A class of bilinear matrix constraint optimization problem and its applications

2021 ◽  
pp. 107429
Author(s):  
Wenjuan Zhang ◽  
Xiangchu Feng ◽  
Feng Xiao ◽  
Xudong Wang
2010 ◽  
Vol 437 ◽  
pp. 61-65 ◽  
Author(s):  
Ling Li Cheng ◽  
Jian Wei Yu ◽  
Xiao Fen Yu

A 6-DOF monolithic nanopositioning stage is developed for three coordinate measuring machines (CMM) with nanometer resolution. The stage consists of a monolithic flexure hinge mechanism, six piezoelectric actuators and six fiber-optic displacement sensors. A mathematical model of the constraint optimization problem is presented. Based on the solution of the optimization problem, the final design of the 6-DOF stage is also presented. The numerical analysis on static and dynamic behavior of the stage is done by using the finite element method. The experimental results of the performance of the 6-DOF stage are presented.


Author(s):  
Alexandre Medi ◽  
◽  
Tenda Okimoto ◽  
Katsumi Inoue ◽  
◽  
...  

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.


2020 ◽  
Vol 140 (2) ◽  
pp. 267-273
Author(s):  
Masato Yasuhara ◽  
Toshiyuki Miyamoto ◽  
Kazuyuki Mori ◽  
Shoichi Kitamura ◽  
Yoshio Izui

2015 ◽  
Vol 10 (6) ◽  
pp. 1081-1090 ◽  
Author(s):  
Yasuki Iizuka ◽  
◽  
Katsuya Kinoshita ◽  
Kayo Iizuka ◽  
◽  
...  

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.


1997 ◽  
Vol 06 (04) ◽  
pp. 567-585 ◽  
Author(s):  
T. L. Lau ◽  
E. P. K. Tsang

The Processor Configuration Problem (PCP) is a real life Constraint Optimization Problem. The task is to link up a finite set of processors into a network, whilst minimizing the maximum distance between these processors. Since each processor has a limited number of communication channels, a carefully planned layout will help reduce the overhead for message switching. In this paper, we present a Genetic Algorithm (GA) approach to the PCP. Our technique uses a mutation-based GA, a function that produces schemata by analyzing previous solutions, and an efficient data representation. Our approach has been shown to out-perform other published techniques in this problem.


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