Skip Nav Destination
Engineering Optimization: Applications, Methods, and Analysis
ISBN:
9781118936337
No. of Pages:
770
Publisher:
ASME Press
Publication date:
2018
Direct searches do not use gradient or second-derivative information. They do not use models of the surface. Direct searches only use function evaluation, and the trial solution sequence is directed either by human logical or stochastic rules. Typically they creep up to optima, as opposed to understanding the surface and jumping to, or near to, the perfect answer. One might think, then, that direct searches are inferior. Well, they are inferior to second-order methods but only for the limited class of applications that meet the ideal conditions of deterministic functions with continuum variables and derivatives, no constraints, no flat spots,...
11.1
Introduction
11.2Cyclic Heuristic Direct (CHD) Search
11.3Hooke–Jeeves (HJ)
11.4Compare and Contrast CHD and HJ Features: A Summary
11.5Nelder–Mead (NM) Simplex: Spendley, Hext, and Himsworth
11.6Multiplayer Direct Search Algorithms
11.7Leapfrogging
11.8Particle Swarm Optimization
11.9Complex Method (CM)
11.10A Brief Comparison
11.11Takeaway
11.12Exercises
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Enhancements to Optimizers
Engineering Optimization: Applications, Methods, and Analysis
Boosting Classification Accuracy with Samples Chosen from a Validation Set
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Model-Building for Robust Reinforcement Learning
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
Detection of Trivial Image Tampering Via Unmatched Feature Points Clustering
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)
Related Articles
An Efficient First-Principles Saddle Point Searching Method Based on Distributed Kriging Metamodels
ASME J. Risk Uncertainty Part B (March,2018)
Knowledge Acquisition of Self-Organizing Systems With Deep Multiagent Reinforcement Learning
J. Comput. Inf. Sci. Eng (April,2022)
Examining the Robustness of Grasping Force Optimization Methods Using Uncertainty Analysis
ASME J. Risk Uncertainty Part B (December,2018)
