Solving the hard knapsack problems with a binary particle swarm approach

  • Authors:

  • Bin Ye;Jun Sun;Wen-Bo Xu

  • Affiliations:

  • School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, P.R. China;School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, P.R. China;School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, P.R. China

  • Venue:
  • ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Knapsack problems are important NP-Complete combinatorial optimization problems. Although nearly all the classical instances can be solved in pseudo-polynomial time nowadays, yet there are a variety of test problems which are hard to solve for the existing algorithms. In this paper we propose a new approach based upon binary particle swarm optimization algorithm (BPSO) to find solutions of these hard knapsack problems. The standard PSO iteration equations are modified to operate in discrete space. Furthermore, a heuristic operator based on the total-value greedy algorithm is employed into the BPSO approach to deal with constrains. Numerical experiments show that the proposed algorithm outperforms both the existing exact approaches and recent state-of-the-art search heuristics on most of the hard knapsack problems.