Improved Antlion Optimization Algorithm Research Articles

Sizing Optimization Algorithm for Vehicle-to-Grid System Considering Cost and Reliability Based on Rule-Based Scheme

The most widely two Renewable Energy Sources (RESs) used are solar and wind as naturally found sources due to the provided merits as clean, free of charge, and environmentally friendly. However, they are facing limitations in intermittency. This article aims to utilize natural resources integrated with the utility grid and Electric Vehicle (EV) to provide a hybrid system with a minimum of two objectives namely Cost of Electricity (COE) and reliability using the Losses Power Supply Probability (LPSP) method. The two mentioned objectives are considered to satisfy the residential load demand with EV in terms of Vehicle-to-Grid (V2G) as this article considered. The mentioned objective has been addressed by the Improved Antlion Optimization Algorithm (IALO) and coupled with a high supervisory control method called Rule-Based Energy Management Strategy (RB-EMS) to guarantee to spread the power among the system component. Optimization results show the hybrid integration of the utilized RESs with the Battery (BT) gives the most economic and reliable system for the study area integrated with the EV battery. The gained result was validated with a natural-inspired metaheuristic algorithm called Particle Swarm Optimization (PSO). This article assesses the effect on the RESs generators to achieve an economic and reliable system.

An adaptive sparse general regression neural network-based force observer for teleoperation system

Restricted by factors such as small robot size and harsh operating environment, the inability to obtain the interaction force between the robot arm and the environment through force sensors has become problematic in promoting the application of teleoperation systems in minimally invasive surgery, nuclear waste cleanup, and other fields. To accurately obtain the interaction force without the force sensors, a force observer based on an adaptive sparse general regression neural network (ASGRNN) is proposed in this paper. The proposed force observer uses a machine learning-based approach to obtain estimated force, thus eliminating the need for the dynamic parameters of the robot arm. Also, an innovative feature selection method incorporating the wrapper method and sparse regularization is proposed to select the input features of the force observer. Secondly, two new criteria are defined to eliminate the useless support vectors in the model. In addition, an improved antlion optimization algorithm (IALO) is proposed to optimize the bandwidth parameters of the model. To verify the performance of the proposed force observer, a 6-degree-of-freedom teleoperation robot experimental platform is built and compared with three existing force estimation models. The results show that the proposed force observer outperforms the existing model in terms of estimation accuracy, and the mean square error (MSE) is at least 35.79% lower than the existing model. In conclusion, this paper provides a feasible and effective force observer for a teleoperation system where force sensors are not applicable and dynamic parameters are non-knowable.

Illumination correction of dyed fabric based on extreme learning machine with improved ant lion optimizer

AbstractIn order to eliminate the influence of scene illumination on the evaluation of the color difference of dyed fabrics, this paper proposes a dyed fabric illumination correction algorithm based on the extreme learning machine (ELM) with grey wolf optimizer (GWO)‐optimized ant lion optimizer (ALO). Firstly, the Grey Edge framework is used to extract the features of the dyed fabric image as the input vector. Then, to improve the optimization ability of the ALO algorithm, the GWO algorithm is used to provide a set of optimized initial populations to the ALO algorithm, and then the improved ALO algorithm is used to optimize the parameters of the ELM. Finally, the proposed GWO‐ALO‐ELM algorithm is used to correct the illumination of the dyed fabric, and restore the graphics to the effect display under standard illumination through the diagonal reduction model. Compared with the experimental results of GWO‐ELM, ALO‐ELM, backpropagation (BP), ELM, random vector function link (RVFL), and other algorithms, it can be seen that the GWO‐ALO‐ELM algorithm proposed in this paper has good predictive value and quasi‐bias effect, and good stability.

