Instinct-based PSO with local search applied to satisfiability

In particle swarm optimization (PSO), each particle stores a candidate solution, and stochastically modifies its candidate over time, based on the best solution found by neighboring particles, and based on the best solution found by the particle itself. In instinct-based PSO, each particle\´s behavior is also influenced by a third component which is meant to represent the particle\´s innate instinct-level intelligence. The instinct component is a function of the intrinsic "goodness" of each dimension of the particle\´s candidate solution and has similarity to the goodness measure used in ant colony methods. In this paper, we introduce a hybrid of instinct-based PSO and stochastic local search and apply it to weighted max-sat. We use, a test suite of ten 100-variable, 900-clauses problem instances, comparing our performance to standard PSO and to the Walk-Sat algorithm.