A tabu search based memetic algorithm for the maximum diversity problem

This paper presents a highly effective memetic algorithm for the maximum diversity problem based on tabu search. The tabu search component uses a successive filter candidate list strategy and the solution combination component employs a combination operator based on identifying strongly determined and consistent variables. Computational experiments on three sets of 40 popular benchmark instances indicate that our tabu search/memetic algorithm (TS/MA) can easily obtain the best known results for all the tested instances (which no previous algorithm has achieved) as well as improved results for 6 instances. Analysis of comparisons with state-of-the-art algorithms demonstrate statistically that our TS/MA algorithm competes very favorably with the best performing algorithms. Key elements and properties of TS/MA are also analyzed to disclose the benefits of integrating tabu search (using a successive filter candidate list strategy) and solution combination (based on critical variables). keywords: combinatorial optimization; maximum diversity problem; metaheuristics; tabu search; memetic algorithm ∗ Corresponding author. Email addresses: [email protected] (Yang Wang), [email protected] (Jin-Kao Hao), [email protected] (Fred Glover), [email protected] (Zhipeng Lü). Preprint submitted to Elsevier 3 September 2013