A Study of Test Suite Reduction Based on Ant Lion Optimizer

The development of smart meters to Internet of Things is the infrastructure for the Internet to carry out user electricity management and enhance user experience with electricity. As the iteration of smart home service system based on smart meters continues to accelerate, the development process is becoming more and more demanding for software testing. Test suite reduction is one of the common methods to improve the efficiency of software testing. In this paper, we proposed an optimization algorithm based on the Ant Lion Optimizer applied to test suite reduction problem of smart IoT meters. The algorithm improved the traditional Ant Lion Optimizer by converting the smart IoT meter test suite reduction problem into a binary coverage problem and combining the Greedy Algorithm to obtain the optimal test case subset. The experimental results showed that the algorithm based on Ant Lion Optimizer performed better on the test suite reduction problems compared to similar algorithms

Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study

This paper introduced two optimization algorithms which are Ant Lion Optimizer (ALO) and Dragonfly Algorithm (DA) for extracting the Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve parameters. The results produced by both algorithms are being compared to observe their performance. As a results, the ALO shows great performance compared to DA. Furthermore, these results also being compared with the results of the other reported metaheuristics algorithms. The ALO and DA presented competitive results

Exploiting Artificial Swarms for the Virtual Measurement of Backlash in Industrial Robots

The backlash is a lost motion in a mechanism created by gaps between its parts. It causes vibrations that increase over time and negatively affect accuracy and performance. The quickest and most precise way to measure the backlash is to use specific sensors, that have to be added to the standard equipment of the robot. However, this solution is little used in practice because raises the manufacturing costs. An alternative solution can be to exploit a virtual sensor, i.e., the information about phenomena that are not directly measured is reconstructed by signals from sensors used for other measurements. This work evaluates the use of bio-inspired swarm algorithms as the processing core of a virtual sensor for the backlash of a robotic joint. Swarm-based approaches, with their relatively modest occupation of memory and low computational load, could be ideal candidates to solve the roblem. In this paper, we exploit four state-of-the-art swarm-based optimization algorithms: the Dragonfly Algorithm, the Ant Lion Optimizer, the Grasshopper Optimization Algorithm, and the Grey Wolf Optimizer. The four candidate algorithms are compared on 20 different datasets covering a range of backlash values that reflect an industrial case scenario. Numerical results indicate that, unfortunately, none of the algorithms considered provides satisfactory solutions for the problem analyzed. Therefore, even if promising, these algorithms cannot represent the final choice for the problem of interest

ECG noise reduction technique using Antlion Optimizer (ALO) for heart rate monitoring devices

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist and automatic heart disorder diagnosis systems. In this paper, the use of Antlion Optimization (ALO) for optimizing and identifying the cutoff frequercy of ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed using the moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO with various parameters is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent AL0-based system successfully denoised the ECG signals more effectively than the conventional method. The percentage of the accuracy increased by 2%

Performance Evaluation of PID Controller for an Automobile Cruise Control System using Ant Lion Optimizer

This paper considers the design and performance evaluation of PID controller for an automobile cruise control system (ACCS). A linearized model of the cruise control system has been studied as per the dominant characteristics in closed loop system. The design problem is recast into an optimization problem which is solved using Ant Lion Optimization (ALO). The transient performance of proposed ACCS i.e., settling time, rise time, maximum overshot, peak time and steady state error are investigated by step input response and root locus analysis. To show the efficacy of the proposed algorithm over a state space method, classical PID, fuzzy logic, genetic algorithm, a comparison study is presented by using MATLAB/SIMULINK. Furthermore, the robustness of the system is evaluated by using bode analysis, sensitivity, complimentary sensitivity and controller sensitivity. The results indicate that the designed ALO based PID controller for ACCS achieves better performance than other recent methods reported in the literature.This paper considers the design and performance evaluation of PID controller for an automobile cruise control system (ACCS). A linearized model of the cruise control system has been studied as per the dominant characteristics in closed loop system. The design problem is recast into an optimization problem which is solved using Ant Lion Optimization (ALO). The transient performance of proposed ACCS i.e., settling time, rise time, maximum overshot, peak time and steady state error are investigated by step input response and root locus analysis. To show the efficacy of the proposed algorithm over a state space method, classical PID, fuzzy logic, genetic algorithm, a comparison study is presented by using MATLAB/SIMULINK. Furthermore, the robustness of the system is evaluated by using bode analysis, sensitivity, complimentary sensitivity and controller sensitivity. The results indicate that the designed ALO based PID controller for ACCS achieves better performance than other recent methods reported in the literature

Design and Optimization of Micro-Machined Sierpinski Carpet Fractal Antenna Using Ant Lion Optimization

This study investigates the optimized Sierpinski carpet fractal patch antenna and also explores the possibility of the integration of the proposed design with monolithic microwave integrated circuits. The optimization process has been performed using an ant lion optimization algorithm to achieve the required operating frequency and impedance matching. Further, due to surface waves excitation in the high index substrates used for the antenna design, the performance of the antenna degrades. Therefore, a process of micro-machining has been adopted to overcome this limitation. The micro-machining process creates an air cavity underneath the patch which further creates the low index environment in the patch antenna causing drastic improvement in the performance parameters along with the compatibility with monolithic microwave integrated circuits. The design shows multiple resonance frequencies in X-band and Ku-band. The proposed micro-machined design shows the resonance at 7.9 GHz, 9.6 GHz, 13.6 GHz, and 19 GHz with a maximum gain of 6 dBi.&nbsp