Changing or keeping solutions in dynamic optimization problems with switching costs

Danial YAZDANI, Jürgen BRANKE, Mohammad Nabi OMIDVAR, Trung Thanh NGUYEN, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

15 Citations (Scopus)


Dynamic optimization problems (DOPs) are problems that change over time. However, most investigations in this domain are focused on tracking moving optima (TMO) without considering the cost of switching from one solution to another when the environment changes. Robust optimization over time (ROOT) tries to address this shortcoming by finding solutions which remain acceptable for several environments. However, ROOT methods change solutions only when they become unacceptable. Indeed, TMO and ROOT are two extreme cases in the sense that in the former, the switching cost is considered zero and in the latter, it is considered very large. In this paper, we propose a new semi ROOT algorithm based on a new approach to switching cost. This algorithm changes solutions when: 1) the current solution is not acceptable and 2) the current solution is still acceptable but algorithm has found a better solution and switching is preferable despite the cost. The main objective of the proposed algorithm is to maximize the performance based on the fitness of solutions and their switching cost. The experiments are done on modified moving peaks benchmark (mMPB) and the performance of the proposed algorithm alongside state-of-the-art ROOT and TMO methods is investigated. © 2018 Association for Computing Machinery.
Original languageEnglish
Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Print)9781450356183
Publication statusPublished - 2 Jul 2018
Externally publishedYes

Bibliographical note

This work was supported in part by a Dean's Scholarship by Faculty of Engineering and Technology, LJMU, a Newton Institutional Links grant no. 172734213, funded by the UK BEIS and delivered by the British Council, a NRCP grant no. NRCP1617-6-125 delivered by Royal Academy of Engineering, two EPSRC grants (nos. EP/P005578/1 and EP/J017515/1). Xin Yao was supported by a Royal Society Wolfson Research Merit Award and Honda Research Institute Europe.


  • Dynamic optimization problems
  • Multi-swarm methods
  • Particle swarm optimization
  • Robust optimization over time
  • Switching cost


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