.png){kind=icon}
.png){kind=icon}
Return
Solving binary multi-objective knapsack problems with novel greedy strategy
.png){kind=icon}
PRE
AI
DOI：10.1007/s12293-021-00344-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper shows that the many greedy strategies that have been designed to repair infeasible solutions to multi-objective knapsack problems (MOKPs) with small item differences perform poorly when item differences are large. To effectively solve different types of MOKPs, this paper proposes a greedy strategy to improve the quality of feasible and infeasible solutions. It repairs all of the infeasible solutions to feasible solutions, and then maximizes the quality of each feasible solution under the limitations of knapsack capacities. Simulation experiments on different types of MOKPs show that the proposed strategy is superior to existing strategies. Compared with MOGLS, MOEA/D, and MOEA/D-M2M, the proposed evolutionary framework performs better in solving different MOKPs.
Keywords:
Knapsack problems
Greedy strategy
Multi-objective optimization
Evolutionary algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.3
Papers:
453
Citations:
718