{"id":22279773,"url":"https://github.com/volpatto/pyde","last_synced_at":"2025-06-23T14:34:17.974Z","repository":{"id":79266814,"uuid":"147149060","full_name":"volpatto/pyde","owner":"volpatto","description":"An implementation of Differential Evolution procedure for unconstrained optimization","archived":false,"fork":false,"pushed_at":"2018-09-30T18:52:18.000Z","size":5439,"stargazers_count":1,"open_issues_count":10,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-25T18:45:30.796Z","etag":null,"topics":["metaheuristics","numerical-optimization","optimization-algorithms","optimization-methods"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/volpatto.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-09-03T03:54:04.000Z","updated_at":"2018-09-30T18:52:19.000Z","dependencies_parsed_at":"2023-03-07T10:30:35.998Z","dependency_job_id":null,"html_url":"https://github.com/volpatto/pyde","commit_stats":{"total_commits":42,"total_committers":4,"mean_commits":10.5,"dds":0.09523809523809523,"last_synced_commit":"b09f8a62afbea03a0288744b55b9a05a2023d018"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/volpatto/pyde","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/volpatto%2Fpyde","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/volpatto%2Fpyde/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/volpatto%2Fpyde/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/volpatto%2Fpyde/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/volpatto","download_url":"https://codeload.github.com/volpatto/pyde/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/volpatto%2Fpyde/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261494836,"owners_count":23167212,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["metaheuristics","numerical-optimization","optimization-algorithms","optimization-methods"],"created_at":"2024-12-03T15:21:02.495Z","updated_at":"2025-06-23T14:34:17.942Z","avatar_url":"https://github.com/volpatto.png","language":"Python","readme":"# PyDE: A Python implementation of Differential Evolution procedure for unconstrained optimization\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Documentation Status](https://readthedocs.org/projects/pyde/badge/?version=master)](https://pyde.readthedocs.io/en/master/?badge=master)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/b2498b2edece40bf96aac44b94a90092)](https://app.codacy.com/app/volpatto/pyde?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=volpatto/pyde\u0026utm_campaign=Badge_Grade_Settings)\n[![Build Status](https://travis-ci.com/volpatto/pyde.svg?branch=master)](https://travis-ci.com/volpatto/pyde)\n[![DOI](https://zenodo.org/badge/147149060.svg)](https://zenodo.org/badge/latestdoi/147149060)\n\n\nHere I provide a simple Python 3.6 implementation of Differential Evolution procedure to solve unconstrained optimization problems.\nAlthough SciPy already provides this method, an open code, flexible to customization, can benefits students, researchers or anyone who desires to implement the DE method. Aiming to fulfill this gap, I provide the present code.\n\nIn development, I try to keep an implementation as good as possible accordling to best pratices in Python. To account such project quality requirement, I strongly recommend read [\"The Hitchhiker’s Guide to Python\"](https://docs.python-guide.org/), section \"Writing Great Python Code\".\n\nFeel free to contribute or use in the sense of MIT License.\n\n# Contact\n\nOne can contact me through the email \u003cvolpatto@lncc.br\u003e.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvolpatto%2Fpyde","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvolpatto%2Fpyde","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvolpatto%2Fpyde/lists"}