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所在平台: Udemy |
课程主页: https://www.udemy.com/course/multi-objective-optimization-problems-and-algorithms/
课程评论：没有评论
**课程名称：** 多目标优化问题与算法 **课程概述：** 本课程是一门关于使用人工智能搜索算法解决多目标优化问题的入门课程。课程内容涵盖： * **多目标优化问题的基础：** 详细介绍问题及其数学模型，并深入理解搜索空间、目标空间、帕累托最优性、帕累托最优解集、帕累托最优前沿、帕累托支配、约束、目标函数、局部前沿、局部最优解、真实帕累托最优解、真实帕累托最优前沿等核心概念。 * **多目标优化算法：** 介绍解决多目标优化问题的各类方法，包括无偏好方法、先验方法、后验方法和渐进方法。 * **实践操作：** 提供大量的编码视频，让学习者有机会实践所学理论。 * **案例研究：** 包含真实世界问题的案例分析，展示如何运用多目标优化算法解决复杂的优化问题。 * **搜索方法：** 重点讲解随机优化算法，如粒子群优化 (PSO) 和遗传算法 (GA)，并在此基础上开发多目标粒子群优化 (MOPSO) 和多目标遗传算法 (MOGA)。 **课程亮点：** * 讲师拥有深厚的专业知识，以详实且贴近现实的例子深入浅出地讲解概念。 * 学习过程生动有趣，即使是困难的概念也能变得易于理解。 * 课程结构严谨，内容设计符合学习者的需求，特别适合初学者。 * 已有超过1000名学生加入，开启他们的优化学习之旅。 * 提供30天无理由退款保证。 **适用人群：** 任何对优化问题感兴趣，特别是希望学习多目标优化理论和实践的学习者。 **学习目标：** 通过本课程，学习者将能够理解多目标优化问题的基本原理，掌握多种求解算法，并具备应用这些算法解决实际问题的能力。
This is an introductory course to multi-objective optimization using Artificial Intelligence search algorithms. We start with the details and mathematical models of problems with multiple objectives. Then, we focus on understanding the most fundamental concepts in the field of multi-objective optimization including but not limited to: search space, objective space, Pareto optimality, Pareto optimal solution set, Pareto optimal front, Pareto dominance, constraints, objective function, local fronts, local solutions, true Pareto optimal solutions, true Pareto optimal front, etc. In the second part of this course, several optimization methods will be given to solve multi-objective optimization problems as follows: No preference methods A priori methods A posteriori methods Progressive methods The course also includes a large number of coding videos to give you enough opportunity to practice the theory covered in the lecture. There are also several case studies including real-world problems that allow you to learn the process of solving challenging multi-objective optimization problems using multi-objective optimization algorithms. For the search methods, we will be using stochastic optimization algorithms including Particle Swarm Optimization and Genetic Algorithms. This means that we develop Multi-Objective Particle Swarm Optimization (MOPSO) and multi-Objective Genetic Algorithms (MOGA). Some of the reviews for this course are as follows: Femi said: "As always, the instructor is expert in the course and explained in details with real-life examples, and I love his teaching style, even though the course is a bit tough, he made it fun!"Pankaj said: "Dr Mirjalili teaches with a very good pace and conveys the concept clearly. The examples he uses are very relatable and he makes learning tricky concepts really fun."Oyakhilome said: "Another great course by Dr. Seyedali. All components of the course were well structured and tailored to meet the educational needs of the students. I strongly recommend this course to everyone new to the field of optimization."Join 1000+ students and start your optimization journey with us. If you are in any way not satisfied, for any reason, you can get a full refund from Udemy within 30 days. No questions asked. But I am confident you won't need to. I stand behind this course 100% and am committed to help you along the way.
