A Comparative Study of Meta-Heuristic Optimization Algorithms for 0 - 1 Knapsack Problem: Some Initial Results

Abstract

In this paper, we present some initial results of several meta-heuristic optimization algorithms, namely, genetic algorithms, simulated annealing, branch and bound, dynamic programming, greedy search algorithm, and a hybrid genetic algorithm-simulated annealing for solving the 0-1 knapsack problems. Each algorithm is designed in such a way that it penalizes infeasible solutions and optimizes the feasible solution. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The numerical results of the hybrid algorithm are compared with the results achieved by the individual algorithms. The results revealed the superior performances of the branch and bound dynamic programming, and hybrid genetic algorithm with simulated annealing methods over all the compared algorithms. This performance was established by taking into account both the algorithm computational time and the solution quality. In addition, the obtained results also indicated that the hybrid algorithm can be applied as an alternative to solve small- and large-sized 0-1 knapsack problems.