Performance Evaluation of Evolutionary Algorithms for Analog Integrated Circuit Design Optimisation

An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function that has resulted in faster convergence to the optimal solution. The algorithms are tuned and modified to converge to a better optimal solution with less standard deviation for multiple runs compared to standard versions. Modified versions of the artificial bee colony optimisation algorithm, genetic algorithm, grey wolf optimisation algorithm, and particle swarm optimisation algorithm are tested and compared for the optimal sizing of two operational amplifier topologies. An extensive performance evaluation of all the modified algorithms showed that the modifications have resulted in consistent performance with improved convergence for all the algorithms. The implementation of parallel computation in the algorithms has reduced run time. Among the considered algorithms, the modified artificial bee colony optimisation algorithm gave the most optimal solution with consistent results across multiple runs

INTELLIGENT MODELLING OF GRADIENT FLEXIBLE PLATE STRUCTURE UTILISING HYBRID EVOLUTIONARY ALGORITHM

The gradient flexible plate structure has been widely used in engineering industries. However, the gradient flexible plate is susceptible to vibrational disturbances and affecting its durability and performance over time. Hence, the unwanted vibration needs to be controlled and can be accomplished by developing an accurate model. Despite that, the accurate model is hard to be obtained especially in estimating the model parameters. Thus, the research presents the development of dynamic modelling for gradient flexible plate structure (GFPS). A slanted GFPS with orientation angle of 30° and all edges clamped was developed and fabricated to represent the actual dynamics of the system. Then, data acquisition and instrumentation system were integrated to the rig to collect the input-output vibration data. The research utilised parametric system identification based on autoregressive with exogenous input (ARX) model structure. First, evolutionary algorithms, namely particle swarm optimisation (PSO) and grey wolf optimisation (GWO) were used in developing GFPS dynamic model and their performances were compared. It was discovered that GWO model outperformed PSO model. However, the computational time of GWO is slower compared to PSO. Thus, a hybrid of grey wolf and particle swarm optimisation (GWO-PSO) were proposed to further improve the system modelling. It was found out that the hybrid GWO-PSO model outperformed PSO and GWO models by achieving the lowest mean squared error, correlation up to 95 % confidence level, and good stability. The obtained GWO-PSO models which is model order 2 and model order 4 were verified by using proportional-integral-derivative (PID) based controller. Their performances were measured in terms of model robustness based on vibration suppression. The final result confirmed that model order 2 of GWO-PSO is the optimum model to represent GFPS system modelling with 71.08% vibration attenuation

Genetic Algorithms For Flow-shop Scheduling Optimization Of An Automated Assembly Line

Manufacturing process is a process of producing and creating a product with the use of technologies and machinery resources. In manufacturing process there are three dimensions, which are important in improving the system. These are cost, quality, and speed that can be considered as basics of every process. In this thesis speed of the manufacturing process is enhanced, which leads to reduction in cost as well. Assembly lines are the part of manufacturing process to convert raw materials into finished products. Considering optimization problems in assembly lines, applying genetic algorithms to the established model could lead to efficient manufacturing. Genetic algorithm is a programming search technique for maximizing productivity, minimizing inefficiency and reducing production time. This work presents an approach for developing simulation models used for optimization of production lines. The results are demonstrated using the assembly line which is located in FAST-Lab. at Tampere University of Technology. The simulation of the line is created to assess cycle times and utilization of workstations using MATLAB and SimEvents library. The optimization, in the context of presented work, is the process of locating and scheduling the products in the line achieving best timing to fulfil production orders. The workstations can be first balanced for better performance and then products are scheduled based on reduction of the production time

