{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/4"}],"enrichment":{"capability":"PySwarms implements particle swarm optimization (PSO) algorithms in Python, providing a high-level interface for solving optimization problems using swarm intelligence techniques.","skillfed_tags":["metaheuristic-optimization","research-toolkit","swarm-intelligence"],"use_cases":["Solve unconstrained continuous optimization problems where gradient-free metaheuristic search is preferred over calculus-based methods.","Tune hyperparameters of a PSO optimizer using built-in grid or random search to find the best configuration for your objective function.","Visualize swarm behavior and cost convergence over iterations using the plotting module to understand optimizer performance and debug tuning choices.","Implement custom PSO variants or hybrid algorithms by extending the toolkit's base classes for research publications or specialized applications.","Benchmark PSO against other metaheuristics on standard test functions provided in the utils module."],"what_it_does":"PySwarms is a research toolkit for particle swarm optimization (PSO) that provides ready-to-use implementations of PSO algorithms alongside utilities for hyperparameter tuning, visualization, and testing. It wraps scipy, numpy, and matplotlib to deliver a declarative interface where you define an objective function, configure swarm parameters, and run the optimizer to find minima or maxima across your problem space.\n\nThe package targets researchers, students, and practitioners who want to apply PSO without building the algorithm from scratch. It includes built-in test functions, grid and random search tools for hyperparameter optimization, and plotting utilities to visualize cost histories and particle trajectories. The API is extensible, allowing researchers to implement custom PSO variants. Dependencies are standard scientific Python libraries (scipy, numpy, matplotlib, attrs, tqdm, pyyaml, future), making it straightforward to integrate into existing workflows.","worth_installing":"Yes, if you are doing research, education, or prototyping with particle swarm optimization. The low install friction, permissive MIT license, and extensible design make it a solid choice for PSO work. However, be aware that the package is dormant\u2014the latest release is from 2021-01-03, so compatibility with very recent Python or dependency versions is uncertain. Not recommended for production systems requiring active maintenance and support."},"id":"pyswarms","links":{"html":"https://skillfed.io/packages/pyswarms","md":"https://skillfed.io/packages/pyswarms.md","pypi":"https://pypi.org/project/pyswarms/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2021-01-03","license_spdx":null,"license_treatment":"permissive","name":"pyswarms","python_support":"unspecified","summary":"A Python-based Particle Swarm Optimization (PSO) library."},"popularity":{"monthly_downloads":74825,"position":14788,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}