scholarly journals Multistage hierarchical optimization problems with multi-criterion objectives

2011 ◽  
Vol 7 (1) ◽  
pp. 103-115 ◽  
Author(s):  
Roxin Zhang ◽  
◽  
Bao Truong ◽  
Qinghong Zhang
Keyword(s):  
Optimization Problems ◽  
Hierarchical Optimization ◽  
Hierarchical Optimization Problems

Subgradient algorithm for split hierarchical optimization problems

2018 ◽  
Vol 34 (3) ◽  
pp. 391-399
Author(s):  
NIMIT NIMANA ◽  
◽  
NARIN PETROT ◽  
◽  
Keyword(s):  
Hilbert Spaces ◽  
Optimization Problems ◽  
Split Feasibility Problem ◽  
Feasibility Problem ◽  
Hierarchical Optimization ◽  
Convergence Results ◽  
Hierarchical Optimization Problems ◽  
Subgradient Algorithm ◽  
The Split Feasibility Problem ◽  
Split Feasibility

In this paper we emphasize a split type problem of some integrating ideas of the split feasibility problem and the hierarchical optimization problem. Working on real Hilbert spaces, we propose a subgradient algorithm for approximating a solution of the introduced problem. We discuss its convergence results and present a numerical example.

scholarly journals Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

2019 ◽  
Vol 9 (4) ◽  
pp. 624 ◽  
Author(s):  
Tao Rui ◽  
Guoli Li ◽  
Qunjing Wang ◽  
Cungang Hu ◽  
Weixiang Shen ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Stackelberg Game ◽  
Optimization Method ◽  
Economic Benefits ◽  
Power Grids ◽  
Mixed Integer ◽  
Hierarchical Optimization ◽  
Load Demand ◽  
Control Framework ◽  
Two Stages

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

Optimization of Machine System Designs Using Hierarchical Decomposition Based on Criteria Influence

2002 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui ◽  
Shigeaki Komori
Keyword(s):  
Design Optimization ◽  
Structural Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Hierarchical Level ◽  
Hierarchical Optimization ◽  
Pareto Optimum ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Basic Characteristics

Machine product designs routinely have so many mutually related characteristics that common design optimization methods often result in an unsatisfactory local optimum solution. In order to overcome this problem, this paper proposes a design optimization method based on the clarification of the conflicting and cooperative relationships among the characteristics. First of all, each performance characteristic is divided into simpler basic characteristics according to its structure. Next, the relationships among the basic characteristics are systematically identified and clarified. Then, based on this clarification, the optimization problem is expressed using hierarchical constructions of these basic characteristics and design variables related to the most basic characteristics. Finally, an optimization strategy and detailed hierarchical optimization procedures are constructed, after clarifying the influence levels of each basic characteristic upon the objective functions and setting a core characteristic for the product under consideration. Here, optimizations are sequentially repeated starting with the basic optimal unit group at the bottom hierarchical level and proceeding to higher levels by the hierarchical genetic algorithms. Then, the Pareto optimum solutions at the top hierarchical level are obtained. With the proposed optimization methods, optimization can be more easily applied after the optimization problems have been simplified by decomposition. In doing so, the volume of design spaces for each optimization is reduced, while useful and unique rules and laws may be uncovered. The optimization strategy expressed by the hierarchical structures can be used for the optimization of similar product designs, which realize these breakthroughs, yielding improved product performances. The proposed method is applied to a machine-tool structural model.

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