Binarization of Metaheuristics: Is the Transfer Function Really Important?

Abstract

In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.

Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics

“…Given the experimental results and the statistical tests applied, we can indicate that the binarization rule has a high impact on the binarization process of continuous metaheuristics, as indicated by the authors in [104]. In addition to this, chaotic maps also have an impact on the behavior of metaheuristics, which can be observed in the experimental results, convergence graphs, and statistical tests.…”

“…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.…”

A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm

“…Examples of such MHs include the binary bat algorithm [35,36], binary particle swarm optimization [37], binary sine-cosine algorithm [38,39], binary salp swarm algorithm [40], binary grey wolf optimizer [39,41], binary dragonfly algorithm [42,43], binary whale optimization algorithm [39], and binary magnetic optimization algorithm [44]. In the scientific literature, two main groups of binary schemes used to solve combinatorial problems can be identified [45]. The first group refers to operators that do not cause alterations in the operations related to different elements of the MH.…”

Integration of Edge AI and Metaheuristic Algorithms for Advanced Optimization and Analytical Solutions in Future Smart Systems

“…As research in metaheuristic optimization continues, these algorithms are poised to play a pivotal role in addressing complex challenges across diverse domains. [10] Edge AI enables data processing and artificial intelligence applications to be conducted closer to the data source, as opposed to centralized servers. This offers several advantages, including reduced latency, enhanced data security, improved energy efficiency, and faster response times.…”