Solving the maximum clique problem by k-opt local search

This paper presents a local search algorithm based on variable depth search, called the k-opt local search, for the maximum clique problem. The k-opt local search performs add and drop moves, each of which can be interpreted as 1-opt move, to search a k-opt neighborhood solution at each iteration until no better k-opt neighborhood solution can be found. To evaluate our k-opt local search algorithm, we repeatedly apply the local search for each of DIMACS benchmark graphs and compare with the state-of-the-art metaheuristics such as the genetic local search and the iterated local search reported previously. The computational results show that in spite of the absence of major metaheuristic components, the k-opt local search is capable of finding better (at least the same) solutions on average than those obtained by these metaheuristics for all the graphs. Keywords Local Search, Variable Depth Search, Neighborhood, Maximum Clique Problem, Combinatorial Optimization

• Type: Conference
• Year: 2004
• Where: SAC
• Authors: Kengo Katayama, Akihiro Hamamoto, Hiroyuki Narihisa
• Keywords: Applied Computing, K-opt Local Search, Local Search, Local Search Algorithm, SAC 2004