Discrete Focus Group Optimization Algorithm for Solving Constraint Satisfaction Problems

We present a new nature-inspired approach based on the Focus Group Optimization Algorithm (FGOA) for solving Constraint Satisfaction Problems (CSPs). CSPs are NP-complete problems meaning that solving them by classical systematic search methods requires exponential time, in theory. Appropriate alternatives are approximation methods such as metaheuristic algorithms which have shown successful results when solving combinatorial problems. FGOA is a new metaheuristic inspired by a human collaborative problem solving approach. In this paper, the steps of applying FGOA to CSPs are elaborated. More precisely, a new diversification method is devised to enable the algorithm to efficiently find solutions to CSPs, by escaping local optimum. To assess the performance of the proposed Discrete FGOA (DFGOA) in practice, we conducted several experiments on randomly generate hard to solve CSP instances (those near the phase transition) using the RB model. The results clearly show the ability of DFGOA to successfully find the solutions to these problems in very reasonable amount of time.