用群求解数值优化问题

M. Črepinšek, M. Mernik, V. Zumer
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引用次数: 1

摘要

本文提出了一种类似粒子群优化算法的优化搜索方法。提出的方法是利用人工生命中基于群的生物的聚集运动。生物有团结在一起的社会倾向,并表现出类似生命的突发行为,这是基于一些简单的,局部的规则。它们与大多数其他人工生命群集(boids型)实现的不同之处在于,它们会被“坐在”最佳发现结果上的生物所吸引。它们的紧急行为产生了对所有可能解决方案的有效搜索。通常用于解决这类问题的进化策略被用来比较搜索的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using flocks for solving numerical optimization problems
The paper deals with an alternative numerical approach to optimum searching, which is similar to the particle swarm optimizer. The proposed approach uses aggregate motion of creatures in the artificial life based on flocks. Creatures have the social tendency to stick together, and to perform lifelike emergent behaviour, which is based on a few simple, local rules. They differ from most other artificial life flocking (Boids-type) implementations by being attracted by creatures that "sit" on the best found result. Their emergent behaviour produces effective search in the space of all possible solutions. Evolution Strategies, which are often used for solving this kind of problems, were used to compare the quality of searches.
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