Nature-Inspired Algorithms for Solving Weighted Constraint Satisfaction Problems

Mahdi Bidar, Malek Mouhoub

Abstract: Several applications such as timetabling, scheduling and resource allocation, can be represented as a Constraint Satisfaction Problem (CSP). Solving a CSP consists in finding a complete assignment of values to variables satisfying all the constraints. In many real-life scenarios (including over-constrained problems), some constraints (called soft constraints) can be violated according to some penalty function. In this regard, the Weighted CSP (WCSP) can be used as an extension of the CSP where each constraint comes with a cost function. Solving a WCSP consists in finding an optimal solution minimizing the total costs related to all constraints. Searching for an optimal solution to a WCSP is usually dealt with classical complete methods like backtracking and bucket elimination techniques. However, since WCSPs are NP-hard, complete methods will require exponential time cost. Therefore, approximation methods such as metaheuristics are appropriate alternatives as they are capable of prov