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摘要种群的行为是生活的一种非常重要的行为，它存在于自然界广泛群居生物中。简单的生物个体表现了全智能的特点。如今，研究成果有生物学建模的行为，如种群和生物个体已经越来越多的在工程组进行应用与研究。拟态物理学的一种方法这是用来判断虚拟对象之间的虚拟力效应和它所遵循牛顿力学定律规则，最初的目标运用在群机器人，算法的构造问题，使应用分析通过使用人工物理学方法成群的行为，并解释了从人工物理内部决策机制。62375
许多科学，工程和应用的问题都可以变成优化问题。对于一系列的关于优化的问题，许多研究表明，寻找高效和强大的智能优化算法势在必行。拟态物理学的基础与核心思想是利用虚拟物理力来相互作用的多机器人系统，一个所需的结构或状态。该系统作为牛顿第二定律的模拟。人工物理专注于小种群集合产生复杂的行为在于实体之间简单的吸引力或排斥力法则。 拟态物理学框架已经施加到分布式机器人群的控制，这已经成为解决分布式的有效工具复杂的问题途径。在本文中，一种新的优化算法提出了一种基于拟态物理学方法和算法的几个方面，例如它的一般框架，框架建立，测试函数优化，与其他算法进行比较等进行了研究，使之更加有效。
毕业论文关键词：种群行为；拟态物理学；优化；人工智能
Abstract Population behavior is a very important act of life, which exists widely in nature gregarious creatures. Simple organisms inpidual performance of the whole intelligence features. Today, research has biological modeling behavior, such as stocks and inpidual organism has been increasingly applied in the engineering and research group. One way of doing physics, which is used to determine a virtual force effect between the virtual object and it follows the rules of the laws of Newtonian mechanics, the initial target group of robots used in the structural problems, the algorithm so that the application through the use of artificial physics analysis the method of flocks of behavior, and explains from doing physical internal decision-making mechanisms.
Optimization problem is in the real world in many areas of common. Many scientific, engineering and applications can become optimization problem. In order to solve complex optimization problems, many studies have been done to find an efficient and powerful intelligent optimization algorithms. Artificial physics is a multi-robot system consists of virtual physical force of the drive, structure, or a desired state. The system is an analog of Newton's second law. Artificial physical focus on small population set to generate complex behaviors that simple law of attraction or repulsion between the entities. AP framework has been applied to the control group distributed robot, which has become an effective tool for solving complex problems of distributed channels. In this paper, a new optimization algorithm is proposed based on several aspects of AP methods and algorithms, such as its general framework, the framework is established, the test function optimization, comparison with other algorithms have been studied to make it more effective.
Keywords: Optimization problem; swarm; artificial physics
第一章 绪论 1
1.1 论文选题的研究意义和背景 1
1.2 优化算法 1
1.2.1 优化问题 1
1.2.2 优化问题局限性 2
1.3 群体智能 2
1.3.1 群体智能基础 2
1.3.2 群体智能算法 3
1.4 拟态物理学