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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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The manufacturing Cell Design Problem (MCDP) is a classical optimization problem that finds application in lines of manufacture. The problem consist in distributing machines in cells, where the parts processed by each machine travels in the production process in such a way that productivity is improved. To solve the MCDP we employ a novel metaheuristic, which is inspired by actions, attitudes, and conducts that people normally have in life, named Human behavior-based optimization (HBBO). An individual try to evolve in life by trying his best in order to be a better human being with a brilliant future, successful at life, and be an example for others. We couple the HBBO with Autonomous Search (AS), which allows the modification of internal components of our approach when exposed to changing external forces and opportunities. We compare our HBBO-AS with the classic HBBO and an implementation using IRace, which is a software package that allows us to automatize the configuration of an algorithm through automatic configuration procedures. Additionally, in order to test the competitiveness of our results, we compare with other algorithms proved to perform well solving the MCDP. We illustrate experimental results, where the proposed approach is able to obtain interesting performance and robustness in the 125 well-known instances of the MCDP.