Toward an Ideal Particle Swarm Optimizer for Multidimensional Functions

Charilogis, Vasileios; Tsoulos, Ioannis G.

doi:10.3390/info13050217

Cited by 13 publications

(7 citation statements)

References 77 publications

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“…This termination rule is based on calculating the variance of the best function value discovered by the optimization method in each iteration. The second termination rule was introduced in the work of Charilogis et al [56] and will be called Similarity in the experiments. In this termination termination technique, at every iteration the difference between the current best value and the previous best value is calculated and the algorithm terminates when this difference is zero for a number of predefined iterations.…”

Section: Termination Check Stepmentioning

confidence: 99%

Improving the Giant Armadillo Optimization Method

Kyrou,

Charilogis,

G.Tsoulos

2024

Preprint

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Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.

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Section: Termination Check Stepmentioning

confidence: 99%

Improving the Giant Armadillo Optimization Method

Kyrou,

Charilogis,

G.Tsoulos

2024

Preprint

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“…Also, the PSO method was used with success in neural network training [53,54]. In this work, an implementation of the PSO method of Charilogis and Tsoulos [55] was used to optimize the problem of Equation (14). The main steps of the utilized method are Termination Check.…”

Section: The Used Pso Variantmentioning

confidence: 99%

Numerical Algorithms in III–V Semiconductor Heterostructures

Tsoulos,

Stavrou

2024

Algorithms

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In the current research, we consider the solution of dispersion relations addressed to solid state physics by using artificial neural networks (ANNs). Most specifically, in a double semiconductor heterostructure, we theoretically investigate the dispersion relations of the interface polariton (IP) modes and describe the reststrahlen frequency bands between the frequencies of the transverse and longitudinal optical phonons. The numerical results obtained by the aforementioned methods are in agreement with the results obtained by the recently published literature. Two methods were used to train the neural network: a hybrid genetic algorithm and a modified version of the well-known particle swarm optimization method.

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“…This inertia calculation was proposed in [104]. With this calculation of the inertia variable, an even better exploration of the research space is achieved with the randomness it introduces, something that was also found in the publication of Charilogis and Tsoulos [105].…”

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confidence: 93%

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

Tsoulos

Tzallas

2023

Applied Sciences

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The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets.

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Toward an Ideal Particle Swarm Optimizer for Multidimensional Functions

Cited by 13 publications

References 77 publications

Improving the Giant Armadillo Optimization Method

Improving the Giant Armadillo Optimization Method

Numerical Algorithms in III–V Semiconductor Heterostructures

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

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