Steepest-ascent constrained simultaneous perturbation for multiobjective optimization

Daniel W. McClary, Violet Syrotiuk, Murat Kulahci

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The simultaneous optimization of multiple responses in a dynamic system is challenging. When a response has a known gradient, it is often easily improved along the path of steepest ascent. On the contrary, a stochastic approximation technique may be used when the gradient is unknown or costly to obtain. We consider the problem of optimizing multiple responses in which the gradient is known for only one response. We propose a hybrid approach for this problem, called simultaneous perturbation stochastic approximation steepest ascent, SPSA-SA or SP(SA)2 for short. SP(SA)2 is an SPSA technique that leverages information about the known gradient to constrain the perturbations used to approximate the others. We apply SP(SA)2 to the cross-layer optimization of throughput, packet loss, and end-to-end delay in a mobile ad hoc network (MANET), a selforganizing wireless network. The results show that SP(SA)2 achieves higher throughput and lower packet loss and end-to-end delay than the steepest ascent, SPSA, and the Nelder-Mead stochastic approximation approaches. It also reduces the cost in the number of iterations to perform the optimization.

Original languageEnglish (US)
Article number2
JournalACM Transactions on Modeling and Computer Simulation
Volume21
Issue number1
DOIs
StatePublished - Dec 1 2010

Keywords

  • Cross-layer optimization
  • Mobile ad hoc networks
  • Multiobjective optimization
  • Nongradient optimization
  • Stochastic approximation

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Steepest-ascent constrained simultaneous perturbation for multiobjective optimization'. Together they form a unique fingerprint.

Cite this