[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HAS1WJZVBK2FNXN7YM4ZH8":3,"skill-same:01M6HAS1WJZVBK2FNXN7YM4ZH8":57},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":14,"repositoryHtmlUrl":38,"repositoryHomepageUrl":6,"ownerHtmlUrl":39,"ownerBlogUrl":40,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M6HAS1WJZVBK2FNXN7YM4ZH8","swarm-intelligence",null,"Swarm intelligence algorithms","NeuralBlitz\u002FMito","NeuralBlitz","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F79354388?v=4","01M6H8T2TFH477HRS0KTSTKR2E","David Riggs","01M6H8T2YQQDXPYM092P670Z21",0,"MIT","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fswarm-intelligence\u002FSKILL.md",[],[19,20,21,22,23,24,25,26],"swarm","intelligence","swarm intelligence","machine-learning","learning","opencode","algorithms","machine","2026-11-10 10:12:30",false,"---\nname: swarm-intelligence\ndescription: Swarm intelligence algorithms\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: machine-learning-engineers\n  category: artificial-intelligence\n---\n\n## What I do\n\n- Implement swarm optimization\n- Design particle swarm systems\n- Build ant colony algorithms\n- Create collective intelligence\n- Optimize using swarm behavior\n\n## When to use me\n\nUse me when:\n- Distributed optimization\n- Routing problems\n- Collective robotics\n- Emergent behavior systems\n\n## Key Concepts\n\n### Particle Swarm Optimization (PSO)\n```python\nimport numpy as np\n\nclass ParticleSwarm:\n    def __init__(self, n_particles, n_dims, func):\n        self.n_particles = n_particles\n        self.func = func\n        \n        # Initialize particles\n        self.positions = np.random.uniform(-10, 10, (n_particles, n_dims))\n        self.velocities = np.random.uniform(-1, 1, (n_particles, n_dims))\n        \n        # Personal best\n        self.personal_best_pos = self.positions.copy()\n        self.personal_best_val = np.array([self.func(p) for p in self.positions])\n        \n        # Global best\n        best_idx = np.argmin(self.personal_best_val)\n        self.global_best_pos = self.personal_best_pos[best_idx].copy()\n        self.global_best_val = self.personal_best_val[best_idx]\n    \n    def update(self, w=0.7, c1=1.5, c2=1.5):\n        r1, r2 = np.random.random((2, self.n_particles, 1))\n        \n        # Update velocities\n        cognitive = c1 * r1 * (self.personal_best_pos - self.positions)\n        social = c2 * r2 * (self.global_best_pos - self.positions)\n        self.velocities = w * self.velocities + cognitive + social\n        \n        # Update positions\n        self.positions += self.velocities\n        \n        # Evaluate and update personal bests\n        current_vals = np.array([self.func(p) for p in self.positions])\n        improved = current_vals \u003C self.personal_best_val\n        self.personal_best_pos[improved] = self.positions[improved]\n        self.personal_best_val[improved] = current_vals[improved]\n        \n        # Update global best\n        best_idx = np.argmin(self.personal_best_val)\n        if self.personal_best_val[best_idx] \u003C self.global_best_val:\n            self.global_best_pos = self.personal_best_pos[best_idx].copy()\n            self.global_best_val = self.personal_best_val[best_idx]\n    \n    def optimize(self, n_iterations):\n        for _ in range(n_iterations):\n            self.update()\n        return self.global_best_pos, self.global_best_val\n```\n\n### Ant Colony Optimization\n- Pheromone-based path finding\n- Probabilistic solution construction\n- Global and local pheromone updates\n- Used for: TSP, VRP, routing\n\n### Swarm Applications\n- **ACO**: Routing, scheduling\n- **PSO**: Function optimization\n- **Artificial Bee Colony**: Optimization\n- **Firefly Algorithm**: Clustering\n","## What I do\n\n- Implement swarm optimization\n- Design particle swarm systems\n- Build ant colony algorithms\n- Create collective intelligence\n- Optimize using swarm behavior\n\n## When to use me\n\nUse me when:\n- Distributed optimization\n- Routing problems\n- Collective robotics\n- Emergent behavior systems\n\n## Key Concepts\n\n### Particle Swarm Optimization (PSO)\n```python\nimport numpy as np\n\nclass ParticleSwarm:\n    def __init__(self, n_particles, n_dims, func):\n        self.n_particles = n_particles\n        self.func = func\n        \n        # Initialize particles\n        self.positions = np.random.uniform(-10, 10, (n_particles, n_dims))\n        self.velocities = np.random.uniform(-1, 1, (n_particles, n_dims))\n        \n        # Personal best\n        self.personal_best_pos = self.positions.copy()\n        self.personal_best_val = np.array([self.func(p) for p in self.positions])\n        \n        # Global best\n        best_idx = np.argmin(self.personal_best_val)\n        self.global_best_pos = self.personal_best_pos[best_idx].copy()\n        self.global_best_val = self.personal_best_val[best_idx]\n    \n    def update(self, w=0.7, c1=1.5, c2=1.5):\n        r1, r2 = np.random.random((2, self.n_particles, 1))\n        \n        # Update velocities\n        cognitive = c1 * r1 * (self.personal_best_pos - self.positions)\n        social = c2 * r2 * (self.global_best_pos - self.positions)\n        self.velocities = w * self.velocities + cognitive + social\n        \n        # Update positions\n        self.positions += self.velocities\n        \n        # Evaluate and update personal bests\n        current_vals = np.array([self.func(p) for p in self.positions])\n        improved = current_vals \u003C self.personal_best_val\n        self.personal_best_pos[improved] = self.positions[improved]\n        self.personal_best_val[improved] = current_vals[improved]\n        \n        # Update global best\n        best_idx = np.argmin(self.personal_best_val)\n        if self.personal_best_val[best_idx] \u003C self.global_best_val:\n            self.global_best_pos = self.personal_best_pos[best_idx].copy()\n            self.global_best_val = self.personal_best_val[best_idx]\n    \n    def optimize(self, n_iterations):\n        for _ in range(n_iterations):\n            self.update()\n        return self.global_best_pos, self.global_best_val\n```\n\n### Ant Colony Optimization\n- Pheromone-based path finding\n- Probabilistic solution construction\n- Global and local pheromone updates\n- Used for: TSP, VRP, routing\n\n### Swarm Applications\n- **ACO**: Routing, scheduling\n- **PSO**: Function optimization\n- **Artificial Bee Colony**: Optimization\n- **Firefly Algorithm**: Clustering\n",{"name":5,"license":15,"metadata":32,"description":7,"compatibility":24},{"audience":33,"category":34},"machine-learning-engineers","artificial-intelligence","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Fswarm-intelligence","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Independent Multidisciplinary Researcher, Creative Producer, and AI Systems Architect\r\nAn independent creator and research-driven systems designer.","2026-11-12 03:13:05",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add NeuralBlitz\u002FMito --skill swarm-intelligence",{"tool":49,"command":50},"bunx","bunx skills add NeuralBlitz\u002FMito --skill swarm-intelligence",{"tool":52,"command":53},"pnpm","pnpm dlx skills add 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