A COLLABORATIVE FRAMEWORK FOR DISTRIBUTED MULTIOBJECTIVE COMBINATORIAL OPTIMIZATION

被引:0
作者
Nino, Elias D.
William Caicedo, T.
Omer Salcedo, G.
机构
来源
2011 INTERNATIONAL CONFERENCE ON COMPUTER AND COMPUTATIONAL INTELLIGENCE (ICCCI 2011) | 2012年
关键词
Combinatorial optimization; Hybrid metaheuristics; Multiobjective optimization; Distributed optimization; Multi-objective traveling salesman problem;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper states a collaborative framework for the distributed multiobjective optimization of combinatorial problems. The proposed framework is completely agnostic to the specific specialized metaheuristic used. Thus, it is able to use different hybrid strategies using two or more metaheuristics in a collaborative fashion. Besides, the designed framework uses a central repository of non-dominated solutions. The solutions are further processed in different nodes (machines) and later go back to the central repository. On the other hand, once the metaheuristic has converged to a new solution its quality is checked, and if it is a non-dominated solution then it is stored in the central repository to be used by other nodes (possibly executing a different metaheuristic) as a new starting point. Lastly, we tested the proposed framework using metrics from the specialized literature. Results show a consistent improvement of the Pareto Front as the number of nodes is increased.
引用
收藏
页码:33 / 37
页数:5
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