Time and Cost Based Cloud Resource Optimization System

Cloud computing is an emerging technology through which resources can be shared over the internet with different users either free or on a rent basis. Resource scheduling in cloud computing is a challenging area for researchers as is maximum utilization can opt through efficient resource scheduling algorithm. Other than this, virtual machine provisioning, packaging, and availability guarantee decrease the performance. Resource management in cloud could be a time and cost effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud.In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

Using an Improved Ant Lion Optimizer Algorithm and Wireless Network to Explore Nondestructive Testing Technology for Water Conservancy Engineering

The damage diagnosis of traditional hydraulic engineering structures is largely based on the modal analysis of contact approach, which makes it difficult to carry out a rapid diagnosis of the damage following an earthquake. In view of enhancing the efficiency of water conservancy engineering damage detection, this study was aimed at improving the traditional ant lion optimizer algorithm, combining it with wavelet transform to detect image denoising and effectively extract the features of the damage. Meanwhile, in terms of signal transmission and collection, the study adopted a geological radar as the carrier. In addition, the study verified the application prospects of nondestructive testing technology in the field of water conservancy engineering by evaluating the quality of a specific water conservancy project. Finally, the effectiveness of the algorithm was analyzed via experimental research. The results demonstrate that the proposed algorithm has clear advantages over traditional detection techniques.

Novel Bio-Inspired Algorithm for Speed Control and Torque Ripple Reduction of Switched Reluctance Motor in Aerospace Application

Switched Reluctance Motor (SRM) is an electrical motor which operates by a reluctance torque that has stator and rotor salient poles. It also has a simple configuration and reasonable power electronics necessity for both AC and DC machines within adjustable-speed drives. The problem working on it is, SRM with doubly salient poles introduce a torque ripple in its output which leads to deteriorated performance on speed control. In this proposed work, torque ripple reduction and speed control of SRM is implemented, utilizing an asymmetrical converter with PI-PWM controller with the aid of Bio-inspired algorithms. By utilizing the best value of flux, torque ripple and integral square error of speed and settling times, controller design is performed. The proposed research work compares three optimization algorithms namely Crow Search Algorithm, an Improved Ant Lion Optimization Algorithm and Modified Chaotic Starling Particle Swarm technique to control the speed and torque reduction of SRM in aerospace applications. To obtain the optimal parameter values, optimization techniques are introduced and the optimized controller results are compared with other conventional controller results. In normal operation, the engine operates with two phases simultaneously, but it is modelled to meet the load specification, which is single and double phase faulty. Detection of fault gives a great outcome in control of an electrical drive system. Reduction of faulty operation time leads to better motor lifetime that is obtained by early detection of a fault. This paper presents how the five-phase switched reluctance motor is designed to meet the needs of flap actuators in mid-sized aircraft. Electrical systems are modelled to provide the right functionality at the right time in advanced aircraft. However, requirement of power for the landing gear and secondary flight control only a short duration. The system is powered on and off as required, thus preserving power. Control performance and fault analysis were verified by the results of the MATLAB simulation. Experiments have been carried out more effectively to compare the results obtained with those of simulations.

IMPROVED ANTLION OPTIMIZATION ALGORITHM FOR QUADRATIC ASSIGNMENT PROBLEM

The Antlion Optimization (ALO) algorithm is a meta-heuristic optimization algorithm based on the hunting of ants by antlions. The basic inadequacy of this algorithm is that it has long run time because of the random walk model used for the ant's movement. We improved some mechanisms in ALO algorithm, such as ants' random walking, reproduction, sliding ants towards antlion, elitism, and selection procedure. This proposed algorithm is called Improved Antlion Optimization (IALO) algorithm. To show the performance of the proposed IALO algorithm, we used different measurement metrics, such as mean best, standard deviation, optimality, accuracy, CPU time, and number of function evaluations (NFE). The proposed IALO algorithm was tested for different benchmark test functions taken from the literature. There are no studies regarding time analysis of ALO algorithm found in the literature. This study firstly aims to demonstrate the success of the proposed IALO algorithm especially in runtime analysis. Secondly, the IALO algorithm was also applied to the Quadratic Assignment Problem (QAP) known as a difficult combinatorial optimization problem. In QAP tests, the performance of the IALO algorithm was compared with the performances of the classic ALO algorithm and 14 well-known and recent meta-heuristic algorithms. The results of the benchmark test functions show that IALO algorithm is able to provide very competitive results in terms of mean best/standard deviation, optimality, accuracy, CPU time and NFE metrics. The CPU time results prove that IALO algorithm is faster than the classic ALO algorithm. As a result of the QAP tests, the proposed IALO algorithm has the best performance according to the mean cost, worst cost and standard deviation. The source codes of QAP with the proposed IALO algorithm are publicly available at https://github.com/uguryuzgec/QAP-with-IALO.

