On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Running time analysis of evolutionary algorithmson a simplified multiobjective knapsack problem
Natural Computing: an international journal
Probabilistic analysis of knapsack core algorithms
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Two-phase Pareto local search for the biobjective traveling salesman problem
Journal of Heuristics
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
Greedy algorithms for a class of knapsack problems with binary weights
Computers and Operations Research
Connectedness and local search for bicriteria knapsack problems
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Analyzing the effect of objective correlation on the efficient set of MNK-Landscapes
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
In this article, a local search approach is proposed for three variants of the bi-objective binary knapsack problem, with the aim of maximizing the total profit and minimizing the total weight. First, an experimental study on a given structural property of connectedness of the efficient set is conducted. Based on this property, a local search algorithm is proposed and its performance is compared to exact algorithms in terms of runtime and quality metrics. The experimental results indicate that this simple local search algorithm is able to find a representative set of optimal solutions in most of the cases, and in much less time than exact algorithms.