Browsing by Author "Garcia, Jose"

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    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.
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    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.
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    Mesostructural Model for the Fatigue Analysis of Open-Cell Metal Foams
    (2024) Pinto, Hernan; Sepulveda, Alexander; Moraga, Paola; Galvez, Hector A.; Pena, Alvaro; Gornall, Jose; Garcia, Jose
    Metallic foams exhibit unique properties that make them suitable for diverse engineering applications. Accurate mechanical characterization is essential for assessing their performance under both monotonic and cyclic loading conditions. However, despite the advancements, the understanding of cyclic load responses in metallic foams has been limited. This study aims to propose a mesostructural model to assess the fatigue behavior of open-cell metal foams subjected to cyclic loading conditions. The proposed model considers the previous load history and is based on the analogy of progressive collapse, integrating a finite element model, a fatigue analysis model, an equivalent number of cycles model, and a failure criterion model. Validation against experimental data shows that the proposed model can reliably predict the fatigue life of the metallic foams for specific strain amplitudes.
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    Optimizing Retaining Walls through Reinforcement Learning Approaches and Metaheuristic Techniques
    (2023) Lemus-Romani, Jose; Ossandon, Diego; Sepulveda, Rocio; Carrasco-Astudillo, Nicolas; Yepes, Victor; Garcia, Jose
    The structural design of civil works is closely tied to empirical knowledge and the design professional's experience. Based on this, adequate designs are generated in terms of strength, operability, and durability. However, such designs can be optimized to reduce conditions associated with the structure's design and execution, such as costs, CO2 emissions, and related earthworks. In this study, a new discretization technique based on reinforcement learning and transfer functions is developed. The application of metaheuristic techniques to the retaining wall problem is examined, defining two objective functions: cost and CO2 emissions. An extensive comparison is made with various metaheuristics and brute force methods, where the results show that the S-shaped transfer functions consistently yield more robust outcomes.
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    Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector
    (2022) Becerra-Rozas, Marcelo; Lemus-Romani, Jose; Cisternas-Caneo, Felipe; Crawford, Broderick; Soto, Ricardo; Garcia, Jose
    In recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, to use this type of metaheuristics, it is necessary to adapt them to work in binary environments, and in general, this adaptation is not trivial. The method proposed in this work evaluates the use of reinforcement learning techniques in the binarization process. Specifically, the backward Q-learning technique is explored to choose binarization schemes intelligently. This allows any continuous metaheuristic to be adapted to binary environments. The illustrated results are competitive, thus providing a novel option to address different complex problems in the industry.