18 skills found
mahdims / 3D Bin PackingTabu search and Genetic algorithm implementation for container loading problem (3D bin packing)
Nivedha-Ramesh / Container Loading Problemhybrid genetic algorithm for container loading problem
nadavbra / Ukbb ParserA Python module for loading phenotypic and genetic data from the UK Biobank.
ZhaoKe1024 / IntelligentAlgorithmSchedulerSome Algorithm such as GA（Genetic Algorithm）, PSO(Particle Swarm Algorithm), ASO(Ant Swarm Algorithm), SA(Simulated Annealing Algorithm) and so on. Mainly used as Scheduling Problem to JSP, FJSP and Load Balance.
nishantdhol / Genetic Based Load Balancing Cloud ComputingThe results of the simulation show that the Genetic algorithm performs well compared to First Come First Serve Algorithm, Round Robin Algorithm, and Shortest Job First algorithm.
tahanakabi / Optimal Price Based Control Of Heterogeneous Thermostatically Controlled Loads Under Uncertainty Usiwe consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.
marco-milanesi / PID PIDA GAtuningImplementation of a genetic algorithm to determine the parameters of the PID, PI-D, I-PD and PIDA controllers in order to compensate various benchmark processes, which are representative of many industrial applications. In particular, by considering separately a set-point and a load disturbance rejection unit step response the IAE is minimized by constraining the maximum sensitivity.
masoudshab / OPTIMIZATION Of The APFs Placement Based On Instantaneous Reactive Power Theory By GENETIC ALGORITHMIn electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.
XxNILOYxX / NomadA Genetic Algorithm (GA) / Discrete Particle Swarm Optimization/ Hybrid (GA-PSO) for nuclear fuel optimization using ML surrogates (DNN, KNN, Random Forest, Ridge) and OpenMC. Optimizes fuel loading patterns for a target k-eff and minimal Power Peaking Factor (PPF).
axeltidemann / Load ForecastingA framework for load forecasting, where seasonal cycles are removed from the input signal. A genetic algorithm is used to parameterize various forecasting models.
viniciusfinger / Genetic CargoOptimize your truck loading process with a genetic algorithm designed to select the optimal combination between items and values. 🧬 (⚠️ Being refactored).
jjac13 / GA HEMS RealTimeThis library contains a set of functions to model of a multi-carrier network of a household (space heating and electric loads), and a multi-objective genetic algorithm home energy management system (HEMS), considering: a PV, solar collectors, thermal storage, a HP and a battery storage system. Created by: Joel Alpízar-Castillo.
BeckySTaylor / GERPA modified Genomic Evolutionary Rate Profiling (GERP) pipeline for the analysis of genetic load in wildlife species
emitruc / Genoloadergenoloader: derived allele polarization and genetic load estimates with high and low coverage data
201614170107 / DSM Campus InfrastructureDemand Side Management for a Campus Infrastructure - Incorporating Load Shifting DSM Strategy using Genetic Alogithm
NeelBansal22 / SBRPThis GitHub repository contains the source code for a comprehensive study on the SBRP, addressing the School-Bus Routing Problem with Single Load Plan (SBRP-SLP). The project explores the effectiveness of various graph theory algorithms, including Genetic Algorithm, Simulated Annealing, Nearest Neighbor, and Clarke-Wright Savings Algorithm
rknair92 / ELD GAEconomic Load Dispatch using Genetic Algorithm
dingjing1998 / Genetic AlgorithmWith hybrid genetic algorithm to solve the logistics distribution path: from a logistics center with more than one delivery vehicle delivery to multiple customers, each customer's location and the demand for certain goods, each distribution vehicle load must be, the maximum range of the primary distribution is certain, for reasonable arrangement of the vehicle distribution route, to optimize the objective function, and satisfy the following conditions: (1) each distribution path each customer's demand is less than the sum of the distribution of vehicle load; (2) each of the distribution path length is not more than the biggest distance delivery vehicles a distribution; (3) must satisfy each customer's demand, and only by a distribution vehicle delivery. Distribution total mileage to be shortest as objective function
