Multi-objective optimization strategy based on desirability functions used for electrophoratic separation and quantification of rosiglitazone and glimepiride in plasma and formulations

2011 ◽  
Vol 4 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Mohamed M. Hefnawy ◽  
Maha A. Sultan ◽  
Haya I. Al-Johar ◽  
Mohamed G. Kassem ◽  
Hassan Y. Aboul-Enein
Keyword(s):  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Desirability Functions ◽  
Multi Objective

A Multi-objective Optimization Strategy for Controlling the Structural Properties of Lightweight Mutiphasic PE/EVA Foams

2020 ◽  
pp. 597-601
Author(s):  
Sirwan Ghavami ◽  
Mohammad-Hasan Khademi ◽  
Farkhondeh Hemmati ◽  
Ali Fazeli ◽  
Jamshid Mohammadi-Roshandeh
Keyword(s):  
Structural Properties ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective

A Multi-Objective Optimization Strategy With Priority Ranking of the Design Objectives

1990 ◽  
Author(s):  
Masahide Matsumoto ◽  
Jumpei Abe ◽  
Masataka Yoshimura
Keyword(s):  
Structural Design ◽  
Torsional Rigidity ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Response Level ◽  
Multi Objective ◽  
Lower Priority ◽  
Priority Ranking ◽  
Priority Order ◽  
Motorcycle Frame

Abstract Generally, two types of priorities are considered among multiple objectives in the design of machine structures. One of these objectives is named the “hard objective”, which is the absolutely indispensable design requirement. The other is called the “soft objective”, which has lower priority order. This paper proposes a multi-objective structural optimization strategy with priority ranking of those design objectives. Further, this strategy is demonstrated on the actual example of a motorcycle frame structural design which has three design objectives, (1) an increase in static torsional rigidity, (2) a reduction of dynamic response level, and (3) a decrease in the weight of the motorcycle frame.

scholarly journals An Integration Optimization Strategy of Line Voltage Cascaded Quasi-Z-Source Inverter Parameters Based on GRA-FA

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4391
Author(s):  
Zhiyong Li ◽  
Shiping Pu ◽  
Yougen Chen ◽  
Renyong Wei
Keyword(s):  
Optimization Model ◽  
Power Loss ◽  
Optimal Solution ◽  
Loss Ratio ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Line Voltage ◽  
Multi Objective ◽  
Double Frequency ◽  
Voltage Ripple

Setting reasonable circuit parameters is an important way to improve the quality of inverters, including waveform quality and power loss. In this paper, a circuit system of line voltage cascaded quasi-Z-source inverter (LVC-qZSI) is built. On this basis, the double frequency voltage ripple ratio and power loss ratio are selected as optimization targets to establish a multi-objective optimization model of LVC-qZSI parameters. To simplify the calculation, an integration optimization strategy of LVC-qZSI parameters based on GRA-FA is proposed. Where, the grey relation analysis (GRA) is used to simplify the multi-objective optimization model. In GRA, the main influence factors are selected as optimization variables by considering the preference coefficient. Then, firefly algorithm (FA) is used to obtain the optimal solution of the multi-objective optimization model. In FA, the weights of objective functions are assigned based on the principle of information entropy. The analysis results are verified by simulation. Research results indicate that the optimization strategy can effectively reduce the double frequency voltage ripple ratio and power loss ratio. Therefore, the strategy proposed in this paper has a superior ability to optimize the parameters of LVC-qZSI, which is of great significance to the initial values setting.

A multi-objective optimization strategy for energy plants in Italy

2013 ◽  
Vol 443 ◽  
pp. 955-964 ◽  
Author(s):  
Federica Cucchiella ◽  
Idiano D'Adamo ◽  
Massimo Gastaldi
Keyword(s):  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Energy Plants

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

2015 ◽  
Vol 23 (1) ◽  
pp. 69-100 ◽  
Author(s):  
Handing Wang ◽  
Licheng Jiao ◽  
Ronghua Shang ◽  
Shan He ◽  
Fang Liu
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Space ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

scholarly journals Multi-Objective Optimization in Axial Compressor Design Using a Linked CFD-Solver

2008 ◽  
Author(s):  
Kai Becker ◽  
Martin Lawerenz ◽  
Christian Voß ◽  
Reinhard Mo¨nig
Keyword(s):  
Guide Vane ◽  
Axial Compressor ◽  
Navier Stokes ◽  
Inlet Guide Vane ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flow Information ◽  
Design Variation

In combination with a multi-objective 3D optimization strategy, a linked CFD-solver is presented in this paper, combining 3D-Reynolds-averaged-Navier-Stokes and an inviscid throughflow method. It enables the adjustment of the 3D boundary conditions for any design variation and contains new options for configuring the objective functions. The link is achieved by matching the flow information between both CFD codes in an iterative procedure. Compared to an individual 3D-CFD calculation, the convergence does not take significantly longer. The potential of the linked CFD-solver is demonstrated in a multi-objective optimization for one blade row to be optimized and one operating point at a 3-stage axial compressor with inlet guide vane. Within the optimization, the objective functions are formulated, so that the performance of the axial compressor is enhanced in addition to the improved efficiency of the 3D-cascade.

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