Modern Methods of Optimization in Models of Hydrothermal Coordination and Emission of Contaminating Particles in Power-Generating Plants

2020 ◽  
pp. 353-376
Author(s):  
Gilberto Pérez Lechuga ◽  
Ugo Fiore ◽  
Francisco Martínez Sánchez
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Large Scale ◽  
Random Search ◽  
Economic Dispatch ◽  
Mathematical Optimization ◽  
Hydrothermal Coordination ◽  
Modern Methods ◽  
Hydroelectric Plants

The problem of hydrothermal coordination is a classic example of nonlinear and large-scale dynamic mathematical optimization that involves the problem of the economic dispatch plus other constraints associated with the coordination of a pair of systems integrated into a network (i.e., the production of energy from thermoelectric and hydroelectric plants). A large number of methods and models that represent this problem, from classical methods of optimization to modern methods based on metaheuristics, exist. In this chapter, the classic structure of the problem is analyzed and new constraints are proposed, including those based on the level of customer service. Some recent modern formulations of the model are shown, and the existing literature is reviewed showing the modern methods of optimization based on heuristics. The authors illustrate it by optimizing an instance consisting of three thermo plants and three hydro plants through random search and genetic algorithm.

scholarly journals Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1297
Author(s):  
Md. Shabiul Islam ◽  
Most Tahamina Khatoon ◽  
Kazy Noor-e-Alam Siddiquee ◽  
Wong Hin Yong ◽  
Mohammad Nurul Huda
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Real Time ◽  
Large Scale ◽  
Fitness Function ◽  
Random Search ◽  
Linear Equations ◽  
Simultaneous Equations ◽  
Function Evaluation ◽  
Solution Sets

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

Large-scale economic dispatch by genetic algorithm

1995 ◽  
Vol 10 (4) ◽  
pp. 1919-1926 ◽  
Author(s):  
Po-Hung Chen ◽  
Hong-Chan Chang
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Economic Dispatch

scholarly journals Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization

2014 ◽  
Vol 7 (4) ◽  
pp. 60-74
Author(s):  
Márcio De Oliveira Barros ◽  
Hélio Costa ◽  
Fábio Vitorino Figueiredo ◽  
Ana Regina Cavalcanti Rocha
Keyword(s):  
Genetic Algorithm ◽  
Experimental Study ◽  
Portfolio Selection ◽  
Heuristic Search ◽  
Large Scale ◽  
Random Search ◽  
Project Selection ◽  
Project Portfolio ◽  
Selection Technique ◽  
Project Portfolio Selection

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of the candidate projects, which turns to be unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four real-world project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure (random search). A second experimental study evaluates the best parameter settings to perform the heuristic search. Experimental results show evidence that the project selection technique can be used in large-scale scenarios and that the genetic algorithm presents better results than simpler search strategies.

scholarly journals A Novel Heap based Optimizer for Scheduling of Large-scale Combined Heat and Power Economic Dispatch

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed R. Ginidi ◽  
Abdallah M. Elsayed ◽  
Abdullah M. Shaheen ◽  
Ehab E. Elattar ◽  
Ragab A. El-Sehiemy
Keyword(s):  
Large Scale ◽  
Economic Dispatch ◽  
Combined Heat And Power ◽  
Power Economic

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

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