Authors:
Sadok Bouamama
and
Khaled Ghédira
Affiliation:
SOIE/University of Tunis, Tunisia
Keyword(s):
Constraint satisfaction and optimization problems, multi-agent systems, genetic algorithms, Min-conflict-heuristic, guidance operator.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Distributed Control Systems
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Learning Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
Within the framework of Constraint satisfaction and optimization problem (CSOP), we introduce a new optimization distributed method based on Genetic Algorithms (GA). This method consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA on its own sub-population. This GA is sometimes random and sometimes guided by both the template concept and by the Min-conflict-heuristic. In addition with guidance, our approach is based on NEO-DARWINISM theory and on the nature laws. In fact, by reference to their specificity the new algorithm will let the agents able to count their own GA parameters. In order to show D3G2A advantages, experimental comparison with GGA is provided by their application on the large processors configuration problem.