11 skills found
alimirjalili / GWOThe GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optimization. This is the source codes of the paper: S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, Volume 69, March 2014, Pages 46-61, ISSN 0965-9978, http://dx.doi.org/10.1016/j.advengsoft.2013.12.007. More information can be found in: http://www.alimirjalili.com/GWO.html
qingfengxia / CAE Pipelinedemonstration of automated engineering design and optimisation process uisng open source tools and Python
Computer-Aided-Validation-Laboratory / PyvaleYour virtual engineering laboratory: An all-in-one package for sensor simulation, uncertainty quantification, sensor placement optimisation and simulation calibration/validation.
appliedcomputingtech / Pro GymProcess Simulations Meet AI. Supercharge Your Process Engineering. Generate Infinite Data, Train Advanced Models, and Revolutionise Industrial Optimisation with One Toolkit. From Flowsheet to AI-Powered Plant in Record Time 🚀
ali-ece / Design Of Optimal CMOS Ring Oscillator Using An Intelligent Optimization ToolThis paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.
pngts / Nonlinear Parameter Estimation In Thermodynamic ModelsThe reliable solution of nonlinear parameter estimation problems is an essential computational and mathematical problem in process systems engineering, both in on-line and off-line applications. Parameter estimation in semi-empirical models for vapor – liquid equilibrium (VLE) data modelling plays an important role in design, optimization and control of separation units. Conventional optimisation methods may not be reliable since they do not guarantee convergence to the global optimum sought in the parameter estimation problem. In this work we demonstrate a technique, based on genetic algorithms (GA), that can solve the nonlinear parameter estimation problem with complete reliability, providing a high probability that the global optimum is found. Two versions of stochastic optimization techniques are evaluated and compared for nine vapour - liquid equilibrium problems: our genetic base algorithm and a hybrid algorithm. Reliable experimental data from the literature on vapor - liquid equilibrium systems were correlated using the UNIQUAC equation for activity coefficients. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. Considering that new globally optimal parameter values are found by using the proposed method we can surmise by our results that several sets of parameter values published in the DECHEMA VLE Data Collection correspond to local instead of global minima.
philikai / FromPromptEngineeringToAutoPromptOptimisationRepository for Automatic Prompt Engineering Resources
professorcode1 / College Time Table Schedulerhttps://portfolio.raghavkumar.co.in/collegeschduler Creates a college schedule using ant-colony optimisation to perform graph colouring. This was my sem 4 Software Engineering project
harshabose / Genetic OptimisationA versatile genetic optimisation C++ header file for engineering optimisation.
LyCrash / GPT NLP PlaygroundThis is a collection of work-study done during 2 months internship at Ornidex in the topic of CRM optimisation using Fine-tuning/Prompt-engineering/Embedding techniques
ressay / ArchToCEAn optimisation program to go from architecture plan to basic civil engineering plan
