Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

Abstract

When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be numerous and make it complicated for classical algorithmic techniques to address them. When this happens, in most cases, it becomes a problem due to the high resource cost they generate, so it is of utmost importance to solve these problems efficiently. To cope with this problem, we can apply other methods, such as metaheuristics. There are some metaheuristics that allow operation in discrete search spaces; however, in the case of continuous swarm intelligence metaheuristics, it is necessary to adapt them to operate in discrete domains. To perform this adaptation, it is necessary to use a binary scheme to take advantage of the original moves of the metaheuristics designed for continuous problems. In this work, we propose to hybridize the whale optimization algorithm metaheuristic with the Q-learning reinforcement learning technique, which we call (the QBWOA). By using this technique, we are able to realize an smart and fully online binarization scheme selector, the results have been statistically promising thanks to the respective tables and graphs.

Binarization of Metaheuristics: Is the Transfer Function Really Important?

“…As can be seen in Section 2.3 , metaheuristics perform the search process alternating between intensification (exploration) and diversification (exploitation). Previous proposals [ 2 , 31 , 40 , 60 , 61 ] used these phases as the states to use in Q-learning. In these papers, the authors determined the stage of the search process by calculating the population diversity.…”

Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics

“…Binary combinatorial problems, such as the Set Covering Problem [9,11,77,104,105], Knapsack Problem [109,110], or Cell Formation Problem [106], are increasingly common in the industry. Given the demand for good results in reasonable times, metaheuristics have begun to gain ground as resolution techniques.…”

“…In the literature [9,[104][105][106], it can be seen that the authors perform a static test to compare the experimental results to determine if there is any significant difference between each experiment. For this type of experimentation, a non-parametric statistical test must be applied.…”

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

“…In this work, we were able to evidence of the complex and broad field of research related to feature selection. Metaheuristics are algorithms that play a significant role in different Combinatorial Optimization Problems such as Set Covering [216][217][218][219][220], Knapsack Problem [221,222] and Cell Formation Problem [223]. There is also a high interest in hybridizations and modifying native metaheuristics.…”