论文标题
物理启发的元启发式优化技术的简要概述
A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques
论文作者
论文摘要
元神经算法是设计用于有效解决计算挑战性优化问题的方法。研究人员从各种自然和物理过程中汲取了灵感，以制定元映射，这些元式术成功地为几项工程任务提供了近乎最佳或最佳的解决方案。本章重点介绍了具有具有具体优化范式的非线性物理现象建立的元效力算法，该算法表现出了强大的探索和针对此类优化问题的强大探索和剥削能力。具体而言，本章侧重于几种流行的基于物理的元启发术，并描述了与每种算法相关的潜在独特物理过程。
Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfully provided near-optimal or optimal solutions to several engineering tasks. This chapter focuses on meta-heuristic algorithms modelled upon non-linear physical phenomena having a concrete optimization paradigm, having shown formidable exploration and exploitation abilities for such optimization problems. Specifically, this chapter focuses on several popular physics-based metaheuristics as well as describing the underlying unique physical processes associated with each algorithm.