Crushing Plant Dynamics

The performance of a crushing plant is an essential element in achieving efficient production of aggregates or metals. A crushing plant\ub4s operating performance depends on the design and configuration of each individual process unit, the configuration of the plant, the design of the control system, events occurring in the process and the physical properties of the incoming feed. The production process is a continuous process and as such it is also subjected to variations and changes in performance depending on the condition of the process. Crushing plants however, are traditionally simulated with steady-state simulation models which are not capable of predicting these conditions. A different technique is therefore necessary in order to estimate the actual behaviour of the plant with respect to time.Crushing plants are affected by both gradual and discrete changes in the process over time which alters the performance of the entire system, making it dynamic. A dynamic simulation is defined in this thesis as continuous simulations with sets of differential equations with static equations to reproduce the dynamic performance of a system. In this thesis multiple operational issues have been identified in order to achieve adequate process fidelity for simulation purposes. These operational issues have been addressed by introducing methods and models for representing different dynamic aspects of the process. These include: different types of bins to handle misaligned feeding, segregation and different flow behaviour, the use of system identification to measure actuator response to accurately estimate unit response, wear estimation for crushers, mechanistic models for crushers and screens for more accurate estimation of unit dynamics, segmented conveyors that can estimate material flow for conveyors with variable speed drives, parameter selection for optimum process performance, discrete events that occur within the process and different control strategies to capture the process dynamics.Different applications for dynamic simulation have been explored and demonstrated in this thesis. These include: process evaluation, control development, process optimization, operational planning, maintenance scheduling and operator training. Each of these areas puts different constraints on the modelling of crushing plants and the level of fidelity, which is determined by the purpose of the simulation.In conclusion, dynamic simulation of production processes has the ability to provide the user with in-depth understanding about the simulated process, details that are usually not available with static simulations. Multiple factors can affect the performance of a crushing plant, factors that need to be included in the simulation to be able to estimate the actual plant performance

Crushing Plant Dynamics

The performance of a crushing plant is an essential element in achieving efficient production of aggregates or metals. A crushing plant\ub4s operating performance depends on the design and configuration of each individual process unit, the configuration of the plant, the design of the control system, events occurring in the process and the physical properties of the incoming feed. The production process is a continuous process and as such it is also subjected to variations and changes in performance depending on the condition of the process. Crushing plants however, are traditionally simulated with steady-state simulation models which are not capable of predicting these conditions. A different technique is therefore necessary in order to estimate the actual behaviour of the plant with respect to time.Crushing plants are affected by both gradual and discrete changes in the process over time which alters the performance of the entire system, making it dynamic. A dynamic simulation is defined in this thesis as continuous simulations with sets of differential equations with static equations to reproduce the dynamic performance of a system. In this thesis multiple operational issues have been identified in order to achieve adequate process fidelity for simulation purposes. These operational issues have been addressed by introducing methods and models for representing different dynamic aspects of the process. These include: different types of bins to handle misaligned feeding, segregation and different flow behaviour, the use of system identification to measure actuator response to accurately estimate unit response, wear estimation for crushers, mechanistic models for crushers and screens for more accurate estimation of unit dynamics, segmented conveyors that can estimate material flow for conveyors with variable speed drives, parameter selection for optimum process performance, discrete events that occur within the process and different control strategies to capture the process dynamics.Different applications for dynamic simulation have been explored and demonstrated in this thesis. These include: process evaluation, control development, process optimization, operational planning, maintenance scheduling and operator training. Each of these areas puts different constraints on the modelling of crushing plants and the level of fidelity, which is determined by the purpose of the simulation.In conclusion, dynamic simulation of production processes has the ability to provide the user with in-depth understanding about the simulated process, details that are usually not available with static simulations. Multiple factors can affect the performance of a crushing plant, factors that need to be included in the simulation to be able to estimate the actual plant performance

Truck scheduling problem in a cross-docking system with release time constraint

Abstract In a supply chain, cross-docking is one of the most innovative systems for ameliorating the operational performance at distribution centers. Cross-docking is a logistical strategy in which freight is unloaded from inbound trucks and (almost) directly loaded into outbound trucks, with little or no storage in between, thus no inventory remains at the distribution center. In this study, we consider the scheduling problem of inbound and outbound trucks with multiple dock doors, aiming at the minimization of the makespan. The considered scheduling problem determines where and when the trucks must be processed; also due to the interchangeability specification of products, product assignment is done simultaneously as well. Inbound trucks enter the system according to their release times', however, there is no mandatory time constraint for outbound truck presence at a designated stack door; they should just observe their relative docking sequences. Moreover, a loading sequence is determined for each of the outbound trucks. In this research, a mathematical model is derived to find the optimal solution. Since the problem under study is NP-hard, a simulated annealing algorithm is adapted to find the (near-) optimal solution, as the mathematical model will not be applicable to solve largescale real-world cases. Numerical examples have been done in order to specify the efficiency of the metaheuristic algorithm in comparison with the results obtained from solving the mathematical model

