scholarly journals STUDIES OF JOINT WORK OF GENETIC ALGORITHM AND COORDINATE SEARCH ALGORITHM TO OPTIMIZE TEMPERATURE OF HEATED INDOOR AREAS

2019 ◽  
pp. 59-69
Author(s):  
Alexander Petrovich Shuravin ◽  
Sergey Valentinovich Vologdin
Keyword(s):  
Genetic Algorithm ◽  
Thermal Energy ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Thermal Power ◽  
Mathematical Formulation ◽  
Gradient Methods ◽  
Optimization Methods ◽  
Search Method ◽  
Component Element

The article is focused on studying optimization algorithms that are relevant both for solving applied problems and for studying the artificial intelligence in general. Optimization methods are used to solve environmental problems including the issues of energy saving. It is important to solve problems of optimizing the thermo-hydraulic modes of buildings (as a part of the “Smart City” project), in particular, problems of eliminating temperature imbalance in terms of saving thermal energy and improving the microclimate in apartments. There is shown a mathematical formulation of the problem of optimizing the temperature modes of the indoor areas using adjustable devices. A hybrid algorithm applied to solve the problem has been described. The considered algorithm combines two optimization methods: a coordinate search method and a genetic algorithm. Thus, the stochastic component (element of the genetic algorithm) and the gradient component (element of the coordinate search method) are used in the hybrid algorithm. A description of the above algorithms is given including the mathematical apparatus used and the design formulas. The results of the numerical experiment for the suggested algorithm are presented. These results are compared with the results of applying the genetic algorithm and the method of coordinates search separately. There has been confirmed the hypothesis that in order to increase the efficiency of solving the considered class of problems, it is necessary to combine the genetic algorithm and gradient methods. At the same time, it has been inferred that in cases of low thermal power of radiators, optimization of the hydraulic resistance of valves is not sufficient, thermal insulation measures and replacement of radiators are also required. The practical value of the work lies in the possibility of solving the problem of saving thermal energy in the housing and communal services system.

scholarly journals Comparison of the characteristics of the genetic algorithm and the method of coordinates search for optimization of temperature modes indoor areas

2019 ◽  
pp. 260-270
Author(s):  
A P Shuravin ◽  
S V Vologdin
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Computational Experiment ◽  
Mathematical Formulation ◽  
Computational Cost ◽  
Optimization Methods ◽  
Search Method ◽  
Mathematical Apparatus ◽  
Temperature Mode ◽  
Project Smart

The article substantiates the relevance of optimization algorithms research for solving various applied problems and for the science of artificial intelligence. The need to solve problems of optimizing the thermal-hydraulic modes of buildings (as part of the project "Smart City") is explained. The paper presents a mathematical formulation of the problem of optimizing the temperature mode of rooms using adjustable devices. Existing work provides two methods for solving the posed problem. They are the coordinates search method and the genetic algorithm. The article contains the description of the above mentioned algorithms (including the mathematical apparatus used). The results of the computational experiment (for the considered optimization methods) are presented. These experimental results show that the genetic algorithm provides better optimization results than the coordinates search method, but it has a large computational cost. The hypothesis was confirmed that in order to increase the efficiency of solving the considered class of problems it is necessary to combine the genetic algorithm and the coordinates search method.

scholarly journals Exergoeconomic optimization of a thermal power plant using particle swarm optimization

2013 ◽  
Vol 17 (2) ◽  
pp. 509-524 ◽  
Author(s):  
Axel Groniewsky
Keyword(s):  
Power Plant ◽  
Energy Conversion ◽  
Power Plants ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Algorithm ◽  
Thermal Power ◽  
Optimization Methods ◽  
Conversion System ◽  
Energy Conversion System

The basic concept in applying numerical optimization methods for power plants optimization problems is to combine a State of the art search algorithm with a powerful, power plant simulation program to optimize the energy conversion system from both economic and thermodynamic viewpoints. Improving the energy conversion system by optimizing the design and operation and studying interactions among plant components requires the investigation of a large number of possible design and operational alternatives. State of the art search algorithms can assist in the development of cost-effective power plant concepts. The aim of this paper is to present how nature-inspired swarm intelligence (especially PSO) can be applied in the field of power plant optimization and how to find solutions for the problems arising and also to apply exergoeconomic optimization technics for thermal power plants.

scholarly journals HYBRID NSGA-II OPTIMIZATION FOR IMPROVING THE THREE-TERM BP NETWORK FOR MULTICLASS CLASSIFICATION PROBLEMS

2015 ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
Siti Mariyam Shamsuddin ◽  
Sultan Noman Qasem
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Classification Performance ◽  
Classification Problems ◽  
Nsga Ii ◽  
Bp Network ◽  
Multiobjective Evolutionary Algorithm

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.  

scholarly journals Collaborative Task Assignment for Multi-UAV with Sequence and Time Constrains

2018 ◽  
Vol 36 (5) ◽  
pp. 890-896
Author(s):  
Yaozhong Zhang ◽  
Lan Chen ◽  
Guoqing Shi ◽  
Cao Guo
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Convergence Rate ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Task Assignment ◽  
Task Sequence ◽  
Rapid Convergence ◽  
Collaborative Task ◽  
Multi Uav

In this paper, based on task sequence and time constraint in the SEAD mission of multi-UAV, a heterogeneous multi-UAV cooperative task assignment mathematical model is established. We put forward a hybrid algorithm GSA-GA(gravity search algorithm-genetic algorithm) to resolve cooperative task assignment. The algorithm combines gravity search algorithm and genetic algorithm, improves the coding and decoding methods in updating the position. The simulation result shows that the GSA-GA has rapid convergence rate in solving the cooperative task assignment compared with the classic DPSO algorithm, and has the better resolution.

