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Linear And Nonlinear Programming

Principles of Plant Breeding

Review of applications of algorithms in bio-inspired computing.Brief description of algorithms without mathematical notations.Brief description of scope of applications of the algorithms.Identification of algorithms whose applications may be explored.Identification of algorithms on which theory development may be explored. With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. It is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application. Specifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.

Bio inspired computing - A review of algorithms and scope of applications

The electric activities of neurons are dependent on the complex electrophysiological condition in neuronal system, and it indicates that the complex distribution of electromagnetic field could be detected in the neuronal system. According to the Maxwell electromagnetic induction theorem, the dynamical behavior in electric activity in each neuron can be changed due to the effect of internal bioelectricity of nervous system (e.g., fluctuation of ion concentration inside and outside of cell). As a result, internal fluctuation of electromagnetic field is established and the effect of magnetic flux across the membrane should be considered during the emergence of collective electrical activities and signals propagation among a large set of neurons. In this paper, the variable for magnetic flow is proposed to improve the previous Hindmarsh–Rose neuron model; thus, a four-variable neuron model is designed to describe the effect of electromagnetic induction on neuronal activities. Within the new neuron model, the effect of magnetic flow on membrane potential is described by imposing additive memristive current on the membrane variable, and the memristive current is dependent on the variation of magnetic flow. The dynamics of this modified model is discussed, and multiple modes of electric activities can be observed by changing the initial state, which indicates memory effect of neuronal system. Furthermore, a practical circuit is designed for this improved neuron model, and this model could be suitable for further investigation of electromagnetic radiation on biological neuronal system.

Model of electrical activity in a neuron under magnetic flow effect

Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market. Stock market prices are dynamic, highly sensitive, nonlinear and chaotic. There are different techniques for forecast prices in the time-variant domain and due to variability and uncertain behavior in stock prices, traditional methods, such as data mining, statistical approaches, and non-deep neural networks models are not suited for prediction and generalized forecasting stock prices. While autoregressive fractional integrated moving average (ARFIMA) model provides a flexible tool for classes of long-memory models. The advancement of machine learning-based deep non-linear modelling confirms that the hybrid model efficiently extracts profound features and model non-linear functions. LSTM networks are a special kind of recurrent neural network (RNN) that map sequences of input observations to output observations with capabilities of long-term dependencies. A novel ARFIMA-LSTM hybrid recurrent network is presented in which ARFIMA model-based filters having the linear tendencies better than ARIMA model in the data and passes the residual to the LSTM model that captures nonlinearity in the residual values with the help of exogenous dependent variables. The model not only minimizes the volatility problem but also overcome the over fitting problem of neural networks. The model is evaluated using PSX company data of the stock market based on RMSE, MSE and MAPE along with a comparison of ARIMA, LSTM model and generalized regression radial basis neural network (GRNN) ensemble method independently. The forecasting performance indicates the effectiveness of the proposed AFRIMA-LSTM hybrid model to improve around 80% accuracy on RMSE as compared to traditional forecasting counterparts.

Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting

A novel weighted least square (WLS) fitness function is adopted.The proposed method outperforms the GA in terms magnitude and phase error.The proposed method is superior to the interpolation based methods.Fast convergence rate is achieved. In this paper, a new meta-heuristic optimization algorithm, called cuckoo search algorithm (CSA) is applied to determine the optimal coefficients of the finite impulse response-fractional order differentiator (FIR-FOD) problem. CSA is based on lifestyle and unique parasitic behavior in egg laying and breeding of some cuckoo species along with Levy flight behavior of some birds and fruit flies. The CSA is capable of solving linear and nonlinear optimization problems. The proposed CSA method prevents the local minima problem encountered in conventional FIR-FOD design method. A novel weighted least square (WLS) fitness function is adopted to improve the response of the FOD to a great extent. The proposed CSA based method has alleviated from inherent drawbacks of premature convergence and stagnation unlike genetic algorithm (GA). To verify the effectiveness of the proposed FIR-FOD based on the cuckoo search algorithm, different set of initial population is tested by simulation. Simulation results affirm that the proposed fractional order differentiator design approach using CSA outperforms the genetic algorithm in terms design accuracy (magnitude and phase error), fast convergence rate and optimal solution. The simulation results confirmed that the proposed FOD using CSA outperforms the FOD designed using evolutionary algorithm like GA and conventional FOD design methods such as radial basis function (RBF) interpolation method and DCT interpolation method.

Optimal design of FIR fractional order differentiator using cuckoo search algorithm

This paper applied a novel global meta-heuristic optimization algorithm, cuckoo search algorithm (CSA) to determine optimal coefficients of a fractional delay-infinite impulse response (FD-IIR) filter and trying to meet the ideal frequency response characteristics. Since fractional delay-IIR filter design is a multi-modal optimization problem, it cannot be computed efficiently using conventional gradient based optimization techniques. A weighted least square (WLS) based fitness function is used to improve the performance to a great extent. FD-IIR filters of different orders have been designed using the CSA. The simulation results of the proposed CSA based approach have been compared to those of well accepted evolutionary algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of the CSA based FD-IIR filter is superior to those obtained by GA and PSO. The simulation and statistical results affirm that the proposed approach using CSA outperforms GA and PSO, not only in the convergence rate but also in optimal performance of the designed FD-IIR filter (i.e., smaller magnitude error, smaller phase error, higher percentage improvement in magnitude and phase error, fast convergence rate). The absolute magnitude and phase error obtained for the designed 5th order FD-IIR filter are as low as 0.0037 and 0.0046, respectively. The percentage improvement in magnitude error for CSA based 5th order FD-IIR design with respect to GA and PSO are 80.93% and 74.83% respectively, and phase error are 76.04% and 71.25%, respectively.

Optimal fractional delay-IIR filter design using cuckoo search algorithm.

Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.

Stationary wavelet transform based ECG signal denoising method.

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Optimal design of FIR high pass filter based on L1 error approximation using real coded genetic algorithm

In this article, an optimal design of two-dimensional finite impulse response (2D FIR) filter with quadrantally even symmetric impulse response using fractional derivative constraints (FDCs) is presented. Firstly, design problem of 2D FIR filter is formulated as an optimization problem. Then, FDCs are imposed over the integral absolute error for designing of the quadrantally even symmetric impulse response filter. The optimized FDCs are applied over the prescribed frequency points. Next, the optimized filter impulse response coefficients are computed using a hybrid optimization technique, called hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA). Further, FDC values are also optimized such that flat passband and stopband frequency response is achieved and the absolute $$L_1$$L1-error is minimized. Finally, four design examples of 2D low-pass, high-pass, band-pass and band-stop filters are demonstrated to justify the design accuracy in terms of passband error, stopband error, maximum passband ripple, minimum stopband attenuation and execution time. Simulation results have been compared with the other optimization algorithms, such as real-coded genetic algorithm, particle swarm optimization and gravitational search algorithm. It is observed that HPSO-GSA gives improved results for 2D FIR-FDC filter design problem. In comparison with other existing techniques of 2D FIR filter design, the proposed method shows improved design accuracy and flexibility with varying values of FDCs.

Manjeet Kumar

Papers

Optimal design of FIR fractional order differentiator using cuckoo search algorithm

Optimal fractional delay-IIR filter design using cuckoo search algorithm.

Stationary wavelet transform based ECG signal denoising method.

Optimal design of FIR high pass filter based on L1 error approximation using real coded genetic algorithm

Optimal Design of 2D FIR Filters with Quadrantally Symmetric Properties Using Fractional Derivative Constraints