An empirical study of population-based metaheuristics for the multiple-choice multidimensional knapsack problem

摘要

In this paper, we study the performance of five population-based metaheuristics to solve a large (393) number of comprehensive problem instances from the literature for the important (NP-Hard) multiple choice multidimensional knapsack problem (MMKP). The five metaheuristics are: teaching-learning-based optimisation (TLBO), artificial bee colony (ABC), genetic algorithm (GA), criss-cross optimisation algorithm (COA), and binary bat algorithm (BBA). All five of these metaheuristics are similar in that they transform a population of solutions in an effort to improve the solutions in the population and they are all implemented in a straightforward manner. Statistically (over all 393 problem instances), we show that COA, GA, and TLBO give similar results which are better than other published solution approaches for the MMKP. However, if we incorporate a simple neighbourhood search into each of these five metaheuristics, in addition to improved solution quality, there is now no statistically significant difference among the results for these five metaheuristics.