Browsing by Author "Astorga, Gino"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem
    (2021) Garcia, Jose; Lemus-Romani, Jose; Altimiras, Francisco; Crawford, Broderick; Soto, Ricardo; Becerra-Rozas, Marcelo; Moraga, Paola; Paz Becerra, Alex; Pena Fritz, Alvaro; Rubio, Jose-Miguel; Astorga, Gino
    Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.
  • No Thumbnail Available
    Item
    A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems
    (2021) Lemus-Romani, Jose; Becerra-Rozas, Marcelo; Crawford, Broderick; Soto, Ricardo; Cisternas-Caneo, Felipe; Vega, Emanuel; Castillo, Mauricio; Tapia, Diego; Astorga, Gino; Palma, Wenceslao; Castro, Carlos; Garcia, Jose
    Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State-Action-Reward-State-Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.