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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "raw", | ||
| "metadata": { | ||
| "raw_mimetype": "text/restructuredtext" | ||
| }, | ||
| "source": [ | ||
| ".. _nb_kgb:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "source": [ | ||
| "# KGB-DMOEA: Knowledge-Guided Bayesian Dynamic Multi-Objective Evolutionary Algorithm" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "jp-MarkdownHeadingCollapsed": true, | ||
| "tags": [] | ||
| }, | ||
| "source": [ | ||
| "KGB-DMOEA is a sophisticated evolutionary algorithm for dynamic multi-objective optimization problems (DMOPs). It employs a knowledge-guided Bayesian classification approach to adeptly navigate and adapt to changing Pareto-optimal solutions in dynamic environments. This algorithm utilizes past search experiences, distinguishing them as beneficial or non-beneficial, to effectively direct the search in new scenarios." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Key Features\n", | ||
| "\n", | ||
| "\n", | ||
| "- **Knowledge Reconstruction-Examination (KRE):** Dynamically re-evaluates historical optimal solutions based on their relevance and utility in the current environment. \n", | ||
| "- **Bayesian Classification:** Employs a Naive Bayesian Classifier to forecast high-quality initial populations for new environments.\n", | ||
| "- **Adaptive Strategy:** Incorporates dynamic parameter adjustment for optimized performance across varying dynamic contexts." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| " " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Example" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "ename": "ModuleNotFoundError", | ||
| "evalue": "No module named 'pymoo.algorithms.moo.kgb'", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||
| "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", | ||
| "\u001b[0;32m/var/folders/dp/fdf8szyn3zd4kcyh18gxsyvm0000gn/T/ipykernel_78338/2424210873.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpymoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkgb\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKGB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpymoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCallbackCollection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpymoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mminimize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpymoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdyn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimeSimulation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpymoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdynamic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDF1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||
| "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pymoo.algorithms.moo.kgb'" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from pymoo.algorithms.moo.kgb import KGB\n", | ||
| "from pymoo.core.callback import CallbackCollection\n", | ||
| "from pymoo.optimize import minimize\n", | ||
| "from pymoo.problems.dyn import TimeSimulation\n", | ||
| "from pymoo.problems.dynamic.df import DF1\n", | ||
| "\n", | ||
| "from pymoo.visualization.video.callback_video import ObjectiveSpaceAnimation\n", | ||
| "\n", | ||
| "problem = DF1(taut=2, n_var=2)\n", | ||
| "\n", | ||
| "algorithm = KGB()\n", | ||
| "\n", | ||
| "simulation = TimeSimulation()\n", | ||
| "\n", | ||
| "res = minimize(problem,\n", | ||
| " algorithm,\n", | ||
| " termination=('n_gen', 100),\n", | ||
| " callback=CallbackCollection(ObjectiveSpaceAnimation(), simulation),\n", | ||
| " seed=1,\n", | ||
| " verbose=True)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "source": [ | ||
| "### Parameters \n", | ||
| "\n", | ||
| "- **perc_detect_change (float, optional):** Proportion of the population used to detect environmental changes. \n", | ||
| "- **perc_diversity (float, optional):** Proportion of the population allocated for introducing diversity. \n", | ||
| "- **c_size (int, optional):** Cluster size.\n", | ||
| "- **eps (float, optional):** Threshold for detecting changes. Default: \n", | ||
| "- **pertub_dev (float, optional):** Deviation for perturbation in diversity introduction. " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### References" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "raw", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Yulong Ye, Lingjie Li, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming. “A knowledge guided Bayesian classification for dynamic multi-objective optimization”. Knowledge-Based Systems, Volume 251, 2022. Link to the paper" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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