Authors:
Limeme Ben Ali
1
;
Maher Helaoui
2
and
Wady Naanaa
3
Affiliations:
1
Faculty of Economics and Management of Sfax, University of Sfax and Tunisia
;
2
Higher Institute of Business Administration, University of Gafsa and Tunisia
;
3
National Engineering School of Tunis, University Tunis El Manar and Tunisia
Keyword(s):
Multi-objective Optimization, Multi-objective Valued Constraint Satisfaction Problems MO-VCSP, Soft Local Arc Consistency, Lower Bound Set, Pareto Dominance.
Related
Ontology
Subjects/Areas/Topics:
AI and Creativity
;
Artificial Intelligence
;
Constraint Satisfaction
;
Knowledge Representation and Reasoning
;
Soft Computing
;
Symbolic Systems
Abstract:
A valued constraint satisfaction problem (VCSP) is a soft constraint framework that can formalize a wide range of applications related to Combinatorial Optimization and Artificial Intelligence. Most researchers have focused on the development of algorithms for solving mono-objective problems. However, many real-world satisfaction/optimization problems involve multiple objectives that should be considered separately and satisfied/optimized simultaneously. Solving a Multi-Objective Optimization Problem (MOP) consists of finding the set of all non-dominated solutions, known as the Pareto Front. In this paper, we introduce multi-objective valued constraint satisfaction problem (MO-VCSP), that is a VCSP involving multiple objectives, and we extend soft local arc consistency methods, which are widely used in solving Mono-Objective VCSP, in order to deal with the multi-objective case. Also, we present multi-objective enforcing algorithms of such soft local arc consistencies taking into acco
unt the Pareto principle. The new Pareto-based soft arc consistency (P-SAC) algorithms compute a Lower Bound Set of the efficient frontier. As a consequence, P-SAC can be integrated into a Multi-Objective Branch and Bound (MO-BnB) algorithm in order to ensure its pruning efficiency.
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