Papers by Keyword: Hybrid Optimization

Abstract: Stainless steel 304 is one of the most promising materials for many industrial applications. Its machinability is poor whether machined in any environment. Conventional machining causes environmental degradation as well. In this paper, sustainable machining of SS304 using green lubricant is presented. Experiments have been conducted on Taguchi’s robust design of experiment technique. For machinability enhancement, a hybrid optimization technique VIKOR-Regression-PSO is employed. Machinability indicators that have been considered are tool wear, surface roughness, and chip reduction coefficient. Cutting speed, depth of cut, and feed rate have been considered as the variable machining parameters in this work. The hybrid optimization has been found very effective and provided a set of optimum machining parameters i.e. cutting speed-70m/min; feed rate-0.1mm/rev; depth of cut-0.5mm for the best values of machinability indicators i.e. tool wear-249.22 μm, roughness-11.08 μm, chip reduction coefficient-2.26.

Abstract: Invasive weed optimization algorithm is a new swarm intelligence algorithm recently. The algorithm has better robustness and adaptation, which is a very good intelligent optimization tools; but it is easy to fall into local optimization, and having the low speed of convergence, and it can not acquire exactly. Aiming at the shortcomings of the algorithm, taking advantage of pattern search excellent local search ability, this paper presents a novel hybrid optimization algorithm of pattern search algorithm and IWO optimization. The Simulation results of three standard benchmark functions show that the improved algorithm can greatly improve the convergence precision and convergence speed, and can effectively discourage the premature convergence.

Abstract: This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

Abstract: To allow utilities to fulfill self-imposed and regulative performance targets the demand for new optimized tools and techniques to Estimate the performance of modern Transformers has increased. The modern power transformers has subjected to different types of faults, which affect the continuity of power supply which in turn causes serious economic losses. To avoid the interruption of power supply, various fault diagnosis approaches are adopted to detect faults in the power transformer and has to eliminate the impacts of the faults at the initial stage. Among the fault diagnosis methods, the hybrid technique of Particle Swarm Optimization (PSO) with Support Vector Machine (SVM) learning algorithm is simple conceptually derived and its implementation process is faster with better scaling properties for complex problems with non linearity and load variations but performance factor related to accuracy has a declined value in case of correlations implicit . In order to obtain better fault diagnosis to improve the service of the power transformer, SVM is optimized with Improved PSO technique to achieve high interpretation accuracy for Dissolved Gas Analysis (DGA) of power transformer through the extracting positive features from both the techniques. Primary SVM is applied to establish classification features for faults in the transformer through DGA. The features are applied as input data to Autonomous optimized Technique for faults analysis. The proposed methodology obtains the DGA data set from diagnostic gas in oil of 500 KV main transformers of Pingguo Substation in South China Electric Power Company. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 4 GB RAM PC. The result obtained by Autonomous optimized Technique (IPSO-SVM) is compared against PSO-SVM to estimate the performance of the classifiers in terms of execution time and quality of classification for precision. The test results indicate that the Autonomous optimization of IPSO-SVM approach has significantly improved the classification accuracy and computational time for power transformer fault classification. Keywords: Transformer Fault Analysis, Improved Particle Swarm Optimization, Hybrid Optimization, Dissolved Gas Analysis, Support Vector Machine

Abstract: For the dynamic optimization and control problem of the signalized arterial network in urban, a dynamic traffic flow model based on multi-phase control is firstly formulated, in which the total number of the retained vehicles through the arterial during the control period is adopted as the optimization objective and the green times and offsets as the control variables. Then a hybrid optimization method based on real-coded genetic algorithm and local search technique is designed to solve the optimization problem. For examining the validity of the optimization and control method proposed, it is applied to a case study with dynamic traffic demands and a large number of simulations show that the dynamic optimization and control method proposed in this paper can work well for the signalized arterial network.

Abstract: The inverse problem of structure damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Based on the hybrid optimization strategy, the parameter identification algorithm was presented according to the measured data of vibrating frequency and mode shapes in the damaged structure. The proposed algorithm combines the local optimum method having fast convergence ability with the neural networks having global optimum ability. By doing this, the local minimization problem of the local optimum method can be solved, and the convergence speed of the global optimum method can be improved. The investigation shows that to identify the location and magnitude of the damaged structure by using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.