群体智能与随机化技术的效率分析
Efficiency Analysis of Swarm Intelligence and Randomization Techniques
摘要：
群体智能已经成为解决设计和调度任务的强大技术。元启发式算法是这种范式的一个组成部分，粒子群优化算法通常被视为一个重要的里程碑。基于群的算法的出色性能和效率激发了许多新的发展，尽管元计算的数学理解仍然是一个谜。与经典的确定性算法相比，诸如PSO之类的元算法总是使用某种形式的随机性，并且这种随机化现在使用各种技术。本文旨在回顾和分析与元算法相关的一些收敛性和效率，如萤火虫算法，随机游动，和Lévy飞行。我们将讨论如何使用这些技术及其对进一步研究的影响。
英文摘要：
Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark. The outstanding performance and efficiency of swarm-based algorithms inspired many new developments, though mathematical understanding of metaheuristics remains partly a mystery. In contrast to the classic deterministic algorithms, metaheuristics such as PSO always use some form of randomness, and such randomization now employs various techniques. This paper intends to review and analyze some of the convergence and efficiency associated with metaheuristics such as firefly algorithm, random walks, and Lévy flights. We will discuss how these techniques are used and their implications for further research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
