.png){kind=icon}
.png){kind=icon}
Return
Agent-based guided local search
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2012.03.074.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The main contribution of the paper is to propose and validate a new hybrid approach for solving combinatorial optimization problems in which guided local search metaheuristic is incorporated into a cooperative multi-agent framework based on the concept of asynchronous teams (A-Teams). Generally, an A-Team assumes that a collection of software agents, each representing a particular problem solving method, cooperate to solve a problem by dynamically evolving a population of solutions. In the suggested implementation each software agent carries out a guided local search. The proposed approach has been extensively validated experimentally on one of the best known combinatorial optimization problem - the vehicle routing problem. The promising results of experiments have confirmed the effectiveness of the suggested approach. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Guided local search
Cooperative multi-agent systems
Asynchronous team
Vehicle routing problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
No organization information available