Methodology and Case Study of Hybrid Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

2007 ◽  
Author(s):  
Qing Yang ◽  
Shengchao Ding
Keyword(s):  
Evolutionary Algorithm ◽  
Numerical Optimization

scholarly journals Optimal Operation of the Voltage-Doubler Boost Converter through an Evolutionary Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 423
Author(s):  
Cesar Ibarra-Nuño ◽  
Alma Rodríguez ◽  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Juan M. Ramirez ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Numerical Optimization ◽  
Boost Converter ◽  
Optimal Operation ◽  
Input Current ◽  
Voltage Gain ◽  
Duty Cycles ◽  
Switching Functions ◽  
Voltage Doubler ◽  
Current Ripple

This manuscript presents the numerical optimization (through a mathematical model and an evolutionary algorithm) of the voltage-doubler boost converter, also called the series-capacitor boost converter. The circuit is driven by two transistors, each of them activated according to a switching signal. In the former operation, switching signals have an algebraic dependence from each other. This article proposes a new method to operate the converter. The proposed process reduces the input current ripple without changing any converter model parameter, only the driving signals. In the proposed operation, switching signals of transistors are independent of each other, providing an extra degree of freedom, but on the other hand, this produces an infinite number of possible combinations of duty cycles (the main parameter of switching signals) to achieve the desired voltage gain. In other words, this leads to a problem with infinite possible solutions. The proposed method utilizes an evolutionary algorithm to determine the switching functions and, at the same time, to minimize the input current ripple of the converter. A comparison made between the former and the proposed operation shows that the proposed process achieves a lower input current ripple while achieving the desired voltage gain.

Incorporation of Preferences in an Evolutionary Algorithm Using an Outranking Relation

2011 ◽  
Vol 2 (1) ◽  
pp. 63-85 ◽  
Author(s):  
Eunice Oliveira ◽  
Carlos Henggeler Antunes ◽  
Álvaro Gomes
Keyword(s):  
Evolutionary Algorithm ◽  
Evolutionary Process ◽  
Computational Effort ◽  
Cognitive Effort ◽  
Point Of View ◽  
Electrical Networks ◽  
Technical Parameters ◽  
Outranking Relation ◽  
Electre Tri

The incorporation of preferences into Evolutionary Algorithms (EA) presents some relevant advantages, namely to deal with complex real-world problems. It enables focus on the search thus avoiding the computation of irrelevant solutions from the point of view of the practical exploitation of results (thus minimizing the computational effort), and it facilitates the integration of the DM’s expertise into the solution search process (thus minimizing the cognitive effort). These issues are particularly important whenever the number of conflicting objective functions and/or the number of non-dominated solutions in the population is large. In EvABOR (Evolutionary Algorithm Based on an Outranking Relation) approaches preferences are elicited from a decision maker (DM) with the aim of guiding the evolutionary process to the regions of the space more in accordance with the DM’s preferences. The preferences are captured and made operational by using the technical parameters of the ELECTRE TRI method. This approach is presented and analyzed using some illustrative results of a case study of electrical networks.

scholarly journals Analysis and Design of Damping Circuit Parameters for LCC Valves Based on Broadband Model

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1059
Author(s):  
Yingjie Tang ◽  
Zheren Zhang ◽  
Zheng Xu
Keyword(s):  
Numerical Optimization ◽  
Low Frequency ◽  
Parameter Design ◽  
Circuit Parameter ◽  
Frequency Model ◽  
Analysis And Design ◽  
Electromagnetic Transient ◽  
High Voltage Direct Current ◽  
Novel Method

Damping circuits are installed inside the converter valve to limit commutation overshoots. They have significant effects on the valve’s turn-off performances, which should be carefully considered in parameter design. First, the calculation models for the turn-off process are discussed, including the conventional low frequency model and the broadband model. Then, it is found that high-frequency equipment parameters have significant effects on the transient valve voltage, which means that the conventional analytical methods based on low-frequency models is not suitable for damping circuit parameter design. The relationships between the turn-off performances and damping circuit parameters have also been analyzed in detail with the broadband model. To achieve better economic efficiency, this paper proposes a novel method for damping circuit parameter optimization, which combines the electromagnetic transient (EMT) calculation and the numerical optimization. Last, the case study is carried out based on a practical ±1100 kV ultra-high-voltage direct-current (UHVDC) transmission project, which proves the reliability and flexibility of the proposed method.

scholarly journals A multi-objective evolutionary algorithm-based soft computing model for educational data mining

2017 ◽  
Vol 12 (1) ◽  
pp. 106-123
Author(s):  
Choo Jun Tan ◽  
Ting Yee Lim ◽  
Chin Wei Bong ◽  
Teik Kooi Liew
Keyword(s):  
Student Achievement ◽  
Evolutionary Algorithm ◽  
Soft Computing ◽  
Real World ◽  
Classification Accuracy ◽  
Content Type ◽  
Computing Model ◽  
Model Based ◽  
Proposed Model

Purpose The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide. Findings The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

scholarly journals The multilevel ant stigmergy algorithm for numerical optimization

2006 ◽  
Vol 19 (2) ◽  
pp. 247-260 ◽  
Author(s):  
Peter Korosec ◽  
Jurij Silc
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Techniques ◽  
Industrial Case Study ◽  
New Approach ◽  
Collective Work ◽  
Better Than

The Multilevel Ant Stigmergy Algorithm (MASA) is a new approach to solving multi-parameter problems based on stigmergy, a type of collective work that can be observed in nature. In this paper we evaluate the performance of MASA regarding its applicability as numerical optimization techniques. The evaluation is performed with several widely used benchmarks functions, as well as on an industrial case study. We also compare the MASA with Differential Evolution, well-known numerical optimization algorithm. The average solution obtained with the MASA was better than a solution recently found using Differential Evolution.

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