Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Journal of the ACM (JACM)
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Approximating Multiobjective Knapsack Problems
Management Science
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multicriteria network design using evolutionary algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Multiobjective EA approach for improved quality of solutions for spanning tree problem
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Multiobjective 0-1 knapsack problem involving multiple knapsacks is a widely studied problem. In this paper, we consider a formulation of the biobjective 0-1 knapsack problem which involves a single knapsack; this formulation is more realistic and has many industrial applications. Though it is formulated using simple linear functions, it is an NP-hard problem. We consider three different types of knapsack instances, where the weight and profit of an item is (i) uncorrelated, (ii) weakly correlated, and (iii) strongly correlated, to obtain generalized results. First, we solve this problem using three well-known multiobjective evolutionary algorithms (MOEAs) and quantify the obtained solution-fronts to observe that they show good diversity and (local) convergence. Then, we consider two heuristics and observe that the quality of solutions obtained by MOEAs is much inferior in terms of the extent of the solution space. Interestingly, none of the MOEAs could yield the entire coverage of the Pareto-front. Therefore, based on the knowledge of the Pareto-front obtained from the heuristics, we incorporate problem-specific knowledge in the initial population and obtain good quality solutions using MOEAs too. We quantify the obtained solution fronts for comparison. The main point we stress with this work is that, for real world applications of unknown nature, it is indeed difficult to realize how good/bad is the quality of the solutions obtained. Conversely, if we know the solution space, it is trivial to obtain the desired set of solutions using MOEAs, which is a paradox in itself.