scholarly journals EVOLUTIONARY ALGORITHM INSPIRED BY THE METHODS OF QUANTUM COMPUTER SCIENCES FOR THE IMPROVEMENT OF A NEURAL MODEL OF THE ELECTRIC POWER EXCHANGE

2017 ◽  
Vol 6 (4) ◽  
pp. 343-355 ◽  
Author(s):  
Jerzy Tchórzewski
Keyword(s):  
Evolutionary Algorithm ◽  
Electric Power ◽  
Quantum Computer ◽  
Electrical Energy ◽  
Neural Model ◽  
Initial Population ◽  
Mutation Operators ◽  
Power Exchange ◽  
Using Data ◽  
Crossover And Mutation

The work contains results of research on the possibility to improve the neural model of the Electric Power Exchange (polish: Towarowa Giełda Energii Elektrycznej – TGEE) in MATLAB and Simulink environment using evolutionary algorithm inspired by quantum computer science. The developed artificial neural network was trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy [MWh] as the input quantities in each hour of the 24-hour day, and average prices [PLN/MWh] as output quantities. The obtained model of the exchange system was improved using the evolutionary algorithm, and further improvement in the accuracy of the model by supplementing the evolutionary algorithm using quantum solutions, related to the initial population, crossover and mutation operators, selection, etc. were proposed.

scholarly journals QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

2018 ◽  
Vol 7 (3) ◽  
pp. 201-212
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Electric Power ◽  
Quantum Computer ◽  
Neural Model ◽  
Power Exchange

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

scholarly journals Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-25
Author(s):  
Hui Lu ◽  
Lijuan Yin ◽  
Xiaoteng Wang ◽  
Mengmeng Zhang ◽  
Kefei Mao
Keyword(s):  
Evolutionary Algorithm ◽  
Task Scheduling ◽  
Chaotic Maps ◽  
Initial Population ◽  
Scheduling Problem ◽  
Local Optima ◽  
Test Task ◽  
Multiobjective Evolutionary Algorithm ◽  
Mutation Operators ◽  
Crossover And Mutation

Test task scheduling problem (TTSP) is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES) to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D) in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. The similarity degree of distribution between chaotic maps and the problem is a very essential factor for the application of chaotic maps. In addition, the experiments of comparisons of CMOEA/D and variable neighborhood MOEA/D (VNM) indicate that our algorithm has the best performance in solving the TTSP.

Scheduling Flexible Production Lines with No Intermediate Buffers by Hybrid Algorithms

2012 ◽  
Vol 557-559 ◽  
pp. 2229-2233
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Initial Population ◽  
Flexible Production ◽  
Scheduling Problems ◽  
Production Lines ◽  
Solution Quality ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Better Than

This paper is concerned about the scheduling problems in flexible production lines with no intermediate buffers. The optimization objective is to minimizing the makespan. The mathematical models are presented. Since the problem is NP-hard, a hybrid algorithm, based on genetic algorithm and tabu search, is put forward for solving the models. In this algorithm, the method of generating the initial population is proposed and the crossover and mutation operators, tabu list, and aspiration rule are newly designed. The performance of the hybrid algorithm is compared with that of the traditional genetic algorithm. The computational results show that satisfactory solutions can be obtained by the hybrid algorithm and it performs better than the genetic algorithm in terms of solution quality.

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2007 ◽  
pp. 42-53 ◽  
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

Sequencing Mixed-Model Assembly Lines to Minimize the Variation of Parts Consumption by Hybrid Genetic Algorithms

2012 ◽  
Vol 566 ◽  
pp. 253-256
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Initial Population ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
Sequencing Problems ◽  
Crossover And Mutation ◽  
Mixed Model Assembly

This paper is concerned about the sequencing problems in mixed-model assembly lines. The optimization objective is to minimizing the variation of parts consumption. The mathematical models are put forward. Since the problem is NP-hard, a hybrid genetic algorithm is newly-designed for solving the models. In this algorithm, the new method of forming the initial population is presented, the hybrid crossover and mutation operators are adopted, and moreover, the adaptive probability values for performing the crossover and mutation operations are used. The optimization performance is compared between the hybrid genetic algorithm and a genetic algorithm proposed in early published literature. The computational results show that satisfactory solutions can be obtained by the hybrid genetic algorithm and it performs better in terms of solution’s quality.

scholarly journals A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market

2019 ◽  
Vol 28 ◽  
pp. 01006
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Evolutionary Algorithm ◽  
Neural Model ◽  
Quantum Calculations ◽  
Quantization Method ◽  
Power Exchange ◽  
Artificial Neural ◽  
Numeric Data

The paper contains selected results of research related to the nature and the implementation of the neural model supported by the evolutionary algorithm inspired by quantum calculations for determination of prices at the Polish Power Exchange. Numeric data quoted at the Day Ahead Market in the period of 1st January 2015 to 30th June 2015 were used to train the artificial neural network in the model of the system. Attention was paid to quantization method, dequantization method and the method of quantum calculations. Significant improvement of the neural model supported by the quantum-inspired evolutionary algorithm was obtained compared with the model without quantum inspiration.

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2008 ◽  
pp. 1081-1090
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

scholarly journals Quantum-Inspired Evolutionary Approach for the Quadratic Assignment Problem

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 781 ◽  
Author(s):  
Wojciech Chmiel ◽  
Joanna Kwiecień
Keyword(s):  
Evolutionary Algorithm ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Evolutionary Approach ◽  
Quadratic Assignment ◽  
Mutation Operators ◽  
Crossover And Mutation

The paper focuses on the opportunity of the application of the quantum-inspired evolutionary algorithm for determining minimal costs of the assignment in the quadratic assignment problem. The idea behind the paper is to present how the algorithm has to be adapted to this problem, including crossover and mutation operators and introducing quantum principles in particular procedures. The results have shown that the performance of our approach in terms of converging to the best solutions is satisfactory. Moreover, we have presented the results of the selected parameters of the approach on the quality of the obtained solutions.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

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