guofei9987/scikit-opt
Scikit Opt
scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions.
The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem.
The framework supports the definition of linear and nonlinear constraints to ensure solution validity. It features capabilities for state-persistent iteration to resume optimization processes from previous checkpoints and utilizes vectorized numerical computation, multithreading, and multiprocessing to accelerate execution.
Features
- Global Optimization Frameworks - Provides a comprehensive framework for solving global optimization problems using a variety of heuristic and swarm algorithms.
- Global Search Heuristics - Provides stochastic search and swarm intelligence algorithms for global numerical optimization of complex functions.
- Genetic Algorithms - Implements genetic algorithms using selection, crossover, and mutation to find optimal solutions for objective functions.
- Nature-Inspired Algorithms - Implements optimization algorithms based on biological processes such as genetic evolution and fish swarming.
- Population-Based Optimization - Maintains a population of candidate solutions that evolve through the search space over multiple generations.
- Linear Space Refinement - Implements search space restrictions using linear and nonlinear equations to ensure mathematical validity of candidate solutions.
- Global Optimization Analysis - Finds absolute maxima or minima for complex mathematical functions across high-dimensional spaces.
- Mathematical Optimization Solving - Implements the numerical solution of linear, quadratic, and nonlinear programs to optimize mathematical functions.
- Metaheuristic Optimization - Offers a suite of algorithms inspired by natural phenomena, including PSO and Genetic Algorithms, for complex search problems.
- Simulated Annealing - Provides a probabilistic cooling process via simulated annealing to escape local optima in global search spaces.
- Optimization Constraint Enforcement - Enforces mathematical feasibility in the search space using linear and nonlinear equalities and inequalities.
- Constrained Parameter Optimization - Searches for optimal variable sets while ensuring all candidates stay within specified linear or nonlinear limits.
- Swarm Optimization Algorithms - Implements particle swarm optimization to converge on global optima using population-based social particle behavior.
- Immune Algorithms - Provides optimization capabilities based on biological immune system adaptation to solve complex tasks.
- Optimizer State Persistence - Allows resuming optimization processes from previous checkpoints by saving the current population and parameter states.
- Combinatorial Problem Solving - Solves discrete optimization challenges such as the Traveling Salesman Problem to find efficient routes.
- Ant Colony Optimization - Simulates pheromone-laying behavior to find optimal paths in complex combinatorial problems.
- Differential Evolution - Implements a vector-based mutation process to evolve candidate solutions for optimizing mathematical problems.
- Numerical Array Operations - Uses array-based operations to process candidate populations, reducing loop overhead and increasing execution speed.
- Numerical Computation Accelerations - Utilizes vectorized numerical computation, multithreading, and multiprocessing to accelerate the execution of optimization algorithms.
- Combinatorial Optimization Problems - Provides a framework for solving discrete optimization tasks like the Traveling Salesman Problem.
- Traveling Salesman Problem Solvers - Includes specialized solvers to determine the shortest route for the traveling salesman problem.
- Algorithm Customization Frameworks - Provides a component-based architecture for swapping internal logic and selection rules within optimization algorithms.
- Algorithm Extension Interfaces - Provides mechanisms for replacing internal algorithm components with custom logic to modify data processing or selection.
- Artificial Fish Swarm Optimization - Simulates fish foraging and grouping behaviors to locate optimal points within a search space.
- Computer Vision - Toolkit for swarm intelligence and genetic algorithm optimization.
- General Machine Learning - Genetic algorithms and optimization for scikit-learn.
- Framework-uri de Machine Learning - Genetic algorithms and swarm intelligence optimization.
- Machine Learning Packages - Genetic algorithms and particle swarm optimization.
- Optimization - Heuristic algorithms for various optimization tasks.
