Differential evolution based on ε-domination and orthogonal design method for power environmentally-friendly dispatch

This paper proposes a differential evolution algorithm based on ε-domination and orthogonal design method (ε-ODEMO) to solve power dispatch problem considering environment protection and saving energy. Besides the operation costs of thermal power plant, contaminative gas emission is also optimized as an objective. In the proposed algorithm, ε-dominance is adopted to make genetic algorithm obtain a good distribution of Pareto-optimal solutions in a small computational time, and the orthogonal design method can generate an initial population of points that are scattered uniformly over the feasible solution space, these modify the differential evolution algorithm (DE) to make it suit for multi-objective optimization (MOO) problems and improve its performance. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method. Compared with other methods, the results obtained demonstrate the effectiveness of the proposed algorithm for solving the power environmentally-friendly dispatch problem.     

An efficient hybrid learning algorithm for neural network–based speech recognition systems on FPGA chip

This paper implemented an artificial neural network (ANN) on a field programmable gate array (FPGA) chip for Mandarin speech measurement and recognition of nonspecific speaker. A three-layer hybrid learning algorithm (HLA), which combines genetic algorithm (GA) and steepest descent method, was proposed to fulfill a faster global search of optimal weights in ANN. Some other popular evolutionary algorithms, such as differential evolution, particle swarm optimization and improve GA, were compared to the proposed HLA. It can be seen that the proposed HLA algorithm outperforms the other algorithms. Finally, the designed system was implemented on an FPGA chip with an SOC architecture to measure and recognize the speech signals.     

An efficient orientation distance–based discriminative feature extraction method for multi-classification

Feature extraction is an important step before actual learning. Although many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. This paper proposes a novel feature extraction method, called orientation distance–based discriminative (ODD) feature extraction, particularly designed for multi-class classification problems. Our proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine an appropriate kernel function and map the input data with all classes into a feature space where the classes of the data are well separated. In the second step, we put forward two variants of ODD features, i.e., one-vs-all-based ODD and one-vs-one-based ODD features. We first construct hyper-plane (SVM) based on one-vs-all scheme or one-vs-one scheme in the feature space; we then extract one-vs-all-based or one-vs-one-based ODD features between a sample and each hyper-plane. These newly extracted ODD features are treated as the representative features and are thereafter used in the subsequent classification phase. Extensive experiments have been conducted to investigate the performance of one-vs-all-based and one-vs-one-based ODD features for multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods.     

Commentary of “Chaotic Krill Herd algorithm for optimal reactive power dispatch considering FACTS devices” by Aparajita Mukherjee et al. “Applied Soft Computing” 44 (2016) 163–190

This short communication presents a discussion of “Chaotic Krill Herd algorithm for optimal reactive power dispatch considering FACTS devices” by Aparajita Mukherjee et al. “Applied Soft Computing” 44 (2016) 163–190. In this paper, an experiment on the reactive power dispatches considering FACTS devices is presented with three example systems, namely 30, 57 and 118-bus test systems. In the reported results for the 57 and 118-bus test system, total losses of load flow with input voltage generators, transformers tab, and capacitor banks were different. In this regard, a clarification on calculations of loss is presented.     

An AHP based heuristic DPSO algorithm for generating multi criteria production–distribution plan

Multi-criteria integrated production–distribution problems were solved by many researchers using different optimization techniques. A novel analytic hierarchy process (AHP) based heuristic discrete particle swarm optimization (DPSO) algorithm is proposed in this research for solving difficult production–distribution problems. A bearing manufacturing industry's case is considered in this paper and the mathematical model is formulated as mixed integer linear programming (MILP) problem considering multi-period, multi-product and multi-plant scenarios. The three major objectives considered are total cost reduction, minimization of change in labor levels and percentage under-utilization. The results of the AHP based heuristic DPSO algorithm are compared with the branch and bound algorithm results generated using LINGO software. The approach gives good near optimal solutions. In addition to the bearing manufacturing industry dataset, two other test datasets are also solved.     

An efficient two phase approach for solving reliability–redundancy allocation problem using artificial bee colony technique

The main goal of the present paper is to present a two phase approach for solving the reliability–redundancy allocation problems (RRAP) with nonlinear resource constraints. In the first phase of the proposed approach, an algorithm based on artificial bee colony (ABC) is developed to solve the allocation problem while in the second phase an improvement of the solution as obtained by this algorithm is made. Four benchmark problems in the reliability–redundancy allocation and two reliability optimization problems have been taken to demonstrate the approach and it is shown by comparison that the solutions by the new proposed approach are better than the solutions available in the literature.     

