A Heuristic Approach to Design Discrete Fractional Order Integrators without Using s-to-z Transform

2017 ◽  
Vol 261 ◽  
pp. 386-393
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal ◽  
Nilotpal Banerjee
Keyword(s):  
Fractional Order ◽  
Search Algorithm ◽  
Statistical Tests ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Infinite Impulse Response ◽  
Error Metrics ◽  
Real Coded Genetic Algorithm

In this paper, a heuristic optimization technique called Harmony Search Algorithm (HSA) is efficiently employed to design Infinite Impulse Response (IIR) Discrete Fractional Order Integrators (DFOIs). Unlike the methods reported in the literature, no discretization (s-to-z transform) operator is necessary to obtain the DFOIs by using the proposed approach. To investigate the design efficiency, the HSA-based DFOIs have been evaluated against the designs based on Real coded Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) using different frequency response error metrics. The reliability in the performance of the proposed DFOIs are extensively investigated by conducting various statistical tests. Comparison of fitness convergence demonstrates that HSA achieves the near global optimal solution in the least number of iterations. Thus, HSA exhibits superior computational efficiency in solving this multimodal optimization problem. The proposed DFOIs also outperform the reported designs.

scholarly journals Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

2018 ◽  
Vol 8 (6) ◽  
pp. 4593 ◽  
Author(s):  
Erwin Erwin ◽  
Saparudin Saparudin ◽  
Wulandari Saputri
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Multilevel Thresholding ◽  
Improved Harmony Search ◽  
High Degree ◽  
Improved Harmony Search Algorithm

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.

Hybrid Data Mining Approach for Image Segmentation Based Classification

Biometrics ◽  
2017 ◽  
pp. 1543-1561 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Aboul Ella Hassanien ◽  
Ajith Abraham
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Rbf Neural Network ◽  
Harmony Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Harmony Search Algorithm ◽  
Data Mining Technique

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

scholarly journals Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Cinthia Peraza ◽  
Fevrier Valdez ◽  
Juan R. Castro ◽  
Oscar Castillo
Keyword(s):  
Fuzzy Systems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Dynamic Parameter ◽  
Harmony Search Algorithm ◽  
Parameter Adaptation ◽  
Error Metrics ◽  
Interval Type ◽  
Following Error

This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

Optimal Design of Fractional-Order Digital Differentiator Using Flower Pollination Algorithm

2018 ◽  
Vol 27 (08) ◽  
pp. 1850129 ◽  
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Fractional Order ◽  
Optimization Technique ◽  
Phase Response ◽  
Computational Time ◽  
Infinite Impulse Response ◽  
Flower Pollination Algorithm ◽  
Solution Quality ◽  
Digital Differentiator ◽  
Flower Pollination ◽  
Real Coded Genetic Algorithm

This paper presents an efficient approach to design wideband, accurate, stable, and minimum-phase fractional-order digital differentiators (FODDs) in terms of the infinite impulse response (IIR) filters using an evolutionary optimization technique called flower pollination algorithm (FPA). The efficiency comparisons of FPA with real-coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE)-based designs are conducted with respect to different magnitude and phase response error metrics, parametric and nonparametric statistical hypotheses tests, computational time, and fitness convergence. Exhaustive simulation results clearly demonstrate that FPA significantly outperforms RGA, PSO, and DE in attaining the best solution quality consistently. Extensive analysis is also conducted in order to determine the role of control parameters of FPA on the performance of the designed FODDs. The proposed FPA-based FODDs outperform all the designs published in the recent literature with respect to the magnitude responses and also achieve a competitive performance in terms of the phase response.

scholarly journals VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS

2018 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour
Keyword(s):  
Convergence Rates ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Search Performance ◽  
Search Spaces ◽  
Search Mechanism ◽  
Heuristic Technique

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism. Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions.   

scholarly journals Solution of combined economic and emission dispatch problem using a novel chaotic improved harmony search algorithm

2018 ◽  
Vol 6 (3) ◽  
pp. 447-467 ◽  
Author(s):  
Hamid Rezaie ◽  
M.H. Kazemi-Rahbar ◽  
Behrooz Vahidi ◽  
Hasan Rastegar
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Transmission Losses ◽  
Test Systems ◽  
Improved Harmony Search ◽  
Emission Dispatch ◽  
Improved Harmony Search Algorithm ◽  
Economic Emission Dispatch

Abstract This paper presents a new optimization technique developed based on harmony search algorithm (HSA), called chaotic improved harmony search algorithm (CIHSA). In the proposed algorithm, the original HSA is improved using several innovative modifications in the optimization procedure such as using chaotic patterns instead of uniform distribution to generate random numbers, dynamically tuning the algorithm parameters, and employing virtual harmony memories. Also, a novel type of local optimization is introduced and employed in the algorithm procedure. Applying these modifications to HSA has resulted in enhancing the robustness, accuracy and search efficiency of the algorithm, and significantly reducing the iterations number required to achieve the optimal solution. To validate the effectiveness of CIHSA, it is used to solve the combined economic emission dispatch (CEED) problem, which practically is a complex high-dimensional non-convex optimization task with several equality and inequality constraints. Six test systems having 6, 10, 13, 14, 40, and 140 generators are investigated in this study, and the valve-point loading effects, ramp rate limits and power transmission losses are also taken into account. The results obtained by CIHSA are compared with the results reported in a large number of other research works. Furthermore, the statistical data regarding the CIHSA performance in all test systems is presented. The numerical and statistical results confirm the high quality of the solutions found by CIHSA and its superiority compared to other existing techniques employed in solving CEED problems. Highlights An innovative and strong optimization technique based on harmony search is proposed. The proposed algorithm is tested on solving economic emission dispatch problem. It has the potential to be applied to many other engineering optimization problems. Six test systems considering valve point effect and transmission losses are studied. High quality solutions are obtained and compared with a large number of other methods.

scholarly journals An Elite Decision Making Harmony Search Algorithm for Optimization Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Lipu Zhang ◽  
Yinghong Xu ◽  
Yousong Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Solution Vector ◽  
Integer Variables ◽  
Design Variables ◽  
New Variant

This paper describes a new variant of harmony search algorithm which is inspired by a well-known item “elite decision making.” In the new algorithm, the good information captured in the current global best and the second best solutions can be well utilized to generate new solutions, following some probability rule. The generated new solution vector replaces the worst solution in the solution set, only if its fitness is better than that of the worst solution. The generating and updating steps and repeated until the near-optimal solution vector is obtained. Extensive computational comparisons are carried out by employing various standard benchmark optimization problems, including continuous design variables and integer variables minimization problems from the literature. The computational results show that the proposed new algorithm is competitive in finding solutions with the state-of-the-art harmony search variants.

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