A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems

2016 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Distributed Resources ◽  
Computing Systems ◽  
Grid Systems ◽  
Job Scheduling Problem

Job scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization (ACO) to show the importance of the proposed algorithm.

Optimal Design of the Stiffener Penstock Structure in a Hydropower Station

2013 ◽  
Vol 438-439 ◽  
pp. 561-564
Author(s):  
Xin Li Bai ◽  
Qi Pei Jia ◽  
Hai Li Su
Keyword(s):  
Genetic Algorithm ◽  
Local Stability ◽  
Hybrid Genetic Algorithm ◽  
Optimization Methods ◽  
External Pressure ◽  
Search Method ◽  
Hydropower Station ◽  
Optimized Design ◽  
Original Design ◽  
Hydropower Stations

In order to optimize the stiffener penstock structure in hydropower stations, the simple genetic algorithm and the direct search method in traditional optimization methods were integrated, and a new hybrid genetic algorithm was obtained. A mathematical model of the stiffener penstock structure was established, and the constraint expressions were presented for global stability of the penstock under external pressure as well as the local stability of the stiffening ring. The corresponding program was developed and applied to a hydropower station. Results show that compared with the original design, the optimized design of rectangular stiffener rings reduces the penstock wall thickness by 8%, saving steel products 12.9%. The economic benefit of optimization is very considerable.

scholarly journals STUDYING CHARACTERISTICS OF GENETIC ALGORITHM FOR OPTIMIZING TEMPERATURE REGIME OF HEATED ROOMS

2020 ◽  
Vol 2020 (4) ◽  
pp. 49-61
Author(s):  
Alexander Petrovich Shuravin ◽  
Sergey Valentinovich Vologdin
Keyword(s):  
Genetic Algorithm ◽  
Energy Saving ◽  
Temperature Regime ◽  
Mathematical Formulation ◽  
Mathematical Optimization ◽  
Heating System ◽  
Optimization Methods ◽  
Actual Condition ◽  
Directed Search

The article discusses the problems of energy-saving, which can be solved by using mathematical optimization methods, and the mathematical optimization algorithms related to these problems. There has been given the review of Russian and foreign works on energy saving and energy optimization. The need for solving the problems of optimizing the thermohydraulic regimes of buildings is explained. There is given the mathematical formulation of the problem of optimizing the temperature regime of indoor areas using adjustable devices and two methods for solving the problem: the directed search method and the genetic algorithm. The above algorithms including the mathematical apparatus are described. The objective function is described as the standard deviation of the temperature of the heated rooms. Various options for using the genetic algorithm have been investigated. A modification of the genetic algorithm is proposed, which allows obtaining the best results in relation to the problem under consideration. The results of a computation experiment for the considered optimization methods are presented. The calculations were carried out for a typical building in Izhevsk under average design conditions, taking into account the actual condition of the enclosing structures, the heating system of the building, and heating devices of indoor areas. A comparative analysis of the convergence of the iterative process for various options for the application of the genetic algorithm and directional search has been carried out. It is concluded that the new modification allows us to improve the quality of the genetic algorithm. The dependence of the convergence of the genetic algorithm on its parameters was investigated and a modification of this algorithm was proposed in relation to the problem of optimizing the thermo-hydraulic modes of heated rooms. The study is of practical value in terms of using the proposed methodology of saving heat energy in the system of housing and communal services.Practical value is the ability to use in the housing and communal services to save thermal energy.

scholarly journals LSA-GA: A Hybrid Algorithm for Solving Economic Emission Dispatch Problem

2020 ◽  
Vol 8 (5) ◽  
pp. 4661-4669
Keyword(s):  
Genetic Algorithm ◽  
Wind Power ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Hybrid Strategy ◽  
Proposal A ◽  
Artificial Neural Network Ann ◽  
Emission Dispatch ◽  
Reproduction Stage ◽  
Economic Emission Dispatch

In this proposal, a hybrid algorithm is conveyed for unraveling Economic Emission Dispatch (EED) issue of the hybrid warm wind power age framework. The hybrid philosophy is a mix of Lightning Search Algorithm (LSA) with Genetic Algorithm (GA). In this, the consolidated endeavor of LSA-GA is utilized for upgrading the warm generators blends dependent on the vulnerability states of wind power. For catching the vulnerability states of wind power, Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) is utilized, so framework guaranteed the breeze power usage at higher. In this manner, the proposed philosophy is utilized for streamlining of the hybrid warm wind power age framework and limited the all out expense of activity. For assessing the adequacy of the proposed hybrid strategy, the six and the ten units of warm age is examined initially without wind power and besides with wind power. The two clashing goals for example fuel cost and outflow are streamlined at a similar interim of time. The proposed procedure is actualized in MATLAB/reproduction stage and results are analyzed by contrasting the got outcome and the consequence of Genetic Algorithm (GA). The examination uncovers that proposed approach has ability to deal with multi-target issues of advancement of electrical force frameworks, more efficiently.

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