paper, we describe three negative learning ACO variants. They differ in the way in which sub-instances are solved at each algorithm iteration to provide negative feedback to the main ACO algorithm. In addition to using IBM ILOG CPLEX, two of these variants use existing MaxSAT solvers for this purpose. The experimental results show that the proposed negative learning ACO variants significantly outperform the baseline ACO as well as IBM ILOG CPLEX and the two MaxSAT solvers. This result is of special interest because it shows that negative learning ACO can be used to improve over the results of existing solvers by internally using them to solve smaller sub-instances.
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,General Computer Science
Reference85 articles.
1. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)
2. Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical Report, Pennsylvania State University (1991). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.6342. Accessed 15 Mar 2020
3. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)
4. Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the ant system. In: A computational study. Technical Report, Wirtschaftsuniversität Vienna (1997). https://epub.wu.ac.at/id/eprint/616. Accessed 16 Mar 2020
5. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolut. Comput. 1(1), 53–66 (1997)
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