A new objective function for super-resolution deconvolution of microscopy images by means of a genetic algorithm

2020 ◽  
Author(s):  
Axel M. Lacapmesure ◽  
Sandra Martínez ◽  
Oscar E. Martínez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Super Resolution ◽  
Microscopy Images

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

PERFORMANCE EVALUATION OF GENETIC DISTRIBUTED FUZZY CONTROLLERS FOR MULTI-PART-TYPE PRODUCTION LINE

2007 ◽  
Vol 06 (02) ◽  
pp. 115-128
Author(s):  
SEYED MAHDI HOMAYOUNI ◽  
TANG SAI HONG ◽  
NAPSIAH ISMAIL
Keyword(s):  
Genetic Algorithm ◽  
Performance Evaluation ◽  
Objective Function ◽  
Production Rate ◽  
Production Line ◽  
Production Systems ◽  
Membership Functions ◽  
Part Type ◽  
High Penalty ◽  
Single Part

Genetic distributed fuzzy (GDF) controllers are proposed for multi-part-type production line. These production systems can produce more than one part type. For these systems, "production rate" and "priority of production" for each part type is determined by production controllers. The GDF controllers have already been applied to single-part-type production systems. The methodology is illustrated and evaluated using a two-part-type production line. For these controllers, genetic algorithm (GA) is used to tune the membership functions (MFs) of GDF. The objective function of the GDF controllers minimizes the surplus level in production line. The results show that GDF controllers can improve the performance of production systems. GDF controllers show their abilities in reducing the backlog level. In production systems in which the backlog has a high penalty or is not allowed, the implementation of GDF controllers is advisable.

Wave Energy Converter Array Optimization: A Review of Current Work and Preliminary Results of a Genetic Algorithm Approach Introducing Cost Factors

2015 ◽  
Author(s):  
Chris Sharp ◽  
Bryony DuPont
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Wave Energy ◽  
Reference Model ◽  
A Priori ◽  
System Optimization ◽  
Primary Energy ◽  
Close Relative ◽  
Cost Information ◽  
Multiple Test

Currently, ocean wave energy is a novel means of electricity generation that is projected to potentially serve as a primary energy source in coastal areas. However, for wave energy converters (WECs) to be applicable on a scale that allows for grid implementation, these devices will need to be placed in close relative proximity to each other. From what’s been learned in the wind industry of the U.S., the placement of these devices will require optimization considering both cost and power. However, current research regarding optimized WEC layouts only considers the power produced. This work explores the development of a genetic algorithm (GA) that will create optimized WEC layouts where the objective function considers both the economics involved in the array’s development as well as the power generated. The WEC optimization algorithm enables the user to either constrain the number of WECs to be included in the array, or allow the algorithm to define this number. To calculate the objective function, potential arrays are evaluated using cost information from Sandia National Labs Reference Model Project, and power development is calculated such that WEC interaction affects are considered. Results are presented for multiple test scenarios and are compared to previous literature, and the implications of a priori system optimization for offshore renewables are discussed.

Optimization of a Torus-Shaped Wave Energy Converter Attached to a Hinged Arm

2021 ◽  
Author(s):  
Mojtaba Kamarlouei ◽  
Thiago S. Hallak ◽  
Jose F. Gaspar ◽  
Miguel Calvário ◽  
C. Guedes Soares
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Prime Mover ◽  
Optimal Result ◽  
Energy Converter ◽  
Sea State ◽  
Absorbed Power ◽  
Spring System

Abstract This paper presents the adaptation of a torus wave energy converter prime mover to an onshore or nearshore fixed platform, by a hinged arm. An optimization code is developed to obtain the best torus and arm geometry, as well as the power take-off parameters, taking as objective function the maximization of total wave absorbed power. In this paper, the power take-off system is modelled as a simplified damper and spring system, where the parameters are optimized for the phase control of the wave energy converter in each sea state, whereas the optimization process is performed with a genetic algorithm. Finally, the optimal result for the productive sea state indicates that the absorbed power is relatively considerable while a better survivability performance is expected from a torus wave energy converter compared to a conventional truncated prime mover.

scholarly journals Optimized Placement of Connecting the Distributed Generationswork Stand Alone to Improve the Distribution Systems Reliability

2013 ◽  
Vol 64 (2) ◽  
pp. 76-83
Author(s):  
Hamed Hashemi-Dezaki ◽  
Ali Agheli ◽  
Behrooz Vahidi ◽  
Hossein Askarian-Abyaneh
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
System Reliability ◽  
Distribution Systems ◽  
Analytical Approach ◽  
Distributed Generations ◽  
Joint Point ◽  
Cost Of Energy ◽  
Optimum Solution ◽  
The Cost

The use of distributed generations (DGs) in distribution systems has been common in recent years. Some DGs work stand alone and it is possible to improve the system reliability by connecting these DGs to system. The joint point of DGs is an important parameter in the system designing. In this paper, a novel methodology is proposed to find the optimum solution in order to make a proper decision about DGs connection. In the proposed method, a novel objective function is introduced which includes the cost of connector lines between DGs and network and the cost of energy not supplied (CENS) savings. Furthermore, an analytical approach is used to calculate the CENS decrement. To solve the introduced nonlinear optimization programming, the genetic algorithm (GA) is used. The proposed method is applied to a realistic 183-bus system of Tehran Regional Electrical Company (TREC). The results illustrate the effectiveness of the method to improve the system reliability by connecting the DGs work stand alone in proper placements.

scholarly journals Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices

2018 ◽  
Vol 29 (1) ◽  
pp. 1151-1165
Author(s):  
Wael Almadhoun ◽  
Mohammad Hamdan
Keyword(s):  
Genetic Algorithm ◽  
Software Development ◽  
Objective Function ◽  
The Self ◽  
Performance Characteristics ◽  
Team Size ◽  
Agile Practices ◽  
Team Characteristics ◽  
Agile Software ◽  
Self Organizing

Abstract In agile software processes, the issue of team size is an important one. In this work we look at how to find the optimal, or near optimal, self-organizing team size using a genetic algorithm (GA) which considers team communication efforts. Communication, authority, roles, and learning are the team’s performance characteristics. The GA has been developed according to performance characteristics. A survey was used to evaluate the communication weight factors, which were qualitatively assessed and used in the algorithm’s objective function. The GA experiments were performed in different stages: each stage results were tested and compared with the previous results. The results show that self-organizing teams of sizes ranged from five to nine members scored more. The model can be improved by adding other team characteristics, i.e. software development efforts and costs.

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