Effective and efficient classification of gastrointestinal lesions: combining data preprocessing, feature weighting, and improved ant lion optimization

This paper presents an approach that combines the data preprocessing, feature weighting, and the improved antlion optimization algorithm for effective and efficient classification of gastrointestinal lesions. A high-dimensional gastrointestinal lesion dataset that consists of extracted texture, color, and shape features from the colonoscopy videos is obtained from the UCI repository. The data has certain imperfections such as the presence of zero-valued features, outliers, and dominant features. So, it is preprocessed to cope with these problems. Then, feature weighting is used to boost the classification performance by assigning the weights to the features according to their relevance in classification. The improved antlion optimization algorithm is used to search for feature weights and the parameters of the Support Vector Machines simultaneously. The experiments are performed using different combinations of features and endoscopic images to analyse the performance. The outcomes show that the combination of texture and color features from NBI images is the best. The accuracy of 97.37% and 98.68% for multi-class and binary classification problems respectively is attained using only $$\sim$$ 31% features. Moreover, feature reduction helps to lower the runtime of the classifier by approx. 60%. In conclusion, a better approach is presented for colorectal lesions classification that competes with well-experienced colonoscopists and outperforms the existing methods.

Cloud Resource Optimization System Based on Time and Cost

Resource management in cloud could be a time and cost-effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud. In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource-management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

An Effective Method for Minimizing Electric Generation Costs of Thermal Systems with Complex Constraints and Large Scale

In this paper, an improved antlion optimization algorithm (IALO) was proposed to search for promising solutions for optimal economic load dispatch (ELD) problems to minimize electrical generation fuel costs in power systems with thermal units and to ensure all constraints are within operating ranges. IALO can be more effective than the original method, called the antlion optimization algorithm (ALO), because of the high performance of the applied modifications on the new solutions searching process. In order to evaluate the abilities of the IALO method, we completed many tests on thermal generating systems including 10, 15, 20, 30, 60, 80, and 90 units with different constraints and fuel-consuming characteristics. The results suggest that the offered method is superior to the ALO method with more stable search ability, faster convergence velocity, and shorter calculation times. Furthermore, the obtained results of the IALO method are much better than those of almost all the other methods used to solve problems for the same systems. As a result, IALO is suggested to be a highly effective method, and it can be applied to other problems in power systems instead of ALO, which has a lower performance.

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling

In this paper, we consider the parallel machine scheduling which is combinatorial optimization problem. An improved meta-heuristic method is proposed to solve this problem. Antlion optimization algorithm (ALO) is one of the new meta-heuristic optimization algorithms and it is inspired by the hunting mechanism of antlion in nature. The original algorithm has some deficiencies like the runtime. Some improvements on the classic ALO algorithm are presented in this study. One of these improvements is to use the tournament method instead of the roulette wheel method in the selection of the antlion from the population for the random walk which is the most important mechanism on the original ALO algorithm. Some mechanisms in the original ALO algorithm have been improved, such as ant random walking, reproduction, sliding of ants towards antlion, elitism and selection procedure. There is no time analysis result about ALO algorithm in the literature. Due to this reason, it is aimed to show the performance of the proposed IALOT algorithm especially in runtime analysis. The proposed improved ALO algorithm via tournament selection method (IALOT) has been compared with the other popular well-known meta-heuristic algorithms on the multi-dimensions benchmark test functions and the Parallel Machine Scheduling (PMS) problem to evaluate its performance. The obtained results show that the IALOT has the best performance in terms of the optimality, accuracy and mean best metrics for the benchmark tests. According to the PMS results, the proposed IALOT algorithm is the best algorithm in comparison with the other meta-heuristic algorithms.

