{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Custom Algorithm and Problem\n", "In this notebook, we will show how to use the [`Algorithm`](#evox.core.components.Algorithm) and [`Problem`](#evox.core.components.Problem) to create a custom algorithm and problem. Here we will give an example of **implementing a PSO algorithm that solves the Sphere problem**.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import torch\n", "\n", "from evox.core import Algorithm, Mutable, Parameter, Problem\n", "from evox.utils import clamp\n", "from evox.workflows import EvalMonitor, StdWorkflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm example: PSO algorithm\n", "\n", "Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm inspired by the social behavior of birds and fish. It is widely used for solving continuous and discrete optimization problems.\n", "\n", "**Here is an implementation example of PSO algorithm in EvoX:**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def min_by(\n", " values,\n", " keys,\n", "):\n", " \"\"\"A helper function to find the minimum value in a list of values.\"\"\"\n", " values = torch.cat(values, dim=0)\n", " keys = torch.cat(keys, dim=0)\n", " min_index = torch.argmin(keys)\n", " return values[min_index[None]][0], keys[min_index[None]][0]\n", "\n", "\n", "class PSO(Algorithm):\n", " def __init__(\n", " self,\n", " pop_size: int,\n", " lb: torch.Tensor,\n", " ub: torch.Tensor,\n", " w: float = 0.6,\n", " phi_p: float = 2.5,\n", " phi_g: float = 0.8,\n", " device: torch.device | None = None,\n", " ):\n", " super().__init__()\n", " device = torch.get_default_device() if device is None else device\n", " assert lb.shape == ub.shape and lb.ndim == 1 and ub.ndim == 1 and lb.dtype == ub.dtype\n", " self.pop_size = pop_size\n", " self.dim = lb.shape[0]\n", " # Here, Parameter is used to indicate that these values are hyper-parameters\n", " # so that they can be correctly traced and vector-mapped\n", " self.w = Parameter(w, device=device)\n", " self.phi_p = Parameter(phi_p, device=device)\n", " self.phi_g = Parameter(phi_g, device=device)\n", " # setup\n", " lb = lb[None, :].to(device=device)\n", " ub = ub[None, :].to(device=device)\n", " length = ub - lb\n", " pop = torch.rand(self.pop_size, self.dim, device=device)\n", " pop = length * pop + lb\n", " velocity = torch.rand(self.pop_size, self.dim, device=device)\n", " velocity = 2 * length * velocity - length\n", " # write to self\n", " self.lb = lb\n", " self.ub = ub\n", " # mutable\n", " self.pop = Mutable(pop)\n", " self.velocity = Mutable(velocity)\n", " self.fit = Mutable(torch.full((self.pop_size,), torch.inf, device=device))\n", " self.local_best_location = Mutable(pop)\n", " self.local_best_fit = Mutable(torch.full((self.pop_size,), torch.inf, device=device))\n", " self.global_best_location = Mutable(pop[0])\n", " self.global_best_fit = Mutable(torch.tensor(torch.inf, device=device))\n", "\n", " def step(self):\n", " compare = self.local_best_fit > self.fit\n", " self.local_best_location = torch.where(compare[:, None], self.pop, self.local_best_location)\n", " self.local_best_fit = torch.where(compare, self.fit, self.local_best_fit)\n", " self.global_best_location, self.global_best_fit = min_by(\n", " [self.global_best_location.unsqueeze(0), self.pop],\n", " [self.global_best_fit.unsqueeze(0), self.fit],\n", " )\n", " rg = torch.rand(self.pop_size, self.dim, device=self.fit.device)\n", " rp = torch.rand(self.pop_size, self.dim, device=self.fit.device)\n", " velocity = (\n", " self.w * self.velocity\n", " + self.phi_p * rp * (self.local_best_location - self.pop)\n", " + self.phi_g * rg * (self.global_best_location - self.pop)\n", " )\n", " pop = self.pop + velocity\n", " self.pop = clamp(pop, self.lb, self.ub)\n", " self.velocity = clamp(velocity, self.lb, self.ub)\n", " self.fit = self.evaluate(self.pop)\n", "\n", " def init_step(self):\n", " \"\"\"Perform the first step of the PSO optimization.\n", "\n", " See `step` for more details.\n", " \"\"\"\n", " self.fit = self.evaluate(self.pop)\n", " self.local_best_fit = self.fit\n", " self.global_best_fit = torch.min(self.fit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem example: Sphere problem\n", "\n", "The Sphere problem is a simple, yet fundamental benchmark optimization problem used to test optimization algorithms.\n", "\n", "The Sphere function is defined as:\n", "\n", "$$\n", "\\min f(x)= \\sum_{i=1}^{n} x_{i}^{2}\n", "$$\n", "**Here is an implementation example of Sphere problem in EvoX:**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class Sphere(Problem):\n", " def __init__(self):\n", " super().__init__()\n", "\n", " def evaluate(self, pop: torch.Tensor):\n", " return (pop**2).sum(-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use the algorithm to solve the problem\n", "\n", "### Initiate the algorithm, problem and monitor" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "algorithm = PSO(\n", " pop_size=100,\n", " lb=torch.tensor([-10.0]),\n", " ub=torch.tensor([10.0]),\n", " w=0.6,\n", " phi_p=2.5,\n", " phi_g=0.8,\n", ")\n", "problem = Sphere()\n", "monitor = EvalMonitor()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initiate the workflow and run it" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "workflow = StdWorkflow(algorithm=algorithm, problem=problem, monitor=monitor)\n", "\n", "for _ in range(100):\n", " workflow.step()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, 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