The cascade optimization algorithm: A new distributed approach for the stochastic optimization of engineering applications

Antonis C. Kokossis, Patrick Linke, Siyu Yang

Research output: Contribution to journalArticle

4 Citations (Scopus)


This paper introduces a new stochastic optimization approach in the form of a cascade optimization algorithm. The algorithm incorporates concepts from Markov processes while eliminating the inherent sequential nature that is a major obstacle preventing the exploitation of advances in distributed computing infrastructures. This method introduces partitions and pools to store intermediate solutions and corresponding objectives. A Markov process increases the population of partitions and pools. The population is distributed periodically, following an external certainty. With the use of partitions and pools, multiple Markov processes can be launched simultaneously for different partitions and pools. The cascade optimization algorithm holds a potential in two different fronts. One aims at its deployment in parallel and distributed computing environments. Through storage of solutions in the pools, the algorithm further offers cost-effective means to analyze intermediate solutions, visualize progress, and integrate optimization with data and/or knowledge management techniques without additional burden to the process.

Original languageEnglish
Pages (from-to)5266-5278
Number of pages13
JournalIndustrial and Engineering Chemistry Research
Issue number9
Publication statusPublished - 4 May 2011


ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Cite this