Optimization algorithms and particularly metaheuristics are constantly improved with the goal of reducing execution times, increasing the quality of solutions, and addressing larger target instances. Hybridizing techniques are one of these methods particularly interesting for the broad scope of problems to which they can be adapted. In this work, we assessed a hybrid algorithm that uses the k-nearest neighbor technique to improve the results of a quantum cuckoo search algorithm for resource allocation. The k-nearest neighbor technique is used to direct the movement of solutions. Numerical experiments were performed to obtain insights from the contribution of the k-nearest neighbor technique in the final result of solutions. The well-known multidimensional knapsack problem was addressed in order to validate our procedure; a comparison is made with state-of-the-art algorithms. Our results show that our hybrid algorithm consistently produces better results in most of the analyzed instances.
- Combinatorial optimization
- Machine learning
- Multidimensional knapsack problem