Colony Optimization Algorithm Research Articles

Path planning and tracking control of orchard wheel mower based on BL-ACO and GO-SMC

This research proposes an improved ant colony algorithm (BL-ACO) path planning algorithm and a tracking controller based on global optimal sliding mode variable structure control (GO-SMC) for the problem of path planning and tracking control of lawn mowers in quadrilateral orchard environments. The novelty of this research lies in two aspects. On one hand, we analyze the operating scenarios of lawn mowers in standardized orchards, then transform the path planning problem into a traveling salesman problem, and mathematically model the U-shaped and T-shaped turning strategies based on the characteristics of the wheeled lawn mower. In order to make the ant colony algorithm suitable for orchard operation path optimization problems, we modified its pheromone update rules, heuristic functions, state transition probabilities, and other equations. In order to accelerate the convergence speed of the ant colony algorithm, we use the bilayer ant colony algorithm optimization strategy. On the other hand, we establish a kinematic model with the wheeled lawn mower as the control object, and design a control law using a hyperbolic tangent function to ensure the global stability of the trajectory tracking control system. Furthermore, we demonstrate through Lyapunov stability analysis that the GO-SMC controller can ensure the mower tracks the reference path accurately. The simulation experiments of path planning and tracking control show that BL-ACO and GO-SMC perform the best compared to similar algorithms. Field experiments shows that BL-ACO & GO-SMC, with a time reduction rate of 47.58 % and a fuel consumption rate reduction of 47.59 % compared to line by line & SMC.

Logistics Routing Intelligence based on Improved Ant Colony Algorithm and Dijkstra Algorithm

As the domestic logistics industry still faces challenges in terms of efficiency and cost-effectiveness, the optimization of logistics routes is crucial to address these challenges. The purpose of this study is to explore the potential of using ant colony algorithm and route optimization algorithm to realize intelligent optimization of logistics routes. It first introduces the principle of ant colony algorithm and its application in path optimization, and then proposes a path intelligent optimization method based on ant colony algorithm by introducing pheromone concentration and heuristic information, which improves the efficiency and accuracy of the algorithm. Finally, the experimental results show that the improved ant colony algorithm can better plan the path than the traditional ant colony algorithm, and can improve the convergence speed of calculation, and can achieve higher optimization accuracy and calculation efficiency, so that it is suitable for the practical application in the field of logistics path intelligent optimization.

The optimization method based on the coupling of genetic algorithm and ant colony algorithm for the exhaust outlet space arrangement

The exhaust outlet space arrangement is a crucial part to avoid casualties and economic losses in the event of contaminant gas leakage. To handle this problem, this work proposed a novel optimization method based on the coupling of the genetic algorithm (GA) and ant colony algorithm optimization (ACO), and the fitness function used in the optimization method is constructed as an implicit form. In this proposed optimization method, the ACO is used to obtain the implicit fitness function value, while the GA is selected to conduct the space arrangement optimization based on the iteration results transferred from ACO. With the help of this novel methodology, the influence of obstacles in space could be well considered into the space arrangement optimization, which leads to a reliable optimization result of the exhaust outlet configuration. Moreover, to validate the accuracy and efficiency of this coupling method, the optimization results are taken into the computational fluid dynamics numerical model to give a comparison with the conventional configuration. The comparison results indicate that the exhaust outlet arrangement following the optimization results shows a lower gas concentration value during the diffusion process. In addition, based on this optimal exhaust outlet space arrangement, the models with various leakage rates are also investigated and discussed in the numerical work. It is believed that the proposed method could provide an effective measure for the space arrangement optimization and the design of gas leakage protection.

Optimal image 2D/3D by Krawtchouk moments and ABC algorithm

In our research, we provide an enhanced and effective Krawtchouk moments parameter p optimization approach using the Artificial Bee Colony (ABC) algorithm optimization technique for the purpose of completing tasks including the reconstruction and classification of 3D and 2D images with high quality. The proposed method, which uses the ABC algorithm to generate a suitable parameter based on Krawtchouck moments, aims to accurately characterize the image as a vector known as the descriptor vector of moments, which is then used to reconstruct the image for each order. The simulation and analysis of the findings demonstrate the high level of quality and effectiveness of the suggested KM-ABC approach for image reconstruction, as well as its excellent accuracy in images with and without noise.

