An intelligent multi-colony multi-objective ant colony optimization (ACO) for the 0–1 knapsack problem

Abstract

The knapsack problem is a famous optimization problem. Even the single objective case has been proven to be NP-hard the multi-objective is harder than the single objective case. This paper presents the modified ant colony optimization (ACO) algorithm for solving knapsack multi-objective problem to achieve the best layer of non-dominated solution. We also proposed a new pheromone updating rule for multi-objective case which can increase the learning of algorithm and consequently increase effectiveness. Finally, the computational result of proposed algorithm is compared with the NSGA II which outperforms most of the multi-objective ant colony optimization algorithm which are reviewed in this paper.