scholarly journals OPTIMIZATION OF PIECEWISE NON-LINEAR MULTI CONSTRAINED ECONOMIC POWER DISPATCH PROBLEM USING AN IMPROVED GENETIC ALGORITHM

2010 ◽  
pp. 320-326
Author(s):  
B. Padmanabhan ◽  
R. S. SivaKumar ◽  
J. Jasper
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Local Optimum ◽  
Improved Genetic Algorithm ◽  
Non Linear ◽  
Equality And Inequality Constraints ◽  
Loading Effects ◽  
Economic Power Dispatch

In this paper, a more realistic formulation of the Economic Dispatch problem is proposed, which considers practical constraints and non linear characteristics. The proposed ED formulation includes ramp rate limits, valve loading effects, equality and inequality constraints, which usually are found simultaneously in realistic power systems. This paper presents a novel Genetic Algorithm to solve the economic load dispatch (ELD) problem of thermal generators of a power system. This method provides an almost global optimal solution, since they don’t get stuck at local optimum. The proposed method and its variants are validated for the two test systems consisting of 3 and 10 thermal units whose incremental fuel cost functions takes into account the valve-point loading effects.

scholarly journals Reliability optimization on power systems network using genetic algorithm

2020 ◽  
Vol 2 ◽  
pp. 18-29
Author(s):  
A. E. Airoboman ◽  
Emmanuel A. Ogujor
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Inequality Constraints ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Equality And Inequality Constraints ◽  
The Mean ◽  
Mean Time ◽  
Reliability And Availability ◽  
Selection Of

In this study, reliability optimization of a non-linear transmission network using Genetic Algorithm (GA) based optimization approach is presented and proposed. A GA based algorithm was developed for Koko, Guinness, Nekpenekpen, Ikpoba-Dam, Switch station, Etete and GRA 33kV tertiary transmission feeders within Benin Metropolis, Nigeria and was used to determine the optimal performance of the feeders’ reliability and availability through the minimization of downtime and the Mean Time between Failure (MTBF) by the appropriate selection of the objective functions and constraints. The equality and inequality constraints for each feeder on the network were defined, thereafter, codes were written on the Matlab 2016a environment to optimize the selected parameters. The results from the study showed a reduction in downtime of 5.63%, 26.87%, 34.20%, 5.42% 8.37%, 5.18% and 10.97% and an increment increased in MTBF by 4.95%, 19.87%, 4.58%, 3.85%, 4.88%, 5.77% and 13.56% for Guinness, Etete, Nekpenekpen, GRA, Switch station and Ikpoba-Dam feeders respectively. The obtained results, therefore, yielded an average corresponding improvement on the network’s reliability and availability by 1.85% and 2.83% respectively. Conclusively, the desired result reached in this paper validates the robustness of the GA tool in reliability studies. However, conscious effort must be geared concerning the ways and manners the system is operated in order to achieve desired results.

Multi-Objective Optimization Calibration of Spark Advanced Angle Based on NSGA-II Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1959-1962
Author(s):  
Hao Ba ◽  
Bao Mei Qiu ◽  
Pei Pei Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Gasoline Engine ◽  
Optimal Solution ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimal Solution Set

Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Routing Problem

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

scholarly journals Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Zheng-kun Zhang ◽  
Chang-feng Zhu ◽  
Qing-rong Wang ◽  
Jia-shan Yuan
Keyword(s):  
Genetic Algorithm ◽  
Prospect Theory ◽  
Decision Maker ◽  
Transportation Network ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Decision Behavior ◽  
Bidirectional Search ◽  
Robustness Optimization

This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, prospect theory is specially introduced to reflect the subjective decision behavior of decision-maker. Then, a discrete robustness optimization model is proposed with the purpose of the better timeliness and robustness. The model is based on the emergency transportation network with multistorage centers and multidisaster points. In order to obtain the optimal solution, an improved genetic algorithm is designed by introducing a bidirectional search strategy based on a newfangled path cluster to obtain specific paths that connect each storage centers and each disaster points. Finally, a case study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the path cluster with the better timeliness and robustness can be well obtained by using the prospect theory and improved genetic algorithm. The analysis especially reveals that the robustness is correspondent to the risk aversion in prospect theory.

scholarly journals Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1472 ◽  
Author(s):  
Manuel Guerrero ◽  
Raul Baños ◽  
Consolación Gil ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Power Systems ◽  
Community Detection ◽  
North American ◽  
Flow Analysis ◽  
Power Grids ◽  
Improved Genetic Algorithm ◽  
Continental Scale ◽  
The North

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).

Optimization Design of Wind/Photovoltaic Hybrid Power Systems Based on Genetic Algorithms

2013 ◽  
Vol 278-280 ◽  
pp. 1692-1695
Author(s):  
Lu Li ◽  
Zhong Fu Tan
Keyword(s):  
Genetic Algorithm ◽  
Linear System ◽  
Power Systems ◽  
Wind Power ◽  
Hybrid System ◽  
Alternative Energy ◽  
Optimization Design ◽  
Non Linear ◽  
Non Linear System ◽  
And Control

Wind power and solar energy power are clean, abundant and renewable. Wind power and photovoltaic power are important alternative energy in the world, which will contribute to adjusting energy structures and protecting environments. The genetic algorithm has the characteristics of automatic optimization and approaches the simulate stuff illimitably. Also, there has no use for accurate model on questions, which is very suitable in the non-linear system. The wind/photovoltaic hybrid systems consist with wind power generation units, photovoltaic matrix, storage battery, diesel engine and data collection and control. This paper optimized the wind/photovoltaic hybrid system using genetic algorithm. The result showed the efficiency of this algorithm in the design of this kind of non-linear system. On the other hand, this hybrid system is strongly non-linear when is running. Finally, abundant operating expenses and maintains expenses will be saved by using genetic algorithm in its dynamic management according to the change of load, wind power and irradiation.

An optimal force distribution scheme for cooperating multiple robot manipulators

Robotica ◽  
1993 ◽  
Vol 11 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Yong Dal Shin ◽  
Myung Jin Chung
Keyword(s):  
Efficient Method ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Force Distribution ◽  
Weak Point ◽  
Contact Points ◽  
Optimal Force ◽  
Distribution Scheme ◽  
Equality And Inequality Constraints ◽  
Optimal Force Distribution

SUMMARYIn this paper, we suggest an optimal force distribution scheme by weak point force minimization and we also present an efficient method to solve the problem. The concept of a weak point is a generalized one which is applicable to any points of interest, as well as joints or contact points between end-effectors and an object. The problem is formulated by a quadratic objective function of the forces exerted at weak points subject to the linear equality and inequality constraints, and its optimal solution is obtained by an efficient method. As regards the solution of the problem, the original problem is reformulated to a reduced order dual problem after the equality constraints are eliminated by force decomposition.

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