Eight-link pressure mechanism and its dynamic simulation based on improved ant colony algorithm optimization

Abstract In recent years, the optimization of eight-link pressure mechanism using ant colony algorithm is a research hotspot, but given the limitations of traditional ant colony algorithm, this paper improves the ant colony algorithm based on the SSA-IACO algorithm and develops the application and analysis of eight-link pressure mechanism and its dynamic simulation. Through the study of parameter settings before and after optimization, simulation analysis and other aspects, it is concluded that: the efficiency of the new scheme optimized by the improved ant colony algorithm is 88.86% higher than the original scheme. The stator power is reduced by 4.10% and the power factor is increased by 14.67% on average over the original scheme. The maximum magnitude of the free end torsion angle of the crankshaft system is 0.16 degrees, and the increased resonance speed is about 1800 rpm. The maximum torsion angle of the main harmonic (6th harmonic) is reduced from 0.54 to 0.16, and the damping effect is noticeable. When 0.7 ≤ ρ 0.8, and ρ is close to 0.7, the algorithm performs better. When 0.15 ≤ θ 0.2, and the value of θ is more comparable to 0.15, the algorithm obtains the optimal solution and the execution time is shorter than the [0.7,0.2] combination.

Fuzzy proportional integral differential control of magnetorheological clutch torque based on bee colony algorithm optimization

As an intelligent transmission device, the magnetorheological (MR) clutch cannot realize its accurate torque control because it is difficult to establish its accurate torque control mathematical model. This paper designs a squeeze shear MR clutch. In order to achieve accurate torque control of the MR clutch, this paper proposes a fuzzy proportional integral differential (PID) control strategy based on the optimization of the bee colony algorithm. Based on the mechanical transmission experiment platform of the MR clutch, the performance of the torque control of the MR clutch is experimentally studied by using the real-time operating system Phar Lap ETS built by LabVIEW real-time module, it is found that the fuzzy PID control strategy based on bee colony algorithm can not only improve the torque control accuracy of the MR clutch by 70% compared to traditional PID control strategies but also increase the response speed by more than 50%.

A comparative analysis of ant colony and genetic algorithm optimization to select optimal arrangement of viscous damper in steel frame

A comparative analysis of ant colony and genetic algorithm optimization to select optimal arrangement of viscous damper in steel frame

A robust optimal control by grey wolf optimizer for underwater vehicle-manipulator system.

Underwater vehicle-manipulator system (UVMS) is a commonly used underwater operating equipment. Its control scheme has been the focus of control researchers, as it operates in the presence of lumped disturbances, including modelling uncertainties and water disturbances. To address the nonlinear control problem of the UVMS, we propose a robust optimal control approach optimized using grey wolf optimizer (GWO). In this scheme, the nonlinear dynamic model of UVMS is deduced to a linear state-space model in the case of the lumped disturbances. Then, the GWO algorithm is used to optimize the Riccati equation parameters of the H∞ controller in order to achieve the H∞ performance criterion, such as stability and disturbance rejection. The optimization is performed by evaluating the performance of the closed-loop UVMS in real-time comparison with the popular artificial intelligent algorithms, such as as ant colony algorithm (ACO), genetic algorithm (GA), and particle swarm optimization (PSO), using feedback control from the physical hardware-in-the-loop UVMS platform. This scheme can result in improved H∞ control system performance, and it is able to ensure that UVMS has strong robustness to these lumped disturbances. Last, the validity of the proposed scheme can be established, and its performance in overcoming modeling uncertainties and external disturbances can be observed and analyzed by performing the hardware-in-the-loop experiments.

