The typical environmental system optimization problems solved by computer software

2015 ◽  
Author(s):  
Baoyou Liu ◽  
Nanxi Jin
Keyword(s):  
Optimization Problems ◽  
Computer Software ◽  
System Optimization ◽  
Environmental System

Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

2003 ◽  
Author(s):  
Bong Seong Jung ◽  
Bryan W. Karney
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Optimization Problems ◽  
System Optimization ◽  
Water Hammer ◽  
Pipeline System ◽  
Optimal Size ◽  
Protection Strategy ◽  
Hydraulic Devices ◽  
Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

Structural Dynamics

1985 ◽  
Vol 38 (10) ◽  
pp. 1287-1289
Author(s):  
F. C. Moon ◽  
E. H. Dowell
Keyword(s):  
Structural Dynamics ◽  
Optimization Problems ◽  
Acoustic Noise ◽  
Flexible Structures ◽  
Computer Software ◽  
Nonlinear Problems ◽  
Great Promise ◽  
Bluff Bodies ◽  
Building Structures ◽  
Dynamics And Control

While much of the linear theory of structural dynamics has been codified in numerous computer software, important problems remain such as inverse methods (modal synthesis or system identification) and optimization problems. Nonlinear problems, however, are a fertile ground for new research, especially those involving large deformations (e.g., crash simulation) and material nonlinearities. Structure interaction problems will continue to be a fruitful area of research including fluid-structure dynamics and interaction with acoustic noise, thermal fields, soils, and electromagnetic forces. For example, new knowledge about unsteady flows around bluff bodies is needed to make significant progress with dynamic interaction problems with bridge and building structures in unsteady winds. A new field which shows great promise for application is the theory of feedback control of flexible structures. Advances in this area could pay off in near-space engineering and robotics. The training of new researchers with backgrounds in both structural dynamics and control theory and experience is a high priority for the control-structure field, however.

scholarly journals Intelligent Total Transportation Management System for Future Smart Cities

2020 ◽  
Vol 10 (24) ◽  
pp. 8933
Author(s):  
Dinh Dung Nguyen ◽  
József Rohács ◽  
Dániel Rohács ◽  
Anita Boros
Keyword(s):  
Public Transport ◽  
Management System ◽  
Optimization Problems ◽  
Smart Cities ◽  
System Optimization ◽  
Preliminary Evaluation ◽  
Total System ◽  
Sensors And Actuators ◽  
Total Energy Consumption ◽  
Transportation Management

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

On a Successive Approximation Technique in Solving Some Control System Optimization Problems

1963 ◽  
Vol 85 (2) ◽  
pp. 177-180 ◽  
Author(s):  
Masanao Aoki
Keyword(s):  
Successive Approximation ◽  
Optimization Problems ◽  
Structural Information ◽  
System Optimization ◽  
Extremal Problems ◽  
Approximation Technique ◽  
Minimizing Sequences ◽  
Approximation Techniques ◽  
Control Optimization ◽  
Computational Procedures

It has been realized for some time that most realistic optimization problems defy analytical solutions in closed forms and that in most cases it is necessary to resort to judicious combinations of analytical and computational procedures to solve problems. For example, in many optimization problems, one is interested in obtaining structural information on optimal and “good” suboptimal policies. Very often, various analytical as well as computational approximation techniques need be employed to obtain clear understandings of structures of policy spaces. The paper discusses a successive approximation technique to construct minimizing sequences for functionals in extremal problems, and the techniques will be applied, to a class of control optimization problems given by: Minv  J(v)=Minv  ∫01g(u.v)dt, where du/dt = h(u, v), h(u, v) linear in u and v, and where u and v are, in general, elements of Banach spaces. In Section 2, the minimizing sequences are constructed by approximating g(u, v) by appropriate quadratic expressions with linear constraining differential equations. It is shown that under the stated conditions the functional values converge to the minimal value monotonically. In Section 3, an example is included to illustrate some of the techniques discussed in the paper.

Building autonomic systems using collaborative reinforcement learning

2006 ◽  
Vol 21 (3) ◽  
pp. 231-238 ◽  
Author(s):  
JIM DOWLING ◽  
RAYMOND CUNNINGHAM ◽  
EOIN CURRAN ◽  
VINNY CAHILL
Keyword(s):  
Reinforcement Learning ◽  
Ad Hoc ◽  
Optimization Problems ◽  
System Optimization ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Coordination Model ◽  
Routing Performance ◽  
Multi Agent ◽  
Unpredictable Environment

This paper presents Collaborative Reinforcement Learning (CRL), a coordination model for online system optimization in decentralized multi-agent systems. In CRL system optimization problems are represented as a set of discrete optimization problems, each of whose solution cost is minimized by model-based reinforcement learning agents collaborating on their solution. CRL systems can be built to provide autonomic behaviours such as optimizing system performance in an unpredictable environment and adaptation to partial failures. We evaluate CRL using an ad hoc routing protocol that optimizes system routing performance in an unpredictable network environment.

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