scholarly journals A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Duan Peibo ◽  
Zhang Changsheng ◽  
Zhang Bin
Keyword(s):  
Optimization Algorithm ◽  
Local Stability ◽  
Large Scale ◽  
Research Direction ◽  
Initial Solution ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Distributed Constraint Optimization Problem ◽  
New Research ◽  
New Criterion

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time.

A Two-Phase Complete Algorithm for Multi-Objective Distributed Constraint Optimization

2014 ◽  
Vol 18 (4) ◽  
pp. 573-580
Author(s):  
Alexandre Medi ◽  
◽  
Tenda Okimoto ◽  
Katsumi Inoue ◽  
◽  
...  
Keyword(s):  
Branch And Bound ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Branch And Bound Algorithm ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Multi Objective ◽  
Multi Agent ◽  
Distributed Constraint Optimization Problem

A Distributed Constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Many application problems in multi-agent systems can be formalized as DCOPs. However, many real world optimization problems involve multiple criteria that should be considered separately and optimized simultaneously. A Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) is an extension of a mono-objective DCOP. Compared to DCOPs, there exists few works on MO-DCOPs. In this paper, we develop a novel complete algorithm for solving an MO-DCOP. This algorithm utilizes a widely used method called Pareto Local Search (PLS) to generate an approximation of the Pareto front. Then, the obtained information is used to guide the search thresholds in a Branch and Bound algorithm. In the evaluations, we evaluate the runtime of our algorithm and show empirically that using a Pareto front approximation obtained by a PLS algorithm allows to significantly speed-up the search in a Branch and Bound algorithm.

scholarly journals Neural Regret-Matching for Distributed Constraint Optimization Problems

2021 ◽  
Author(s):  
Yanchen Deng ◽  
Runsheng Yu ◽  
Xinrun Wang ◽  
Bo An
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Scale Up ◽  
Constraint Optimization ◽  
Confidence Bounds ◽  
Distributed Constraint Optimization ◽  
Multi Agent ◽  
Constraint Optimization Problems ◽  
Regret Bound ◽  
Agent Coordination

Distributed constraint optimization problems (DCOPs) are a powerful model for multi-agent coordination and optimization, where information and controls are distributed among multiple agents by nature. Sampling-based algorithms are important incomplete techniques for solving medium-scale DCOPs. However, they use tables to exactly store all the information (e.g., costs, confidence bounds) to facilitate sampling, which limits their scalability. This paper tackles the limitation by incorporating deep neural networks in solving DCOPs for the first time and presents a neural-based sampling scheme built upon regret-matching. In the algorithm, each agent trains a neural network to approximate the regret related to its local problem and performs sampling according to the estimated regret. Furthermore, to ensure exploration we propose a regret rounding scheme that rounds small regret values to positive numbers. We theoretically show the regret bound of our algorithm and extensive evaluations indicate that our algorithm can scale up to large-scale DCOPs and significantly outperform the state-of-the-art methods.

A Distributed Autonomous Approach to Developing a Disaster Evacuation Assist System

2015 ◽  
Vol 10 (6) ◽  
pp. 1081-1090 ◽  
Author(s):  
Yasuki Iizuka ◽  
◽  
Katsuya Kinoshita ◽  
Kayo Iizuka ◽  
◽  
...  
Keyword(s):  
Completion Time ◽  
Optimization Problem ◽  
Ad Hoc ◽  
The Other ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Emergency Situations ◽  
Agent Simulation ◽  
Multi Agent ◽  
Distributed Constraint Optimization Problem

In times of disaster, or other emergency situations, it is essential for people to be evacuated in a smooth manner. Evacuation must be performed promptly and safely. It is necessary to avoid generating a secondary disaster at the time of evacuation. However, this is not easy to realize, because people often tend to panic when faced with disaster, crowding the evacuation passageways of buildings. On the other hand, people do not attempt to evacuate themselves from danger when the normalcy bias has occurred. Therefore, evacuation guidance is very important. However, it is impossible to guide all evacuees through authorities such as disaster countermeasure offices. To deal with this issue, the authors propose a system that provides optimal evacuation guidance autonomously without central server. The system works on the mobile devices of evacuees, performs distributed calculations using the framework of the distributed constraint optimization problem on ad-hoc communication, and does not need a central server. In the experiment using multi-agent simulation, for the case where the evacuees can receive evacuation guidance from this system, the evacuation completion time decreased. This paper presents an overview and the evaluation results of the prototype of the disaster evacuation assistance system.

Smart system to generate the optimal authorized bounding volume

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alia Belkaïd ◽  
Abdelkader Ben Saci ◽  
Ines Hassoumi
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problem ◽  
Generation Process ◽  
Constraint Optimization ◽  
Content Type ◽  
Distributed Constraint Optimization ◽  
Bounding Volume ◽  
Target Values ◽  
Distributed Constraint Optimization Problem ◽  
Overall Functioning

PurposeThe overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine, and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence (AI) in accomplishing this task and solving the problem.Design/methodology/approachThis paper provides a method of computational design in generating the optimal authorized bounding volume which uses the best target values of morphological urban rules. It examines an intelligent system, adopting the multi-agent approach, which aims to control and increase urban densification by optimizing morphological urban rules. The process of the system is interactive and iterative. It allows collaboration and exchange between the machine and the designer. This paper is adopting and developing a new approach to resolve the distributed constraint optimization problem in generating the authorized bounding volume. The resolution is not limited to an automatic volume generation from urban rules, but also involves the production of multiple optimal-solutions conditioned both by urban constraints and relevance chosen by the designer. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the AI in accomplishing this task and solving the problem. The human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction. This paper shows and analyses interactions with the machine during the building generation process. It presents different cases of application and discusses the relationship between relevance and constraints satisfaction. This topic can inform a chosen urban densification strategy by assisting a typology of the optimal authorized bounding volume.FindingsThe human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction with fitness function.Originality/valueThe resolution of the distributed constraint optimization problem is not limited to an automatic generation of urban rules, but involves also the production of multiple optimal ABV conditioned both by urban constraints as well as relevance, chosen by the designer.

Leximin Asymmetric Multiple Objective Distributed Constraint Optimization Problem

2017 ◽  
Vol 34 (1) ◽  
pp. 49-84 ◽  
Author(s):  
Toshihiro Matsui ◽  
Hiroshi Matsuo ◽  
Marius Silaghi ◽  
Katsutoshi Hirayama ◽  
Makoto Yokoo
Keyword(s):  
Optimization Problem ◽  
Multiple Objective ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Distributed Constraint Optimization Problem
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