The Experts below are selected from a list of 6399 Experts worldwide ranked by ideXlab platform
Prem Kalra - One of the best experts on this subject based on the ideXlab platform.
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A Probabilistic Transmission Expansion Planning Methodology based on Roulette Wheel Selection and Social Welfare
arXiv: Artificial Intelligence, 2012Co-Authors: Neeraj Gupta, Rajiv Shekhar, Prem KalraAbstract:A new probabilistic methodology for transmission expansion planning (TEP) that does not require a priori specification of new/additional transmission capacities and uses the concept of social welfare has been proposed. Two new concepts have been introduced in this paper: (i) Roulette Wheel methodology has been used to calculate the capacity of new transmission lines and (ii) load flow analysis has been used to calculate expected demand not served (EDNS). The overall methodology has been implemented on a modified IEEE 5-bus test system. Simulations show an important result: addition of only new transmission lines is not sufficient to minimize EDNS.
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Congestion management based Roulette Wheel simulation for optimal capacity selection: Probabilistic transmission expansion planning
International Journal of Electrical Power & Energy Systems, 2012Co-Authors: Neeraj Gupta, Rajiv Shekhar, Prem KalraAbstract:This paper proposes a single-stage probabilistic transmission expansion planning (TEP) methodology where a priory specification of candidate transmission lines capacity is not required. The proposed methodology involves: (i) minimization of the sum of the investment, expected demand not served (EDNS), expected generation not served (EGNS) and expected Wheeling loss (EWL) costs to put together economic and reliability analysis on a single platform, (ii) probabilistic contingency analysis using Monte Carlo simulation (MCS), (iii) merit order dispatch of generators to minimizes operational cost of generators, (iv) a non-iterative DC load flow analysis to calculate EDNS, EGNS, and EWL, (v) a novel congestion management based Roulette Wheel selection method to calculate optimum capacity of the transmission lines, and (vi) genetic algorithm (GA) optimization method. The overall methodology has been implemented on a modified IEEE 5-bus test system. Simulations show an important result: addition of only new transmission lines is not sufficient to minimize EDNS.
Rakesh Kumar - One of the best experts on this subject based on the ideXlab platform.
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Alpha Cut based Novel Selection for Genetic Algorithm
International Journal of Computer Applications, 2013Co-Authors: Rakesh Kumar, Girdhar Gopal, Rajesh KumarAbstract:Genetic algorithm (GA) has several genetic operators that can be changed to improve the performance of particular implementations. These operators include selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection like Roulette-Wheel, Rank, and Tournament etc. This paper presents a new selection operator based on alpha cut as in Fuzzy Logic. This is compared with other selection in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that proposed selection outperformed proportional Roulette Wheel, achieving best solution quality with low computing times.
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Blending Roulette Wheel Selection & Rank Selection in Genetic Algorithms
International Journal of Machine Learning and Computing, 2012Co-Authors: Rakesh Kumar, JyotishreeAbstract:Both exploration and exploitation are the techniques employed normally by all the optimization techniques. In genetic algorithms, the Roulette Wheel selection operator has essence of exploitation while rank selection is influenced by exploration. In this paper, a blend of these two selection operators is proposed that is a perfect mix of both i.e. exploration and exploitation. The blended selection operator is more exploratory in nature in initial iterations and with the passage of time, it gradually shifts towards exploitation. The proposed solution is implemented in MATLAB using travelling salesman problem and the results were compared with Roulette Wheel selection and rank selection with different problem sizes. Genetic algorithms are adaptive algorithms proposed by John Holland in 1975 (1) and were described as adaptive heuristic search algorithms (2) based on the evolutionary ideas of natural selection and natural genetics by David Goldberg. They mimic the genetic processes of biological organisms. Genetic algorithm works with a population of individuals represented by chromosomes. Each chromosome is evaluated by its fitness value as computed by the objective function of the problem. The population undergoes transformation using three primary genetic operators - selection, crossover and mutation which form new generation of population. This process continues to achieve the optimal solution. Basic flowchart of genetic algorithm is illustrated in Figure 1.
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blending Roulette Wheel selection rank selection in genetic algorithms
International Journal of Machine Learning and Computing, 2012Co-Authors: Rakesh KumarAbstract:Both exploration and exploitation are the techniques employed normally by all the optimization techniques. In genetic algorithms, the Roulette Wheel selection operator has essence of exploitation while rank selection is influenced by exploration. In this paper, a blend of these two selection operators is proposed that is a perfect mix of both i.e. exploration and exploitation. The blended selection operator is more exploratory in nature in initial iterations and with the passage of time, it gradually shifts towards exploitation. The proposed solution is implemented in MATLAB using travelling salesman problem and the results were compared with Roulette Wheel selection and rank selection with different problem sizes. Genetic algorithms are adaptive algorithms proposed by John Holland in 1975 (1) and were described as adaptive heuristic search algorithms (2) based on the evolutionary ideas of natural selection and natural genetics by David Goldberg. They mimic the genetic processes of biological organisms. Genetic algorithm works with a population of individuals represented by chromosomes. Each chromosome is evaluated by its fitness value as computed by the objective function of the problem. The population undergoes transformation using three primary genetic operators - selection, crossover and mutation which form new generation of population. This process continues to achieve the optimal solution. Basic flowchart of genetic algorithm is illustrated in Figure 1.
