An Ant Colony Optimization Algorithm for Solving the Multidimensional Knapsack Problems

Ant colony optimization (ACO) algorithm is a metaheuristic and stochastic search technology, which has been one of the effective tools for solving discrete optimization problems. However, there are two bottlenecks for large-scaled optimization problems: the ACO algorithm needs too much time to convergent and the solutions may not be really optimal. This paper proposes a novel ACO algorithm for the multidimensional knapsack problems (MKP), which employs a new pheromone diffusion model and a mutation scheme. First, in light of the preference to better solutions, the association distances among objects are mined in each iteration with top-k strategy. Then, a pheromone diffusion model based on info fountain of an object is established, which strengthens the collaborations among ants. Finally, an unique mutation scheme is applied to optimizing the evolution results of each step. The experimental results for the benchmark testing set of MKPs show that the proposed algorithm can not only get much more optimal solutions but also greatly enhance convergence speed.

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