http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

A survey on new generation metaheuristic algorithms

An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models

Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

In this paper, atom search optimization (ASO) algorithm and a novel chaotic version of it [chaotic ASO (ChASO)] are proposed to determine the optimal parameters of the fractional-order proportional+integral+derivative (FOPID) controller for dc motor speed control. The ASO algorithm is simple and easy to implement, which mathematically models and mimics the atomic motion model in nature, and is developed to address a diverse set of optimization problems. The proposed ChASO algorithm, on the other hand, is based on logistic map chaotic sequences, which makes the original algorithm be able to escape from local minima stagnation and improve its convergence rate and resulting precision. First, the proposed ChASO algorithm is applied to six unimodal and multimodal benchmark optimization problems and the results are compared with other algorithms. Second, the proposed ChASO-FOPID, ASO-FOPID, and ASO-PID controllers are compared with GWO-FOPID, GWO-PID, IWO-PID, and SFS-PID controllers using the integral of time multiplied absolute error (ITAE) objective function for a fair comparison. Comparisons were also made for the integral of time multiplied squared error (ITSE) and Zwe-Lee Gaing's (ZLG) objective function as the most commonly used objective functions in the literature. Transient response analysis, frequency response (Bode) analysis, and robustness analysis were all carried out. The simulation results are promising and validate the effectiveness of the proposed approaches. The numerical simulations of the proposed ChASO-FOPID and ASO-FOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively.

/pdf/optimal-tuning-of-fractional-order-pid-controller-for-dc-2izwdt4d4d.pdf

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

This paper proposes a novel tuning design of proportional integral derivative (PID) controller via an improved kidney-inspired algorithm (IKA) with a new objective function. The main objective of the proposed approach is to optimize the transient response of the AVR system by minimizing the maximum overshoot, settling time, rise time and peak time values of the terminal voltage, and eliminating the steady state error. After obtaining the optimal values of the three gains of the PID controller (KP, KI, and KD) with the proposed approach, the transient response analysis was performed and compared with some of the current heuristic algorithms-based approaches in literature to show the superiority of the optimized PID controller. In order to evaluate the stability of the automatic voltage regulator (AVR) system tuned by IKA method, the pole/zero map analysis and Bode analysis are performed. Finally, the robustness analysis of the proposed approach has been carried out with variations in the parameters of the AVR system. The numerical simulation results demonstrated that the proposed IKA tuned PID controller has better control performances compared to the other existing approaches. The essence of the presented study points out that the proposed approach may successfully be applied for the AVR system.

/pdf/improved-kidney-inspired-algorithm-approach-for-tuning-of-4dr9zmzdrs.pdf

Improved Kidney-Inspired Algorithm Approach for Tuning of PID Controller in AVR System

A novel design method, sine-cosine algorithm (SCA) is presented in this paper to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AV...

Sine-cosine algorithm-based optimization for automatic voltage regulator system:

A novel application of a very recent heuristic-based method, namely Salp Swarm Algorithm (SSA) is presented here for tuning of power system stabilizer (PSS) in a multi- machine power system. The tuning problem of PSS parameters is expressed as an optimization problem and the SSA method is utilized for searching the optimal parameters. The efficacy of the SSA-based PSS design was successfully tested on a well- known 3-machine, 9-bus power system. The results are comparatively evaluated with the other results obtained by the Tabu Search (TS) and the Biogeography-Based Optimization (BBO) methods. From the eigenvalue analysis and nonlinear simulation results it is confirmed that for damping oscillations, the performance of the proposed SSA approach in this study is better than that obtained by other intelligent techniques (TS and BBO).

Parameter optimization of power system stabilizer via Salp Swarm algorithm

A novel design method is presented to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system utilizing the grasshopper optimization algorithm (GOA). The proposed approach is a simple and effective algorithm that is able to solve many optimization problems even those with unknown search spaces effectively. The simplicity of algorithm provides high quality tuning of optimal PID controller parameters. The integral of time weighted squared error (ITSE) is used as the performance index to confirm the performance of the proposed GOA-PID controller. When compared to the other PID controllers based on Ziegler- Nichols (ZN), differential evolution (DE), and artificial bee colony (ABC) tuning methods, the proposed method is found highly effective and robust to improve AVR system's transient response.

Baran Hekimoglu

Papers

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

Improved Kidney-Inspired Algorithm Approach for Tuning of PID Controller in AVR System

Sine-cosine algorithm-based optimization for automatic voltage regulator system:

Parameter optimization of power system stabilizer via Salp Swarm algorithm

Grasshopper optimization algorithm for automatic voltage regulator system