Valdecy Pereira's repositories
pyMetaheuristic
pyMetaheuristic: A Comprehensive Python Library for Optimization
pyDecision
pyDecision is a comprehensive Python library that encompasses a wide array of Multi-Criteria Decision Analysis (MCDA) methods. These powerful and versatile tools assist in making effective decisions by comparing alternatives based on multiple criteria, making it a valuable resource for researchers, analysts, and decision-makers.
pyMultiobjective
A python library for the following Multiobjective Optimization Algorithms or Many Objectives Optimization Algorithms: C-NSGA II; CTAEA; GrEA; HypE; IBEA-FC; IBEA-HV; MOEA/D; NAEMO; NSGA II; NSGA III; OMOPSO; PAES; RVEA; SMPSO; SMS-EMOA; SPEA2; U-NSGA III
pyCombinatorial
A library to solve the TSP (Travelling Salesman Problem) using Exact Algorithms, Heuristics and Metaheuristics : 2-opt; 2.5-opt; 3-opt; 4-opt; 5-opt; 2-opt Stochastic; 2.5-opt Stochastic; 3-opt Stochastic; 4-opt Stochastic; 5-opt Stochastic; Ant Colony Optimization; Bellman-Held-Karp Exact Algorithm; Branch & Bound; BRKGA (Biased Random Key Genetic Algorithm); Brute Force; Cheapest Insertion; Christofides Algorithm; Clarke & Wright (Savings Heuristic); Concave Hull Algorithm; Convex Hull Algorithm; Elastic Net; Extremal Optimization; Farthest Insertion; Genetic Algorithm; GRASP (Greedy Randomized Adaptive Search Procedure); Greedy Karp-Steele Patching; Guided Search; Hopfield Network; Iterated Search; Karp-Steele Patching; Multifragment Heuristic; Nearest Insertion; Nearest Neighbour; Random Insertion; Random Tour; Scatter Search; Simulated Annealing; SOM (Self Organizing Maps); Space Filling Curve (Hilbert); Space Filling Curve (Morton); Space Filling Curve (Sierpinski); Stochastic Hill Climbing; Sweep; Tabu Search; Truncated Branch & Bound; Twice-Around the Tree Algorithm (Double Tree Algorithm); Variable Neighborhood Search.
pyInterDemand
A python Library for Intermittent Demand Methods: Croston, SBA, SBJ, TSB, HES, LES and SES
Metaheuristic-Local_Search-Variable_Neighborhood_Search
Variable Neighborhood Search Function for TSP problems
J-Horizon
(Update-15-MAY-2020) A Vehicle Routing Problem Software. CVRP (Capacitated VRP), MDVRP (Multiple Depot VRP), VRPTW (VRP with Time Windows), VRPB (VRP with Backhauls), VRPPD (VRP with Pickups and Deliveries), VRP with Homogeneous or Heterogeneous Fleet, Finite or Infinite Fleet, TSP, mTSP and various combination of these types
Metaheuristic-Local_Search-Tabu_Search
Tabu Search Function for TSP problems
Recommender-Systems-Content_Based_Filtering
Content-Based Filtering using TF-IDF Matrices with Cosine Similarity
Metaheuristic-Local_Search-Scatter_Search
Scatter Search Function for TSP problems
Metaheuristic-Local_Search-Extremal_Optimization
Extremal Optimization Function for TSP problems
Metaheuristic-Local_Search-Iterated_Search
Iterated Search Function for TSP problems
mcdm_scheduler
A MCDM approach for Scheduling Problems
pyAutoSummarizer
pyAutoSummarizer - An Extractive and Abstractive Summarization Library Powered with Artificial Intelligence
Neural-Networks
Pure Python Neural Network Function for Binary or Linear Problems
ga_scheduler
A Comprehensive Library for Solving Machine Scheduling Problems Using Genetic Algorithms
Metaheuristic-NSGA_II
NSGA II (Non-Dominated Sorting Genetic Algorithm II) Function to Minimize Multiple Objectives with Continuous Variables. Real Values Encoded
Method_3MOAHP
Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods
pyCritical
CPM & PERT methods with Gantt chart plots
pyMissingAHP
A Method to Infer AHP Missing Pairwise Comparisons
Recommender-Systems-Collaborative_Filtering-Regression_User_Based
Collaborative Filtering Function using an User Based Regression Approach
ec_promethee
The EC-PROMETHEE Method - A Committee Approach for Outranking Problems Using Randoms Weights
Recommender-Systems-Collaborative_Filtering-Nearest_Neighbors
Collaborative Filtering Function using a Nearest Neighbors Approach
Recommender-Systems-Collaborative_Filtering-Regression_Item_Based
Collaborative Filtering Function using an Item Based Regression Approach
Recommender-Systems-Collaborative_Filtering-Regression_Latent_Factors
Collaborative Filtering Function using Regression with Latent Factors Approach
Recommender-Systems-Collaborative_Filtering-Regression_User_Item_Based
Collaborative Filtering Function using an User-Item Based Regression Approach
Recommender-Systems-Collaborative_Filtering-SVD
Collaborative Filtering Function using a SVD (Singular Value Decomposition) Approach.