Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Algorithms for optimizing piecewise linear functions and for degree-constrained minimum spanning tree problems
SSPMO: A Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization
INFORMS Journal on Computing
A multi-objective model for environmental investment decision making
Computers and Operations Research
Solving efficiently the 0-1 multi-objective knapsack problem
Computers and Operations Research
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem
Computational Optimization and Applications
Greedy algorithms for a class of knapsack problems with binary weights
Computers and Operations Research
Multiobjective memetic algorithms for time and space assembly line balancing
Engineering Applications of Artificial Intelligence
Solving the hard knapsack problems with a binary particle swarm approach
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Boolean lexicographic optimization: algorithms & applications
Annals of Mathematics and Artificial Intelligence
Computers & Mathematics with Applications
On beam search for multicriteria combinatorial optimization problems
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
| Hi-index | 0.00 |
We consider in this paper the solving of 0-1 knapsack problems with multiple linear objectives. We present a tabu search approach to generate a good approximation of the efficient set. The heuristic scheme is included in a redu tion decision space framework. The case of two objectives is developed in this paper. TS principles viewed into the multiobjective context are discussed. According to a prospective way, several variations of the algorithm are investigate. Numerical experiments are reported and compared with available exact efficient solutions. Intuitive justifications for the observed empirical behavior of the procedure and open questions are discussed.