Time Inconsistency and Self-Control Optimization Problems: Progress and Challenges

2017 ◽  
pp. 33-42
Author(s):  
Yun Shi ◽  
Xiangyu Cui
Keyword(s):  
Optimization Problems ◽  
Time Inconsistency ◽  
Self Control ◽  
Control Optimization

Enhancement of SPSA algorithm performance using reservoir quality maps: Application to coupled well placement and control optimization problems

2020 ◽  
Vol 189 ◽  
pp. 106984 ◽  
Author(s):  
Behzad Pouladi ◽  
Abdorreza Karkevandi-Talkhooncheh ◽  
Mohammad Sharifi ◽  
Shahab Gerami ◽  
Alireza Nourmohammad ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Reservoir Quality ◽  
Well Placement ◽  
Algorithm Performance ◽  
Control Optimization ◽  
And Control ◽  
Quality Maps

scholarly journals Symmetry-Breaking for Airflow Control Optimization of an Oscillating-Water-Column System

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 895 ◽  
Author(s):  
Fares M’zoughi ◽  
Izaskun Garrido ◽  
Aitor J. Garrido
Keyword(s):  
Symmetry Breaking ◽  
Water Column ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Time ◽  
Pso Algorithm ◽  
Search Space ◽  
Oscillating Water Column ◽  
Column System ◽  
Control Optimization

Global optimization problems are mostly solved using search methods. Therefore, decreasing the search space can increase the efficiency of their solving. A widely exploited technique to reduce the search space is symmetry-breaking, which helps impose constraints on breaking existing symmetries. The present article deals with the airflow control optimization problem in an oscillating-water-column using the Particle Swarm Optimization (PSO). In an effort to ameliorate the efficiency of the PSO search, a symmetry-breaking technique has been implemented. The results of optimization showed that shrinking the search space helped to reduce the search time and ameliorate the efficiency of the PSO algorithm.

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.

Integrated Plant, Observer, and Controller Optimization With Application to Combined Passive/Active Automotive Suspensions

2003 ◽  
Author(s):  
Hosam K. Fathy ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
Optimization Problems ◽  
System Optimization ◽  
Optimization Strategy ◽  
Guarantee System ◽  
Control Optimization ◽  
Car Suspension ◽  
State Measurement ◽  
Full State ◽  
And Control ◽  
Original Derivation

The plant and control optimization problems are coupled in the sense that solving them sequentially does not guarantee system optimality. This paper extends previous studies of this coupling by relaxing their assumption of full state measurement availability. An original derivation of first-order necessary conditions for plant, observer, controller, and combined optimality furnishes coupling terms quantifying the underlying trilateral coupling. Special scenarios where the problems decouple are pinpointed, and a nested optimization strategy that guarantees system optimization strategy that guarantees system optimality is adopted otherwise. Applying these results to combined passive/active car suspension optimization produces a suspension design outperforming its passive, active, and sequentially optimized passive/active counterparts.

6. Taking time

2017 ◽  
pp. 67-81
Author(s):  
Michelle Baddeley
Keyword(s):  
Life Cycle ◽  
Mental Accounting ◽  
Time Inconsistency ◽  
Self Control ◽  
Best Interests ◽  
Policy Makers ◽  
Present Bias ◽  
Life Cycle Models ◽  
Over Time

Often our everyday decisions unfold over time and what we want today is not always consistent with what we might want tomorrow. Understanding why many people do not behave in a way that is consistent with their own long-term best interests is a key challenge for behavioural economists and policy-makers. ‘Taking time’ explains how humans (and animals) suffer from present bias: we have a disproportionate preference for smaller, immediate rewards over delayed, larger rewards—a reflection of underlying time inconsistency. It considers the intertemporal tussle between our patient and impatient selves, pre-commitment strategies, and self-control. The behavioural life cycle models of choice bracketing, framing, and mental accounting are also discussed.

PERFORMANCE COMPARISON OF DIFFERENTIAL EVOLUTION AND SOMA ON CHAOS CONTROL OPTIMIZATION PROBLEMS

2012 ◽  
Vol 22 (08) ◽  
pp. 1230025 ◽  
Author(s):  
ROMAN SENKERIK ◽  
DONALD DAVENDRA ◽  
IVAN ZELINKA ◽  
ZUZANA OPLATKOVA ◽  
ROMAN JASEK
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Chaos Control ◽  
Optimization Problems ◽  
Performance Comparison ◽  
Quality Of Results ◽  
Control Optimization ◽  
Extreme Sensitivity ◽  
Small Change

This paper compares the performance of Differential Evolution (DE) with Self-Organizing Migrating Algorithm (SOMA) in the task of optimization of the control of chaos. The main aim of this paper is to show that evolutionary algorithms like DE are capable of optimizing chaos control, leading to satisfactory results, and to show that extreme sensitivity of the chaotic environment influences the quality of results on the selected EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two-dimensional Henon map is used and two complex targeting cost functions are tested. The evolutionary algorithms, DE and SOMA were applied with different strategies. For each strategy, repeated simulations demonstrate the robustness of the used method and constructed CF. Finally, the obtained results are compared with previous research.

A Comprehensive Evaluation of Dimension-Reduction Approaches in Optimization of Well Rates

SPE Journal ◽  
2019 ◽  
Vol 24 (03) ◽  
pp. 912-950
Author(s):  
Abeeb A. Awotunde
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
Comprehensive Evaluation ◽  
Time Frame ◽  
Decomposition Approach ◽  
Piecewise Constant ◽  
Well Control ◽  
Field Development ◽  
Control Optimization ◽  
Better Than

Summary This paper evaluates the effectiveness of six dimension-reduction approaches. The approaches considered are the constant-control (Const) approach, the piecewise-constant (PWC) approach, the trigonometric approach, the Bessel-function (Bess) approach, the polynomial approach, and the data-decomposition approach. The approaches differ in their mode of operation, but they all reduce the number of parameters required in well-control optimization problems. Results show that the PWC approach performs better than other approaches on many problems, but yields widely fluctuating well controls over the field-development time frame. The trigonometric approach performed well on all the problems and yields controls that vary smoothly over time.

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