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 (where no previous algorithm has achieved) as well as improved results for six instances. Analysis of comparisons with state-of-the-art algorithms demonstrates statistically that our TS/MA 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)

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using some randomly generated instances.genetic algorithm;heuristics;opportunities;maintenance optimization;memetic algorithm