A novel improved antlion optimizer algorithm and its comparative performance

In this study, the improvement of the ant lion optimization which is inspired by ant lion’s hunting strategy is dealt with. The most disadvantageous property of this algorithm is its having a long run time due to the random walking process. In order to overcome this drawback, we proposed the improved random walking model, tournament selection method instead of the roulette wheel selection method, and reproduction mechanism at the boundary values. The performance of improved ant lion optimization algorithm based on the tournament selection (IALOT) is evaluated in comparison with the commonly known and used heuristic algorithms for ten benchmark functions. Furthermore, we have tested the performance of IALOT on the training of ANFIS known as a difficult optimization problem. The benchmark and ANFIS test results show that IALOT algorithm exhibits better performance than that of the ALO algorithm.

Image Enhancement Based on Improved Antlion Optimization Algorithm

Swarm intelligence based image enhancement method is an important method to improve the image visual effect. In order to improve the image enhancement algorithm based on the antlion optimization, a quadratic interpolation technique is introduced to improve the image enhancement algorithm of the antlion optimization method which has the disadvantages of slow convergence speed and low computational accuracy. The new algorithm uses quadratic interpolation to accelerate the convergence speed of ant population and antlion population, to balance the global and local searching ability of the antlion algorithm, so as to improve the estimation precision of the optimal parameters in the incomplete Beta function and improve the image quality. The two evaluation indexes verify that the new algorithm is better than the contrast algorithm in statistical meaning, and extend the research of the antlion optimization algorithm.

An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification

In this paper, an improved ant lion optimization (IALO) algorithm for parameter identification of hydraulic turbine governing system (HTGS) is proposed. In the proposed algorithm, the search space is explored by the ant lion optimization first, and then the domain is searched by the particle swarm optimization (PSO) in each iteration cycle. A chaotic mutation operation namely Logistics map is introduced for the elite to break out of the local optimum. In mutation operation, a serial-parallel combined method is developed to increase the diversity of mutant population. When the proposed IALO algorithm is applied in the parameter identification of HTGS, the comparative simulation results show that the proposed IALO algorithm has the highest accuracy among different optimization algorithms, and the proposed IALO algorithm has a good convergence characteristic and high stability.

Parameter identification of photovoltaic cell model based on improved ant lion optimizer

For photovoltaic cell model, the accurate identification of parameters has a great impact on the prediction of power and the maximum power point tracking, so it always has a high demand on accuracy. Many intelligent algorithms can identify the parameters of the model, but there are the situations that the convergence speed is slow, influenced by the initial value, and easy to premature convergence. Ant lion optimizer (ALO) is a novel intelligent algorithm proposed in recent years, and it also has the problems mentioned above. The improved ant lion optimizer (IALO) arranges the initial positions of individuals by chaotic sequence to enhance the uniformity and ergodicity of population; The idea of particle swarm algorithm is introduced in the position updating of individuals, and the position of individuals are calculated based on the current best individuals and the global best individual to enhance the local and global searching capability; The dynamic contraction region in which the best individual is considered is used to decrease the search range and shorten the time of optimization efficiently. Comparing with particle swarm algorithm, bat algorithm and ant lion algorithm in the simulation, IALO algorithm is better than the other algorithm for four standard test functions. IALO algorithm is also used to identify the parameter of photovoltaic cell. The results show that, for Iph the average of IALO algorithm is 5.180, the average of ALO algorithm is 5.179, and the average of PSO algorithm is 5.052. For Io the average of IALO algorithm is 1.02, the average of ALO algorithm is 0.97, and the average of PSO algorithm is 0.87. For A the average of IALO algorithm is 48.0, the average of ALO algorithm is 37.4, and the average of PSO algorithm is 29.7. For Rs the average of IALO algorithm is 0.146, the average of ALO algorithm is 0.140, and the average of PSO algorithm is 0.142. For Rsh the average of IALO algorithm is 298.6, the average of ALO algorithm is 221.5, and the average of PSO algorithm is 188.9. So IALO algorithm is the best.