After the seminal books by Martello and Toth (1990) and Kellerer, Pferschy, and Pisinger
(2004), knapsack problems became a classical and rich research area in combinatorial …

Optimization techniques known as metaheuristics have been applied successfully to solve
different problems, in which their development is characterized by the appropriate selection …

Reactive Search integrates sub-symbolic machine learning techniques into search
heuristics for solving complex optimization problems. By automatically adjusting the working …

The class of vehicle routing problems involves the optimization of freight or passenger
transportation activities. These problems are generally treated via the representation of the …

The ever-growing deployment scale of surveillance cameras and the users' increasing
appetite for real-time queries have urged online video analytics. Synergizing the virtually …

During the last few years we have witnessed impressive developments in the area of
stochastic local search techniques for intelligent optimization and Reactive Search …

Abstract The Multidimensional Multiple-choice Knapsack Problem (MMKP) is a complex
combinatorial optimization problem for which finding high quality feasible solutions is very …

Many practical decision-making problems involve selecting a subset of objects from a set of
candidate objects such that the selected objects optimize a given objective while satisfying …

In this paper, we propose an optimal algorithm for the Multiple-choice Multidimensional
Knapsack Problem MMKP. The main principle of the approach is twofold:(i) to generate an …

Recent advances in algorithms for the multidimensional multiple choice knapsack problems
have enabled us to solve rather large problem instances. However, these algorithms are …