Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem

Abstract

This chapter proposes a combination of estimation of distribution algorithm (EDA) and the 2-opt local search algorithm (EDA/LS) for the quadratic assignment problem (QAP). In EDA/LS, a new operator, called guided mutation, is employed for generating new solutions. This operator uses both global statistical information collected from the previous search and the location information of solutions found so far. The 2-opt local search algorithm is applied to each new solution generated by guided mutation. A restart strategy based on statistical information is used when the search is trapped in a local area. Experimental results on a set of QAP test instances show that EDA/LS is comparable with the memetic algorithm of Merz and Freisleben and outperforms estimation of distribution algorithm with guided local search (EDA/GLS). The proximate optimality principle on the QAP is verified experimentally to justify the rationale behind heuristics (including EDA/GLS) for the QAP.