{ "_id": "6a1ed721b401979e7340ecd2", "Package": "globpso", "Type": "Package", "Title": "Swarm Intelligence Optimization", "Version": "1.3.0", "Date": "2025-03-08", "Authors@R": "c(\nperson(\"Ping-Yang\", \"Chen\", email = \"pychen.ping@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Russell\", \"Eberhart\", email = \"\", role = c(\"ctb\")),\nperson(\"Kennedy\", \"Kennedy\", email = \"\", role = c(\"ctb\")),\nperson(\"Jun\", \"Sun\", email = \"\", role = c(\"ctb\")),\nperson(\"Bin\", \"Feng\", email = \"\", role = c(\"ctb\")),\nperson(\"Wenbo\", \"Xu\", email = \"\", role = c(\"ctb\")),\nperson(\"Mohammad Reza\", \"Bonyadi\", email = \"\", role = c(\"ctb\")),\nperson(\"Zbigniew\", \"Michalewicz\", email = \"\", role = c(\"ctb\")),\nperson(\"Ran\", \"Cheng\", email = \"\", role = c(\"ctb\")),\nperson(\"Yaochu\", \"Jin\", email = \"\", role = c(\"ctb\")),\nperson(\"Milan\", \"Stehlík\", email = \"\", role = c(\"ctb\")),\nperson(\"Weng Kee\", \"Wong\", email = \"\", role = c(\"ctb\")),\nperson(\"Jozef\", \"Kiseľák\", email = \"\", role = c(\"ctb\")),\nperson(\"Rainer\", \"Storn\", email = \"\", role = c(\"ctb\")),\nperson(\"Kenneth\", \"Price\", email = \"\", role = c(\"ctb\")),\nperson(\"Okazaki\", \"Naoaki\", email = \"\", role = c(\"cph\")))", "Author": "Ping-Yang Chen [aut, cre], Russell Eberhart [ctb], Kennedy\nKennedy [ctb], Jun Sun [ctb], Bin Feng [ctb], Wenbo Xu [ctb],\nMohammad Reza Bonyadi [ctb], Zbigniew Michalewicz [ctb], Ran\nCheng [ctb], Yaochu Jin [ctb], Milan Stehlík [ctb], Weng Kee\nWong [ctb], Jozef Kiseľák [ctb], Rainer Storn [ctb], Kenneth\nPrice [ctb], Okazaki Naoaki [cph]", "Maintainer": "Ping-Yang Chen ", "Description": "A fast and flexible general-purpose implementation of\nParticle Swarm Optimization (PSO) and Differential Evolution\n(DE) for solving global minimization problems is provided. It\nis designed to handle complex optimization tasks with\nnonlinear, non-differentiable, and multi-modal objective\nfunctions defined by users. There are five types of PSO\nvariants: Particle Swarm Optimization (PSO, Eberhart & Kennedy,\n1995) , Quantum-behaved particle\nSwarm Optimization (QPSO, Sun et al., 2004)\n, Locally convergent rotationally\ninvariant particle swarm optimization (LcRiPSO, Bonyadi &\nMichalewicz, 2014) , Competitive\nSwarm Optimizer (CSO, Cheng & Jin, 2015)\n and Double exponential particle\nswarm optimization (DExPSO, Stehlik et al., 2024)\n. For the DE algorithm, six\ntypes in Storn, R. & Price, K. (1997)\n are included: DE/rand/1,\nDE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and\nDE/rand_to-best/2.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Repository": "https://pingyangchen.r-universe.dev", "Date/Publication": "2025-06-27 02:41:04 UTC", "RemoteUrl": "https://github.com/pingyangchen/globpso", "RemoteRef": "HEAD", "RemoteSha": "1cbf472070cb4d4ce60c3b8e85232176ebb6df2c", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-20 10:08:16 UTC", "User": "root" }, "MD5sum": "a72f2a54b2ef6c040e8104d87768fe22", "_user": "pingyangchen", "_type": "src", "_file": "globpso_1.3.0.tar.gz", "_fileid": "bc6b5a859efd13e31707e0c1746a9486ebe146268a4e09ceb103fef141a4bc02", "_filesize": 168414, "_sha256": "bc6b5a859efd13e31707e0c1746a9486ebe146268a4e09ceb103fef141a4bc02", "_created": "2026-05-20T10:08:16.000Z", "_published": "2026-06-02T13:14:09.589Z", "_distro": "noble", "_jobs": [ { "job": 79078787797, "time": 125, "config": "linux-devel-arm64", "r": "4.7.0", 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