An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications

Alagöz, Barış Baykant; Simsek, Ozlem Imik; Ari, Davut; Tepljakov, Aleksei; Petlenkov, Eduard; Alimohammadi, Hossein

doi:10.3390/s22103836

Cited by 8 publications

(4 citation statements)

References 82 publications

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“…Developing a metaheuristic method typically involves two steps: exploration and exploitation. As mentioned in [9], exploring metaheuristic methods entails studying and evaluating various techniques to assess their suitability for solving a specific problem. Another approach to exploring metaheuristic methods involves experimenting with different variations of a particular algorithm.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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A novel approach to the development of neural network architecture based on metaheuristic protis approach

Harumy,

Zarlis,

Lydia

et al. 2023

EEJET

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Determining the best model for the neural network architecture and how to optimize the architecture with the metaheuristic Protis Approach is a subject of the study. A comprehensive investigation and utilization of metaheuristic methods are necessary. These methods aim to solve problems and adapt from the lifestyle of the amoeba protis. In this study, the proposed method modifies the life cycle of the amoeba, which consists of four phases: prophase, metaphase, anaphase, and telophase. These four phases are modified in the neural network architecture to optimize the appropriate number of hidden layers and produce an efficient architecture model. The results show that the protis approach optimized the neural network architecture, especially in generating hidden layers to improve the neural network model. Distinctive features of the results obtained are that the average range of degenerate neurons in the hidden layer is 0 to 35 neurons in each layer. The standard number of neurons makes it possible to solve the problem of determining the best model on the neural network architecture. The protis algorithm embedded in the protis recurrent neural network for categorical data measurements produces an average RMSE value, representing the difference between actual measurements and predictions, equal to 0.066. Consequently, the developed model surpasses the current classical neural network model in terms of performance. Regarding accuracy, the protis algorithm embedded in the neural network for categorical and time series data achieves an average precision of 0.952 and a recall of 0.950. The protis convolutional neural network achieves an accuracy of 95.9 %. Therefore, from the three tested datasets, the protis convolutional neural network exhibits the highest accuracy value

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Previous studies have identified that one of the reasons for the slow training process of neural networks is the inappropriate number of hidden layers [7]. However, currently, the average standard number of neurons that are set to determine the optimal number of hidden layers in a model still varies [4,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to the development of neural network architecture based on metaheuristic protis approach

Harumy,

Zarlis,

Lydia

et al. 2023

EEJET

View full text Add to dashboard Cite

show abstract

“…These algorithms are mainly based on the evolutionary process of organisms in the natural world and adopt the "survival of the fittest" theory to realize the optimization of the search space. Cooperative co-evolutionary algorithms (CCEA) 22 , evolutionary mating algorithms (EMA) 23 , evolutionary field optimization algorithms (EFO) 24 , and quantum-based avian navigation optimizer algorithm (QANA) 25 belong to this type of algorithm. The main search mechanisms of such algorithms are crossover and mutation.…”

Section: Introductionmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

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The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.

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“…The study [12] introduces the evolutionary field theorem of search agents and proposes the Evolutionary Field Optimization with Geometric Strategies (EFO-GS) algorithm to improve the quality evolutionary searches. The study also modifies the multiplicative neuron model to develop Power-Weighted Multiplicative (PWM) neural models, which can better represent polynomial nonlinearity and operate in different modes.…”

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confidence: 99%

Intelligent Control and Digital Twins for Industry 4.0

Tepljakov

2023

Sensors

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An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications

Cited by 8 publications

References 82 publications

A novel approach to the development of neural network architecture based on metaheuristic protis approach

A novel approach to the development of neural network architecture based on metaheuristic protis approach

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Intelligent Control and Digital Twins for Industry 4.0

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