Abstract
Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems. This work proposes MaxMin-SAT, an ACO alternative for the satisfiability problem (SAT). MaxMin-SAT is the first ACO algorithm for SAT that implements an Adaptive Fitness Function, which is a technique used for Genetic Algorithms to escape local optima. To show effectiveness of this technique, three different adaptive fitness functions are compared: Stepwise Adaptation of Weights, Refining Functions, and a mix of the previous two. To experimentally test MaxMin-SAT, a comparison with Walksat (a successful local search algorithm) is presented. Even though MaxMin-SAT cannot beat Walksat when dealing with phase transition instances, experimental results show that it can be competitive with the local search heuristic for overconstrained instances.KeywordsSATAnt Colony OptimizationLocal SearchAdaptive Fitness Function
Published Version
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