A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

Soto, Ricardo; Crawford, Broderick; Olivares, Rodrigo; Carrasco, Cesar; Rodríguez-Tello, Eduardo; Castro, Carlos; Paredes, Fernando; Fuente‐Mella, Hanns de la

doi:10.3390/math8091389

Cited by 7 publications

(6 citation statements)

References 81 publications

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“…The accuracy of the classification is to evaluate the performance of the model during the test. Formula (11) is the calculation of MSE, and the calculation method of accuracy is as follows (39).…”

Section: Methodsmentioning

confidence: 99%

“…With continuous research and development, the algorithm cluster is growing, and a variety of algorithms arises at the historic moment, most of them inspired by biological evolution in nature. A example is the genetic algorithm (GA), which maintains and improves multiple candidate solutions based on population method and uses population characteristics to guide search [36,37], and another typical algorithm is the differential evolution algorithm (DE) of heuristic search based on population [38,39]. Moreover, inspired by biogeography theory, a biogeographic optimization algorithm (BBO) is proposed [40,41], which based on study of mathematical model of biological species migration.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Dendritic Neural Model for Classification Problems

Jia

Zhong

et al. 2021

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In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Dendritic Neural Model for Classification Problems

Jia

Zhong

et al. 2021

Symmetry

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“…Here, the new version intensifies the local search towards more promising areas. Finally, the work [27] proposes integrating the autonomous search paradigm in the dolphin echolocation algorithm for population self-regulation.…”

Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Preprint

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Stagnation at local optima represents a significant challenge in bio–inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca Predator Algorithm with Deep Q–Learning. Orca Predator Algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q–Learning is a reinforcement learning technique that combines Q–Learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of Orca Predator Algorithm to identify promising regions nearby in the search space, complemented by the fine–tuning capabilities of Deep Q–Learning to navigate these areas precisely. The practical application of this approach is evaluated using the high–dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of Orca Predator Algorithm against its native version and a set of state–of–the–art algorithms.

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“…Here, the new version intensifies the local search toward more promising zones. Finally, the work in [27] proposes integrating the autonomous search paradigm in the dolphin echolocation algorithm for population self-regulation. The paradigm is applied when stagnation of a local optimum is detected.…”

Section: Related Workmentioning

confidence: 99%

A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

Olivares,

Soto,

Crawford

et al. 2023

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This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.

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A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

Cited by 7 publications

References 81 publications

Intelligent Dendritic Neural Model for Classification Problems

Intelligent Dendritic Neural Model for Classification Problems

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

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