Meta-heuristic combining prior online and offline information for the quadratic assignment problem

Jianyong SUN, Qingfu ZHANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

17 Citations (Scopus)


The construction of promising solutions for N{script}P-hard combinatorial optimization problems (COPs) in meta-heuristics is usually based on three types of information, namely a priori information, a posteriori information learned from visited solutions during the search procedure, and online information collected in the solution construction process. Prior information reflects our domain knowledge about the COPs. Extensive domain knowledge can surely make the search effective, yet it is not always available. Posterior information could guide the meta-heuristics to globally explore promising search areas, but it lacks local guidance capability. On the contrary, online information can capture local structures, and its application can help exploit the search space. In this paper, we studied the effects of using this information on meta-heuristic's algorithmic performances for the COPs. The study was illustrated by a set of heuristic algorithms developed for the quadratic assignment problem. We first proposed an improved scheme to extract online local information, then developed a unified framework under which all types of information can be combined readily. Finally, we studied the benefits of the three types of information to meta-heuristics. Conclusions were drawn from the comprehensive study, which can be used as principles to guide the design of effective meta-heuristic in the future. © 2013 IEEE.
Original languageEnglish
Article number6517261
Pages (from-to)429-444
Number of pages16
JournalIEEE Transactions on Cybernetics
Issue number3
Early online date17 May 2013
Publication statusPublished - Mar 2014
Externally publishedYes


  • Fitness landscape analysis
  • meta-heuristics
  • offline global information
  • online local information
  • quadratic assignment problem


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