Using artificial intelligence algorithms to reconstruct the heat transfer coefficient during heat conduction modeling

The article shows the usage of swarming algorithms for reconstructing the heat transfer coefficient regarding the continuity boundary condition. Numerical calculations were performed using the authors’ own application software with classical forms of swarm algorithms implemented. A functional determining error of the approximate solution was used during the numerical calculations. It was minimized using the artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO). The considered in paper geometry comprised a square (the cast) in a square (the casting mold) separated by a heat-conducting layer with the coefficient kappa . Due to the symmetry of that geometry, for calculations, only a quarter of the cast-mold system was considered. A Robin’s boundary condition was assumed outside the casting mold. Both regions’ inside boundaries were insulated, but between the regions, a continuity boundary condition with nonideal contact was assumed. The coefficient of the thermally conductive layer was restored using the swarm algorithms in the interval 900{-}1500 ; [mathrm{W/m}^{2}textrm{K} ] and compared with a reference value. Calculations were carried out using two finite element meshes, one with 111 nodes and the other with 576 nodes. Simulations were conducted using 15, 17, and 20 individuals in a population with 2 and 6 iterations, respectively. In addition, each scenario also considered disturbances at 0%, 1%, 2%, and 5% of the reference values. The tables and figures present the reconstructed value of the kappa coefficient for ABC and ACO algorithms, respectively. The results show high satisfaction and close agreement with the predicted values of the kappa coefficient. The numerical experiment results indicate significant potential for using artificial intelligence algorithms in the context of optimization production processes, analyze data, and make data-driven decisions.

Optimal Location and Sizing of Photovoltaic-Based Distributed Generations to Improve the Efficiency and Symmetry of a Distribution Network by Handling Random Constraints of Particle Swarm Optimization Algorithm

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.

A Constitutive Model Study of Chemical Corrosion Sandstone Based on Support Vector Machine and Artificial Bee Colony Algorithm

The mechanical characteristics of rock are greatly influenced by hydrochemical corrosion. The chemical corrosion impact and deformation properties of the meso-pore structure of rock under the action of different hydrochemical solutions for the stability evaluation of rock mass engineering are of high theoretical relevance and applied value. Based on actual data, a support vector machine (SVM) rock constitutive model based on artificial bee colony algorithm (ABC) optimization is constructed in this article. The impact of porosity (chemical deterioration), confining pressure, and other aspects is thoroughly examined. It is used to mimic the triaxial mechanical behavior of rock under various hydration conditions, with high nonlinear prediction ability. Simultaneously, the statistical damage constitutive model and the ABC-SVM constitutive model are used to forecast the sample’s stress–strain curve and compare it to the experimental data. The two models’ correlation coefficients (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) are computed and examined. The correlation coefficient between the ABC-SVM constitutive model calculation results and the experimental results is found to be larger (R2 = 0.998), and the error is smaller (RMSE = 0.7730, MAPE = 1.51), indicating that it has better prediction performance on the conventional triaxial constitutive relationship of rock. It is a highly promising new way of describing the rock’s constitutive connection.

A Novel MPPT Method Based on PSO and ABC Algorithms for Solar Cell

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.

Test case prioritization for changed code using nature inspired optimizer

The software development and maintenance phase succeeded with significant regression testing activity. The software must be re-tested every time it upgrades to preserve its quality. Software testing as a whole is an expensive and tedious task due to resource constraints. Using the prioritization technique implies regression testing to re-test software after it has been modified. In this situation, the prioritization technique can use information acquired about earlier test case executions to generate test case orderings. The approaches for test case prioritization arrange them all in such a sequence that maximizes their efficacy in accomplishing specific goals. This paper presents a hybrid technique for change-testing or regression testing through test case prioritization. The suggested method first generates the test cases, then clustered in untested and unimportant groups using kernel-based fuzzy c-means clustering technique. The appropriate test cases are then considered for prioritization using the grey wolf optimizer. The results compared with the approaches such as ant colony, particle swarm, and genetic algorithm optimization method, and it is observed that the proposed approach efficiency increased by 91% of fault detection rate.

