Search

This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods. Index Terms--Hybrid particle swarm optimization (HPSO), industrial power system, optimization methods, power generation dispatch.

In this paper, an accurate and effective probabilistic plurality voting method to combine outputs from multiple Simplified Fuzzy ARTMAP (SFAM) classifiers is presented. Five ELENA benchmark problems and five medical benchmark data sets have been used to evaluate the applicability and performance of the proposed Probabilistic Ensemble Simplified Fuzzy ARTMAP (PESFAM) network. Among the five benchmark problems in ELENA project, PESFAM outperforms the SFAM and Multi-layer Perceptron (MLP) classifier. In addition, the effectiveness of the proposed PESFAM is delineated in medical diagnosis applications. For the medical diagnosis and classification problems, PESFAM achieves 100 percent in accuracy, specificity, and sensitivity based on the 10-fold crossvalidation and these results are superior to those from other classification algorithms. In addition, the a posteri probability of the predicted class can be used to measure the prediction reliability of PESFAM. The experiments demonstrate the potential of the proposed multiple SFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent medical diagnosis tool. Index Terms--Simplified Fuzzy ARTMAP, plurality voting, medical diagnosis, ensemble neural networks.

Abstract--In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features [...]

Abstract--This paper presents three new methodologies for the basic operations, which aim at finding new ways of computing union (maximum) and intersection (minimum) membership values by taking into effect the [...]

This paper presents two novel approaches to determine optimum growing multi-exports network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts serf-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully serf-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models. Index Terms--Growing neural network modular neural network, local linear model.

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local optimization by direct search and systematic reduction in size of the search region method is next employed to do the fine tuning. Constraint satisfaction technique has been employed to improve the solution quality and reduce the computational expenses. In order to validate the effectiveness of the proposed hybrid real coded genetic algorithm, the result of 10-generation unit ED problem with multiple fuel options is considered. The result shows that the proposed hybrid algorithm not only improves the solution accuracy and reliability but also makes the algorithm more efficient in terms of number of function evaluations and computation time. The simulation study clearly demonstrates that the proposed hybrid real coded genetic algorithm is practical and valid for real-time applications. Keywords: Economic dispatch; Non-convex function; Optimization; Genetic algorithms; Hybrid genetic algorithms; Local tuning

In this paper, an accurate and effective probabilistic plurality voting method to combine outputs from multiple Simplified Fuzzy ARTMAP (SFAM) classifiers is presented. Five ELENA benchmark problems and five medical benchmark data sets have been used to evaluate the applicability and performance of the proposed Probabilistic Ensemble Simplified Fuzzy ARTMAP (PESFAM) network. Among the five benchmark problems in ELENA project, PESFAM outperforms the SFAM and Multi-layer Perceptron (MLP) classifier. In addition, the effectiveness of the proposed PESFAM is delineated in medical diagnosis applications. For the medical diagnosis and classification problems, PESFAM achieves 100 percent in accuracy, specificity, and sensitivity based on the 10-fold crossvalidation and these results are superior to those from other classification algorithms. In addition, the a posteri probability of the predicted class can be used to measure the prediction reliability of PESFAM. The experiments demonstrate the potential of the proposed multiple SFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent medical diagnosis tool. [ABSTRACT FROM AUTHOR]

This paper presents a new robust, two-phase hybrid optimization algorithm to solve non-linear constrained optimization problems. The proposed algorithm is very much useful when addressing heavily constrained optimization problem in terms of solution accuracy and computation time. It can out perform conventional genetic algorithms (CGAs) in the sense that Hybrid GAs makes it possible to improve both the quality of the solution and reduce the computing expenses. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local search method is next employed to do fine tuning. The phase − 1 uses real coded genetic algorithm, while optimization by direct search and systematic reduction of the size of search region method is employed in the phase − 2. Thirteen test problems were taken from the literature have been simulated and results obtained were compared. The results clearly demonstrate that the proposed hybrid real coded genetic algorithm not only improve the reliability but also make the algorithm more efficient in terms of number of function evaluations to reach the global optimum point. Thus the usefulness and effectiveness of this algorithm is placed in a sharper focus. [ABSTRACT FROM AUTHOR]