scholarly journals A Novel Fast Decision-making Method Based on Integrated Intelligent Technologies for Wind Disaster Management of Energy Manufacture

CONVERTER ◽  
2021 ◽  
pp. 169-190
Author(s):  
Baishang Zhang, Et al.
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Evidence Theory ◽  
Nonlinear Problems ◽  
Case Based Reasoning ◽  
Genetic Operators ◽  
Electricity Grid ◽  
Fast Decision

Energy manufacture is very important to all of industries. Typhoons hit the power grid in China's southeast coastal areas frequently for the past few years, seriously affecting the industries’ operation. Therefore, making-decision of wind damage management for nation's electricity grid in real time is an urgent subject to be studied. The traditional decision making method is easy to be implemented, but is not proper for dealing with nonlinear problems in complex systems. The purpose of this article is to design a fast decision making framework for accomplishing fast decision making by making combination Case-Based Reasoning (CBR) with Rule-Based Reasoning (RBR), Genetic Algorithm (GA), which is called fast decision making method based on integrated intelligent technologies (FDMMBIIT). Compared with traditional methods, FDMMBIIT completes case adaptation with BPNN after extending case base. To make the decision-making more accurate, this article updated the multi-object genetic algorithm (MOGA) with adaptive genetic operators and a selection method by using the fitness function. Likewise, BPNN is improved with adaptive simulated annealing algorithm (ASAA), which is named as BPNNASAA. More important, this paper expands the frame theory by integrating it to the D/S evidence theory, exploring a novel solution to representing cases with incomplete information. The case of Guangdong demonstrates FDMMBIIT achieves better decision-making performance for storm disaster emergency management.

scholarly journals Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Wang ◽  
Bi-hua Zhou ◽  
Shu-dao Zhou ◽  
Zheng Sheng
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Chaotic Time Series ◽  
Genetic Operators ◽  
Genetic Simulated Annealing Algorithm ◽  
Function Expression ◽  
Annealing Operation

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

2018 ◽  
Vol 7 (4.33) ◽  
pp. 130
Author(s):  
Atiqa Zukreena Zakuan ◽  
Shuzlina Abdul-Rahman ◽  
Hamidah Jantan ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Succession Planning ◽  
Fitness Function ◽  
Academic Staff ◽  
Fitness Value ◽  
Selection Decision ◽  
The University ◽  
Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.    

scholarly journals Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

2017 ◽  
Vol 44 (11) ◽  
pp. 945-955 ◽  
Author(s):  
Mansour Fakhri ◽  
Ershad Amoosoltani ◽  
Mona Farhani ◽  
Amin Ahmadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
Crumb Rubber ◽  
Concrete Pavement ◽  
Genetic Operators ◽  
Efficiency Criterion ◽  
Roller Compacted Concrete

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

Hardware Implementation of a Genetic Algorithm for Motion Path Planning

2016 ◽  
pp. 250-275
Author(s):  
Imbaby I. Mahmoud ◽  
May Salama ◽  
Asmaa Abd El Tawab Abd El Hamid
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Fitness Function ◽  
Random Number Generation ◽  
Motion Path ◽  
Genetic Operators ◽  
Fitness Evaluation ◽  
Evaluation Task ◽  
Evaluation Module ◽  
The Individual

The aim of this chapter is to investigate the hardware (H/W) implementation of Genetic Algorithm (GA) based motion path planning of robot. The potential benefit of using H/W implementation of genetic algorithm is that it allows the use of huge parallelism which is suited to random number generation, crossover, mutation and fitness evaluation. The operation of selection and reproduction are basically problem independent and involve basic string manipulation tasks. The fitness evaluation task, which is problem dependent, however proves a major difficulty in H/W implementation. Another difficulty comes from that designs can only be used for the individual problem their fitness function represents. Therefore, in this work the genetic operators are implemented in H/W, while the fitness evaluation module is implemented in software (S/W). This allows a mixed hardware/software approach to address both generality and acceleration. Moreover, a simple H/W implementation for fitness evaluation of robot motion path planning problem is discussed.

