The Multidimensional Knapsack Problem: Structure and Algorithms

The Multidimensional Knapsack Problem: Structure and Algorithms

Structure and Algorithms Jakob Puchinger NICTA Victoria Laboratory Department of Computer Science & Software Engineering University of Melbourne, Australia [email protected] Günther R. Raidl Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria [email protected] Ulrich Pferschy Department of Statistics and Operations Research University of Graz, Au...

Heuristic algorithms for the multiple-choice multidimensional knapsack problem

Ant Algorithm for the Multidimensional Knapsack Problem

We propose a new algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for the Multidimensional Knapsack Problem, the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. We show that our new algorithm obtains better results than two other ACO algorithms on most instances.

Neural metaheuristics for the multidimensional knapsack problem

The multidimensional knapsack problem (MKP) belongs to a very important class of integer optimization problems. In this study, we propose, develop and test metaheuristics based on the neural-networks paradigm for solving the MKP. We show how domain-specific knowledge can be incorporated within the neural-network framework for solving this NP-Hard problem. We provide a mathematical formulation o...

Multidimensional Knapsack Problem: The Influence of Representation

Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Five representations are investigated for the Multidimensional Knapsack problem. Common mutation operators (like bit-flip mutation) and classic 1-point and uniform crossover are employed to generate fit...