Neeraj Gupta - One of the best experts on this subject based on the ideXlab platform.
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A Probabilistic Transmission Expansion Planning Methodology based on Roulette Wheel Selection and Social Welfare
arXiv: Artificial Intelligence, 2012Co-Authors: Neeraj Gupta, Rajiv Shekhar, Prem KalraAbstract:A new probabilistic methodology for transmission expansion planning (TEP) that does not require a priori specification of new/additional transmission capacities and uses the concept of social welfare has been proposed. Two new concepts have been introduced in this paper: (i) Roulette Wheel methodology has been used to calculate the capacity of new transmission lines and (ii) load flow analysis has been used to calculate expected demand not served (EDNS). The overall methodology has been implemented on a modified IEEE 5-bus test system. Simulations show an important result: addition of only new transmission lines is not sufficient to minimize EDNS.
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Congestion management based Roulette Wheel simulation for optimal capacity selection: Probabilistic transmission expansion planning
International Journal of Electrical Power & Energy Systems, 2012Co-Authors: Neeraj Gupta, Rajiv Shekhar, Prem KalraAbstract:This paper proposes a single-stage probabilistic transmission expansion planning (TEP) methodology where a priory specification of candidate transmission lines capacity is not required. The proposed methodology involves: (i) minimization of the sum of the investment, expected demand not served (EDNS), expected generation not served (EGNS) and expected Wheeling loss (EWL) costs to put together economic and reliability analysis on a single platform, (ii) probabilistic contingency analysis using Monte Carlo simulation (MCS), (iii) merit order dispatch of generators to minimizes operational cost of generators, (iv) a non-iterative DC load flow analysis to calculate EDNS, EGNS, and EWL, (v) a novel congestion management based Roulette Wheel selection method to calculate optimum capacity of the transmission lines, and (vi) genetic algorithm (GA) optimization method. The overall methodology has been implemented on a modified IEEE 5-bus test system. Simulations show an important result: addition of only new transmission lines is not sufficient to minimize EDNS.
Graham Kendall - One of the best experts on this subject based on the ideXlab platform.
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COCOA - Roulette Wheel Graph Colouring for Solving Examination Timetabling Problems
Combinatorial Optimization and Applications, 2009Co-Authors: Nasser R Sabar, Graham Kendall, Masri Ayob, Rong QuAbstract:This work presents a simple graph based heuristic that employs a Roulette Wheel selection mechanism for solving exam timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a Roulette Wheel, with a larger degree giving a larger segment. The Roulette Wheel selection mechanism selects an exam if the generated random number falls within the exam's segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the un-capacitated Carter's benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.
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Roulette Wheel graph colouring for solving examination timetabling problems
Conference on Combinatorial Optimization and Applications, 2009Co-Authors: Nasser R Sabar, Masri Ayob, Graham KendallAbstract:This work presents a simple graph based heuristic that employs a Roulette Wheel selection mechanism for solving exam timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a Roulette Wheel, with a larger degree giving a larger segment. The Roulette Wheel selection mechanism selects an exam if the generated random number falls within the exam's segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the un-capacitated Carter's benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.
Nasser R Sabar - One of the best experts on this subject based on the ideXlab platform.
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COCOA - Roulette Wheel Graph Colouring for Solving Examination Timetabling Problems
Combinatorial Optimization and Applications, 2009Co-Authors: Nasser R Sabar, Graham Kendall, Masri Ayob, Rong QuAbstract:This work presents a simple graph based heuristic that employs a Roulette Wheel selection mechanism for solving exam timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a Roulette Wheel, with a larger degree giving a larger segment. The Roulette Wheel selection mechanism selects an exam if the generated random number falls within the exam's segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the un-capacitated Carter's benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.
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Roulette Wheel graph colouring for solving examination timetabling problems
Conference on Combinatorial Optimization and Applications, 2009Co-Authors: Nasser R Sabar, Masri Ayob, Graham KendallAbstract:This work presents a simple graph based heuristic that employs a Roulette Wheel selection mechanism for solving exam timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a Roulette Wheel, with a larger degree giving a larger segment. The Roulette Wheel selection mechanism selects an exam if the generated random number falls within the exam's segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the un-capacitated Carter's benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.