Optimal Design of Pre-Forging for Gear Blank Using BP Neural Network and Genetic Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 801-806
Author(s):  
Ming Yue Zhang ◽  
Xin Yun Wang ◽  
Ju Chen Xia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fitness Function ◽  
Back Propagation ◽  
Nonlinear Problems ◽  
Back Propagation Neural Network ◽  
Highly Nonlinear ◽  
Die Stress ◽  
Trial And Error Method ◽  
Quality Of Products

The design of pre-forging is very important during multistage forging of producing gear blank. It directly affects the behavior of metal flowing, filled situation of die cavity of finish forging, quality of products and die life. Most designs of pre-forging for gear blank are based on trial and error method. This paper presents a suitable method for practical designation of pre-forging for gear blank by proposing an improved algorithm, which combines Back Propagation Neural Network and Genetic Algorithm. Firstly, the mathematical model between the size parameters of pre-forging and forming force and maximum die stress of finish forging was established by using Back Propagation Neural Network which has the feature of processing highly nonlinear problems. Secondly, the established model was set as the fitness function of Genetic Algorithm. At last, the most superior pre-forging shape and the size parameters were solved by using the Genetic Algorithm with the function of overall situation optimization. These can lead to lower cost and time in the stages of designing pre-forging for gear blank.

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Location Routing ◽  
Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

scholarly journals Thinking Fast and Slow: A CBR Perspective

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Srashti Kaurav ◽  
Devi Ganesan ◽  
Deepak P ◽  
Sutanu Chakraborti
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Human Cognition ◽  
Case Based Reasoning ◽  
Machine Learning Model ◽  
Intuitive Decision Making ◽  
Thinking Process ◽  
Real World Datasets ◽  
First Time ◽  
Fast Decision

In a path-breaking work, Kahneman characterized human cognition as a result of two modes of operation, Fast Thinking and Slow Thinking. Fast thinking involves quick, intuitive decision making and slow thinking is deliberative conscious reasoning. In this paper, for the first time, we draw parallels between this dichotomous model of human cognition and decision making in Case-based Reasoning (CBR). We observe that fast thinking can be operationalized computationally as the fast decision making by a trained machine learning model, or a parsimonious CBR system that uses few attributes. On the other hand, a full-fledged CBR system may be seen as similar to the slow thinking process. We operationalize such computational models of fast and slow thinking and switching strategies, as Models 1 and 2. Further, we explore the adaptation process in CBR as a slow thinking manifestation, leading to Model 3. Through an extensive set of experiments on real-world datasets, we show that such realizations of fast and slow thinking are useful in practice, leading to improved accuracies in decision-making tasks.

Automatic Generation of Design Scheme Based on Improved Segmentation Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1655-1660
Author(s):  
Ji Wen Chen ◽  
Bo Huang ◽  
Bo Pang ◽  
Li Li
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Information Seeking ◽  
Structural Information ◽  
Fitness Function ◽  
Automatic Generation ◽  
Genetic Operators ◽  
Structure Information ◽  
Design Scheme

Research on composition and quantitative representation of the function unit structural information, seeking the combination process effectively, is the key technology to generate product design schemes. Full life cycle assessment properties of structure is introduced into expression of design scheme. The gene model of life cycle assessment properties of structure is established, and the variable length coding is converted to equal length coding to realize the quantitative representation of structure information. The fitness function is established for life cycle assessment of structure with Analytic Hierarchy Process. The improved segmentation genetic algorithm is studied. The gene sequence of design scheme is segmented. Genetic operators such as across and mutate is designed for structure information the segmented gene fragment. Life cycle assessment gene of structure as attribute does not participate in the genetic operation. Design schemes automatic generation is achieved based on improved segmented genetic operators, reflecting life cycle assessment properties of design schemes.

scholarly journals Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gilseung Ahn ◽  
Sun Hur
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Real Time ◽  
Fitness Function ◽  
Cloud Manufacturing ◽  
Genetic Operators ◽  
Real Time Scheduling ◽  
Chromosome Representation ◽  
Time Scheduling ◽  
The Mathematical Model

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

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