Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight

2017 ◽  
Vol 67 ◽  
pp. 266-279 ◽  
Author(s):  
Manjeet Kumar ◽  
Tarun Kumar Rawat ◽  
Apoorva Aggarwal
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Search Algorithm ◽  
Infinite Impulse Response ◽  
Lévy Flight ◽  
Levy Flight ◽  
Response System

Adaptive infinite impulse response system identification using teacher learner based optimization algorithm

2018 ◽  
Vol 49 (5) ◽  
pp. 1785-1802 ◽  
Author(s):  
Sandeep Singh ◽  
Alaknanda Ashok ◽  
Manjeet Kumar ◽  
Tarun Kumar Rawat
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Optimization Algorithm ◽  
Infinite Impulse Response ◽  
Response System

Infinite impulse response system identification using antlion optimisation algorithm

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Sandeep Singh ◽  
Alpana Shekhar ◽  
Chakshu Kalra ◽  
Alaknanda Ashok ◽  
Tarun Kumar Rawat ◽  
...  
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Infinite Impulse Response ◽  
Response System ◽  
Optimisation Algorithm

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

scholarly journals A gravitational search algorithm based on levy flight

2021 ◽  
Vol 1865 (4) ◽  
pp. 042006
Author(s):  
Jing Zhao ◽  
Haidong Zhu ◽  
Yinhua Hu ◽  
Enjun Hu ◽  
Baole Huang ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Gravitational Search

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

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