Improved particle swarm optimization based on particles' explorative capability enhancement

Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.

Analyzing Cavitation Caused by Metal Particles in the Transaxle： Application of High Quality Assurance CAE Analysis Model

This paper focuses on a strategic improving quality of＂high quality assurance CAE （computer aided engineering） analysis model＂ to be used in development design. The authors present a case oftransaxle oil seal leakage in automotive drive trains, a technical reliability problem that generates bottlenecks for auto manufacturers around the world. The application of this model is used to analyze cavitation caused by the metal particles （foreign matter） generated through transaxle wear. This analyzing method primarily uses numerical simulation （CAE） to clarify the technological mechanism generating oil leaks as a result of foreign metallic substances entering oil seals in the drive train. Quality improvement using this CAE analyzing method was verified by successfully applying it to the technological problem of development design bottlenecks at auto manufacturers.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

3D Printing with a 5-Axis FDM Printer Using Bézier Techniques

In the last decade,3D printing,especially fused deposition modeling(FDM),has revolutionized manufacturing with intricate designs.Traditional 3-axis FDM printers face challenges with complex geometries,but 5-axis versions offer more design freedom.However,it requires specialized strategies.This research presents a model for 5-axis FDM printers using Bézier curves with an algorithm to enhance print quality.The result shows significant accuracy improvements,especially for curve-based tasks.In addition,this study deepens the understanding of 5-axis FDM technology,setting a solid basis for further research and potentially refining manufacturing methods.

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.

An Optimization Algorithm for Service Composition Based on an Improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

A comprehensive review of recent advancements and developments in heat exchanger network synthesis techniques

The rapid development of computational technology and the increasing energy demand have improved heat exchanger network(HEN)synthesis.The HEN synthesis involves several optimizations of matches,distributions of heat loads,and stream splitting of heat units.Thus,obtaining good results at high efficiency has been the main standard for evaluating the techniques in the research area of HEN synthesis.This paper first summarizes and analyzes the main contributions of the existing HEN synthesis techniques.To compare related data quantitively,information on ten typical cases is presented in this paper.Furthermore,recently improved solutions for commonly encountered existing literature cases demonstrate the evolution and competition trends in the field of HEN synthesis.The comparison data presented in this paper not only provide a useful reference for future research but also present the optimization directions.Based on the findings of this study,it is noted that there is still a large room for improvement,and current approaches are incapable of dealing with all HEN cases.Moreover,it is still difficult to escape a local optimum and overcome structural constraints when seeking the global optimum.As a follow-up to the current work,the parallel computing mode and adaptively coordinating the ratio of global and local searching abilities are major development trends for future investigation.

Theoretical search for solutions to minimize negative influence of segregation in mixing of particulate solids

The objective of this study is to examine several optimization problems in the batch mixing of segregating particulate solids that can be set up and solved using Markov chain models. To improve the adequacy of such models and exclude some physical contradictions that arise in the linear form, a non-linear Markov chain model for the mixing of segregating components is proposed. Optimal solutions are obtained by controlling the particle flow outside the mixing operating volume while the components are being loaded, modifying particle circulation inside the mixing zone during the process, and by structuring the load in the mixing zone. Solutions are found that not only reduce the negative influence of segregation, but also exclude it altogether. The gain resulting from optimization grows with the rate of segregation. The optimal solutions presented here can be used to improve the design of mixers.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM(1,1) model

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.