Improvement of Ant Colony Algorithm Performance for the Job-Shop Scheduling Problem Using Evolutionary Adaptation and Software Realization Heuristics

Planning tasks are important in construction, manufacturing, logistics, and education. At the same time, scheduling problems belong to the class of NP-hard optimization problems. Ant colony algorithm optimization is one of the most common swarm intelligence algorithms and is a leader in solving complex optimization problems in graphs. This paper discusses the solution to the job-shop scheduling problem using the ant colony optimization algorithm. An original way of representing the scheduling problem in the form of a graph, which increases the flexibility of the approach and allows for taking into account additional restrictions in the scheduling problems, is proposed. A dynamic evolutionary adaptation of the algorithm to the conditions of the problem is proposed based on the genetic algorithm. In addition, some heuristic techniques that make it possible to increase the performance of the software implementation of this evolutionary ant colony algorithm are presented. One of these techniques is parallelization; therefore, a study of the algorithm’s parallelization effectiveness was made. The obtained results are compared with the results of other authors on test problems of scheduling. It is shown that the best heuristics coefficients of the ant colony optimization algorithm differ even for similar job-shop scheduling problems.

OPTIMIZATION OF SUPPORT VECTOR MACHINES BY META-HEURISTIC APPROACH FOR DIFFERENTIAL DIAGNOSIS AND CLASSIFICATION OF ORAL DISEASES

Classifying cancers as different groups is the key to a more accurate diagnosis. In every case, the goal is to reach an in-time diagnosis of the disease. The diagnosis, prediction, and control of oral cancers have been based on clinical and histopathological symptoms so far. Given the failure to confirm an in-time diagnosis in all the conventional methods, multiple biopsies are necessary for reaching a differential diagnosis, although patients are not satisfied with such biopsies. Machine learning algorithms can be employed to prevent such complications and reach faster and more accurate diagnosis. In this paper, Support Vector Machine algorithms were used for a four-class classification of a dataset containing 210 real samples. The research goal was to classify patients’ cancers as four classes: normal, lichen planus, oral squamous cell carcinoma, and leukoplakia. To improve the accuracy of the classifier, the Support Vector Machine was combined with meta-heuristic algorithms such as Artificial Bee Colony algorithm and Ant Colony Optimization to propose a new approach. The proposed diagnostic approach showed a classification accuracy of 94.28%.

Optimal Energy Management of Virtual Power Plants with Storage Devices Using Teaching-and-Learning-Based Optimization Algorithm

In recent decades, Renewable Energy Sources (RES) have become more attractive due to the depleting fossil fuel resources and environmental issues such as global warming due to emissions from fossil fuel-based power plants. However, the intermittent nature of RES may cause a power imbalance between the generation and the demand. The power imbalance is overcome with the help of Distributed Generators (DG), storage devices, and RES. The aggregation of DGs, storage devices, and controllable loads that form a single virtual entity is called a Virtual Power Plant (VPP). In this article, the optimal scheduling of DGs in a VPP is done to minimize the generation cost. The optimal scheduling of power is done by exchanging the power between the utility grid and the VPP with the help of storage devices based on the bidding price. In this work, the state of charge (SOC) of the batteries is also considered, which is a limiting factor for charging and discharging of the batteries. This improves the lifetime of the batteries and their performance. Energy management of VPP using the teaching-and-learning-based optimization algorithm (TLBO) is proposed to minimize the total operating cost of VPP for 24 hours of the day. The power loss in the VPP is also considered in this work. The proposed methodology is validated for the IEEE 16-bus and IEEE 33-bus test systems for four different cases. The results are compared with other evolutionary algorithms, like Artificial Bee Colony (ABC) algorithm and Ant Lion Optimization (ALO) algorithm.

Online Labor Education Optimization Method Based on Computer Intelligent Algorithm.

People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education.

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.

Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

PurposeCoronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.Design/methodology/approachIn this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.FindingsThe main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.Originality/valueThe novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.

Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm

Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Berger model is analyzed through modeling by the human metamodel. Based on this, the global search capability of genetic algorithm is introduced into the initial information allocation process so as to integrate the genetic algorithm and ant colony algorithm and then apply them in the cloud computing resource allocation process. As can be learned from the simulation results, the proposed method can comprehensively improve user satisfaction and resource utilization while shortening the total execution time of tasks.