一个新的基于人类的元启发算法,用于解决基于学前教育的优化问题
1Department of Mathematics, Faculty of Science, University of Hradec Králové, Rokitanského 62, 500 03, Hradec Králové, Czech Republic. pavel.trojovsky@uhk.cz.
Scientific reports
|December 5, 2023
概括
一个新的学前教育优化算法 (PEOA) 平衡了复杂问题的探索和利用. 在基准和工程任务上,PEOA的性能优于现有的方法,证明了它的有效性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 没有免费午餐定理强调了对专门优化算法的需求.
- 超启发式算法对于解决复杂的计算问题至关重要.
- 现有的算法可能难以有效地平衡勘探和开发.
研究的目的:
- 介绍一种新的基于人类的元启发算法,即学前教育优化算法 (PEOA).
- 通过从学前教育中汲取灵感来解决当前优化技术的局限性.
- 评估PEOA在各种基准和现实问题上的表现和有效性.
主要方法:
- PEOA是基于学前教育的三个阶段进行数学建模的:教师的影响,指导知识发展和自我意识.
- 该算法的性能使用52个标准基准函数 (单模,多模) 和CEC 2017测试套件进行评估.
- 对比分析涉及PEOA与十个已建立的元启发算法和统计测试 (Wilcoxon签名等级测试) 相比较.
主要成果:
- 在优化过程中,PEOA在平衡勘探和开采方面表现出强大的能力.
- 该算法在各种基准函数中与十个知名的元启发算法相比,实现了更高的性能.
- 统计分析证实了PEOA在竞争方法上的显著优势,并将其列为顶级优化器.
- PEOA在解决CEC 2011测试套件问题和四个工程设计挑战方面表现出有效性.
结论:
- 学前教育优化算法 (PEOA) 是一个高效且在统计学上优越的优化问题的元启发法.
- PEOA独特的设计,灵感来自教育过程,允许高效的勘探-开发平衡.
- 该算法对工程和其他复杂领域的现实应用具有显著的前景.
更多相关视频
11:53Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
13.0K
10:26Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
4.0K
相关概念视频
01:21Heuristics
93
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
93
01:29Problem-Solving
166
Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
166
01:21Cognitive Learning
246
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
246
01:22Machines: Problem Solving I
331
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
331
01:30Machines: Problem Solving II
312
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
312
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56