Numerical algorithm for the standard pairing problem based on the Heine–Stieltjes correspondence and the polynomial approach

We present a detailed study of the computational complexity of a numerical algorithm based on the Heine–Stieltjes correspondence following the new approach we proposed recently for solving the Bethe ansatz (Gaudin–Richardson) equations of the standard pairing problem. For k$k$ pairs of valence nucleons in n$n$ non-degenerate single-particle energy levels, the approach utilizes that solutions of the Bethe ansatz equations can be obtained from two matrices of dimensions (k+1)×(k+1)$(k+1)\times (k+1)$ and (n−1)×(k+1)$(n-1)\times (k+1)$, which are associated with the extended Heine–Stieltjes and Van Vleck polynomials, respectively. Since the coefficients in these polynomials are free from divergence with variations in contrast to the original Bethe ansatz equations, the approach provides an efficient and systematic way to solve the problem. The method reduces to solving a system of k$k$ polynomial equations, which can be efficiently implemented by the fast Newton–Raphson algorithm with a Monte Carlo sampling procedure for the initial guesses. By extension, the present algorithm can also be used to solve a large class of Gaudin-type quantum many-body problems, including an efficient angular momentum projection method for multi-particle systems.     

An iterative algorithm for simulation error based identification of polynomial input–output models using multi-step prediction

Effective identification of polynomial input–output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input–output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.     

Design of reduced search space strategy based on integration of Nelder–Mead method and pattern search algorithm with application to economic load dispatch problem

This paper presents a novel hybrid technique for the solution of economic load dispatch problem with valve point loading effect using Nelder–Mead (NM) simplex method and pattern search (PS) algorithm. Strength of globalized NM optimization algorithm has been employed to explore the search space for near optimal solution, and PS algorithm is used in combination with a search space reduction strategy, incorporating the principles of selection and stochastic reproduction, to fine-tune the result. The proposed technique has been applied to three different systems having 3, 13 and 40 generating units to demonstrate the application for small to large load dispatch set-up. The efficacy of the design scheme is established from comparison of the results with the state-of-the-art solvers, and it is found that the proposed scheme gives the best result in terms of mean cost while the average computational time is less than most of the reported methods.

     

An efficient approximate method for solving linear fractional Klein–Gordon equation based on the generalized Laguerre polynomials

In this paper, a new approximate formula of the fractional derivative is derived. The proposed formula is based on the generalized Laguerre polynomials. Global approximations to functions defined on a semi-infinite interval are constructed. The fractional derivatives are presented in terms of Caputo sense. Special attention is given to study the error and the convergence analysis of the proposed formula. A new spectral Laguerre collocation method is presented for solving linear fractional Klein–Gordon equation (LFKGE). The properties of Laguerre polynomials are utilized to reduce LFKGE to a system of ordinary differential equations, which solved using the finite difference method. Numerical results are provided to confirm the theoretical results and the efficiency of the proposed method.     

An efficient numerical technique based on the extended cubic B-spline functions for solving time fractional Black–Scholes model

Financial theory could introduce a fractional differential equation (FDE) that presents new theoretical research concepts, methods and practical implementations. Due to the memory factor of fractional derivatives, physical pathways with storage and inherited properties can be best represented by FDEs. For that purpose, reliable and effective techniques are required for solving FDEs. Our objective is to generalize the collocation method for solving time fractional Black–Scholes European option pricing model using the extended cubic B-spline. The key feature of the strategy is that it turns these type of problems into a system of algebraic equations which can be appropriate for computer programming. This is not only streamlines the problems but speed up the computations as well. The Fourier stability and convergence analysis of the scheme are examined. A proposed numerical scheme having second-order accuracy via spatial direction is also constructed. The numerical and graphical results indicate that the suggested approach for the European option prices agree well with the analytical solutions.

     

Comments on “An improved model for vehicle routing problem with time constraint based on genetic algorithm” [Comput. Ind. Eng. 42 (2002) 361–369]

An improved model for vehicle routing problem proposed by Heung-Suk Hwang (2002) was recently published in Computer & Industrial Engineering Journal to solve the time constraint vehicle routing problem based on improved genetic algorithm. In this work, comments are given to the published paper of Heung-Suk Hwang (2002). And they centralize on the example of improved mutation operator and the problem formulation. The suggested modifications are also provided in this work.