Discrete Particle Swarm Optimization Algorithm for Dynamic Constraint Satisfaction with Minimal Perturbation

Abstract

Constraint Satisfaction Problems (CSPs) provide an appropriate framework to formulate many real-world applications including scheduling, planning and resource allocation. However, the CSP description can change due to the evolving environment. The latter points to the fact that constraints might be subject to change over time and this can affect the feasibility of the solutions found so far. These changes can be captured with the Dynamic CSP (DCSP) formalism that has been proposed and investigated in the literature. More formally, a DCSP can be seen as a series of static CSPs, each resulting from a restriction or a relaxation of some constraints in the previous CSP constraint set. This paper focuses on constraint restriction (constraint addition) and the goal is to obtain the most similar solution to the previous one that satisfies the old and new constraints. In this regard, we propose a new method based on the Particle Swarm Optimization algorithm to solve these DCSPs with minimal perturbation. To evaluate the efficiency of the proposed method, we conducted extensive experiments on randomly generated DCSP instances generated by the model RB. The results achieved clearly demonstrate the efficiency of the proposed algorithm over other known exact and approximation techniques used in the literature for solving these problems.