A novel multi-population genetic algorithm for multiple-choice multidimensional knapsack problems

Authors:
Qian Zhou;Wenjian Luo
Affiliations:
Nature Inspired Computation and Applications Lab., School of Computer Science and Techn., Univ. of Science and Techn. of China, Hefei, Anhui, China and Anhui Key Lab. of Software in Computing and ...;Nature Inspired Computation and Applications Lab., School of Computer Science and Techn., Univ. of Science and Techn. of China, Hefei, Anhui, China and Anhui Key Lab. of Software in Computing and ...
Venue:
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Year:
2010

Citing 5
Cited 0

A Reactive Local Search-Based Algorithm for the Multiple-Choice Multi-Dimensional Knapsack Problem

Computational Optimization and Applications
Solving the multidimensional multiple-choice knapsack problem by constructing convex hulls

Computers and Operations Research
Hard multidimensional multiple choice knapsack problems, an empirical study

Computers and Operations Research
A column generation method for the multiple-choice multi-dimensional knapsack problem

Computational Optimization and Applications
A New Heuristic for Solving the Multichoice Multidimensional Knapsack Problem

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel Multi-Population Genetic Algorithm (MPGA) is proposed to solve the Multiple-choice Multidimensional Knapsack Problem (MMKP), a kind of classical combinatorial optimization problems. The proposed MPGA has two evolutionary populations and one archive population, and can effectively balance the search biases between the feasible space and the infeasible space. The experiment results demonstrate that the proposed MPGA is better than the existing algorithms, especially when the strength of constraints is relatively strong.