Artificial Bee Colony Particle Swarm Optimization Research Articles

Quality–efficiency coupling prediction and monitoring-based process optimization of thin plate parts with multi-machining feature

ABSTRACT Quality and efficiency prediction, as well as coupling optimization, is very important for improving product production. However, most of the researches are studying the quality and efficiency separately, which makes it difficult to improveproduction. Therefore, this paper proposes a quality–efficiency coupling prediction and monitoring-based process optimization method to effectively improve the quality and efficiency of thin plate parts with multi-machining features at the same time. And the best process parameters are recommended to better improve machining stability. Firstly, based on the generalized multi-layer residual network and deep neural network (MLResNet-DNN), the prediction models of quality and efficiency are constructed, respectively. Secondly, the quality–efficiency coupling index is constructed based on coupled permutation entropy (CPE) accordingly. Finally, the process optimization model based on the hybrid artificial bee colony–particle swarm optimization (HABC-PSO) algorithm is established to recommend the best process parameters according to the monitoring results of quality–efficiency CPE. The RMSE average value of the proposed quality and machining time prediction model has an average improvement of at least 10.8% and 25.9%, respectively, than other prediction model. The process parameters recommended by the proposed HABC-PSO method have improved the machining stability of quality and efficiency by at least 25.6%, and machining time is reduced by at least 25.7% compared with other optimization algorithms.

Combined Economic and Emission Power Dispatch Control Using Substantial Augmented Transformative Algorithm

The purpose of the Combined Economic Emission Dispatch (CEED) of electric power is to offer the most exceptional schedule for production units, which must run with both low fuel costs and emission levels concurrently, thereby meeting the lack of system equality and inequality constraints. Economic and emissions dispatching has become a primary and significant concern in power system networks. Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations. The optimal power allocation to generators serves as a solution to this problem. Emission dispatch reduces emissions while ignoring economic considerations. A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions. Consequently, this work manages the Substantial Augmented Transformative Algorithm (SATA) to take care of the Combined Economic Emission Dispatch Problem (CEEDP) of warm units while fulfilling imperatives, for example, confines on generator limit, diminish the fuel cost, lessen the emission and decrease the force misfortune. SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms. The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution. The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search. The simulation of the proposed system is developed using MATLAB Simulink software. Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues. The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Simulated Annealing (SA), and Differential Evolution (DE) methods. The fuel cost and gas emission of the proposed system are 128904 $/hr and 138094.4652$/hr.

Enhancing the performance of hybrid evolutionary algorithms in microarray colon data classification

AbstractMicroarray technology aids in monitoring simultaneous gene expressions of human cells for diagnosis and treatment of infectious diseases in a single experiment. The microarray data are of high dimension with a large number of genes and fewer samples. Highly informative features and appropriate classifiers are necessary to increase the accuracy of disease classification. In this paper, a publicly available colon cancer dataset is analyzed for classification. At first, Power Spectral Density (PSD) technique is employed to obtain the reduced gene features and after that, they are classified using various types of classifiers for colon cancer classification. To improve the classification accuracy, the hybridized evolutionary classifiers, namely, Artificial Bee Colony‐Firefly (ABC‐Firefly), Artificial Bee Colony‐Particle Swarm Optimization (ABC‐PSO), Particle Swarm Optimization‐Firefly (PSO‐Firefly) are derived from the conventional evolutionary classifiers, namely, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Firefly classifiers. The performance of the three hybridized evolutionary algorithms is compared with the conventional classifiers. Additionally, the comparison was also performed on the conventional and hybridized evolutionary algorithms using Expectation Maximization (EM) and cascaded Power Spectral Density‐Expectation Maximization (PSD‐EM) dimensionality reduction techniques. Although there are many classifiers for colon classification, it is wise to use PSD features with a Hybrid ABC‐PSO classifier for enhanced performance. The experimental results reveal that the Hybrid ABC‐PSO classifier with PSD features achieves 98.96% for colon cancer samples and 100% accuracy for colon normal samples when compared with the other classifiers reported in the literature.

Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems

The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

WITHDRAWN: Stabilized micro grid using enhanced ANFIS-PID with SVPWM controller through HAPSO optimized selective harmonic elimination

Abstract The Microgrid (MG) paradigm is developing as a striking solution for the designs of future grids. In islanded micro grid, the variances in the system cause MG flimsiness. A portion of the controllers used to handle such issue are powerless against stack aggravation and results in an impressive voltage vacillation, or frequency variety, of the micro grid. Subsequently in this examination work we propose a novel fell controller system with the incorporation of EANFIS-PID controller and SVPWM controller. The EANFIS-PID controller is responsible for the erratic irregularities of voltage and frequency though the SVPWM controller is in charge of current vacillations that are the higher request harmonics. The proposed fell controller in this manner produces a balanced out micro grid with higher system effectiveness. Likewise a novel Hybrid Artificial Bee Colony-Particle Swarm Optimization (HAPSO) based Selective Harmonic Elimination (SHE) conspire is utilized in SVPWM controller for wiping out the harmonics produced by the VSC inverter. The proposed system is confirmed through MATLAB/SIMULINK stage and from the reproduction comes about it has been demonstrated that this outcomes in a more responsive control activity contrasted with the conventional controllers.

Damage identification in three-dimensional structures using single-objective evolutionary algorithms and finite element model updating: evaluation and comparison

ABSTRACTThis study presents a methodology which integrates single-objective evolutionary algorithms (EAs) and finite element (FE) model updating for damage inference in three-dimensional (3D) structures. First, original well-known EAs, namely the genetic algorithm, differential evolution (DE) and particle swarm optimization (PSO), are combined with FE model updating for detecting damage in a 3D four-storey modular structure and their performances are compared. Next, to obtain more accurate results, hybrid Lévy flights–DE and hybrid artificial bee colony–PSO are developed for enhancing damage identification. With each method, the objective function composed of modal strain energy and mode shape residuals, taken from the FE model of the intact structure and the simulated damage responses, is initially created. Then, the performance of each algorithm combined with FE model updating for damage detection is assessed in terms of three characteristics: consistency, computational cost and accuracy, and the best performing algorithm is recommended.

PMDC Motor Parameter Estimation Using Bio-Inspired Optimization Algorithms

The precise estimation of the motor parameter is essential to design the appropriate controller. The main goal of this paper is to estimate the parameters of permanent magnet dc (PMDC) motor used in a wheelchair, applying standard as well as a dynamic particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC) along with experimental methods. The electromechanical, mechanical, and electrical parameters, such as torque constant, back-emf constant, moment of inertia, viscous friction coefficient, armature inductance, and resistance are estimated using both the experimental and optimization methods. The motor is modeled in Matlab/Simulink R2015a using the estimated motor parameters and studied the performance with different loading conditions starting from no-load to full-load. The simulated results of motor performance with estimated parameters are compared with the experimental load test results. The results showed that the PMDC motor parameters estimated from dynamic PSO with varying inertia weight as well as ABC algorithm have comparatively very less speed and current error than standard PSO, dynamic PSO with constant inertia weight, and ACO algorithms. Furthermore, parameters from dynamic PSO with varying inertia weight showed speed as well as current error less than 0.5%, and the ABC algorithm shown current error slightly more than 0.5%. However, the analysis of variance tests shown no significant difference in current and speed performance with parameter estimated from ABC and dynamic PSO with varying inertia weight. Furthermore, ABC algorithm convergence is faster than dynamic PSO with varying inertia weight. But parameters estimated from dynamic PSO with varying inertia weight are precise and may be appropriate for the design of the motor controllers.