A simulation modelling approach to improve the OEE of a bottling line

This dissertation presents a simulation approach to improve the efficiency performance, in terms of OEE, of an automated bottling line. A simulation model of the system is created by means of the software AnyLogic; it is used to solve the case. The problems faced are a sequencing problem related to the order the formats of bottles are processed and the buffer sizing problem. Either theoretical aspects on OEE, job sequencing and simulation and practical aspects are presented

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Efficient optimization methods for analog/mixed-signal integrated circuits via machine learning

During the analog design process, a significant amount of human effort is spent on optimizing circuit specifications by tuning the device parameters. Sizing device parameters is the task of obtaining satisfactory performance for certain constraint metrics and minimizing/maximizing other objective metrics. In general, an initial optimization is conducted based on schematic-level electrical simulations. However, the Analog/Mixed-Signal (AMS) Integrated Circuits (IC) design is also sensitive to the parasitics introduced during the layout. Therefore, a more comprehensive approach is to size device parameters under the consideration of layout parasitics. To automate this process, many automation methods are proposed where simulation feedback is integrated into the automation loop for an accurate evaluation of design choices. AMS simulations are typically costly to run; therefore, the automation method's cost is crucial. This dissertation proposes efficient automated solutions to solve the AMS sizing problem. First, this dissertation proposes a novel Machine Learning (ML) assisted evolutionary algorithm to tackle analog sizing problem. We address the data scarcity issue by introducing a data augmentation method that facilitates and improves the modeling of design metrics via Artificial Neural Networks (ANN). Further, we borrow techniques developed for evolutionary algorithms and introduce a parameter-free ranking methodology to differentiate design performance without human input. We assess the performance of our approach on several academic circuits and show that ML-based modeling significantly improves the simulation cost of the optimization algorithm. Second, in this dissertation, we study applying Reinforcement Learning~(RL) to solve analog sizing problem. We are influenced by the state-of-the-art policy gradient methods and tailor them to solve analog sizing task. Further, we include a recipe to extend this method for solving industrial-scale circuits with thousands of devices. We demonstrate the performance of our approach both on academic circuits and industrial circuits. We observe a significant performance improvement compared to several conventional baseline algorithms and compared to existing commercial tools. Then we visit the AMS tasks with varying simulations costs. Motivated by the fact that one typically needs to run multiple types of simulations, we leverage cheap-to-run simulations to make intermediate decisions on the potential quality of explored points. Then we refrain from expensive-to-run simulations if necessary. In addition, we introduce an asynchronously parallel framework and adapt our previous work for the case of designs with the differentiated cost of simulations. Our benchmarking shows that the proposed methods significantly reduce the total real-time optimization cost and the total CPU effort. Finally, this dissertation includes a solution on how to solve the sizing problem under layout effects effectively. We conduct a study to quantify the impacts of considering layout during transistor sizing. Then, we apply a Bayesian Neural Network~(BNN) based approach to solve the sizing problem. To include layout-induced parasitics, we extend our approach via Multi-Fidelity BNN, where the algorithm utilizes multiple information sources for efficient learning of post-layout performances. We also include a search-space exploration strategy using the trust-region approach, which is shown to be effective on problems with high number of input dimensions. Our tests suggest that the BNN-based sizing algorithm is very competitive compared to previous state-of-the-art algorithms. We further demonstrate that the co-learning strategy of Multi-Fidelity BNN further improves the efficiency, which is very crucial considering the costly post-layout simulations.Electrical and Computer Engineerin