Abstract
For multi-objective optimization problems, it is meaningful to compute a set of solutions covering all possible trade-offs between the different objectives. The multi-objective knapsack problem is a generalization of the classical knapsack problem in which each item has several profit values. For this problem, efficient algorithms for computing a provably good approximation to the set of all non-dominated feasible solutions, the Pareto frontier, are studied. For the multi-objective 1-dimensional knapsack problem, a fast fully polynomial-time approximation scheme is derived. It is based on a new approach to the single-objective knapsack problem using a partition of the profit space into intervals of exponentially increasing length. For the multi-objective m-dimensional knapsack problem, the first known polynomial-time approximation scheme, based on linear programming, is presented.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
