Physics Inspired Optimization Algorithms

Lindfield, George; Penny, J.E.T.

doi:10.1016/b978-0-12-803636-5.00008-6

Cited by 3 publications

(5 citation statements)

References 25 publications

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“…As can be deduced from its name, UEGO is an evolutionary optimization algorithm (Lindfield and Penny, 2017 ), so it works with a population of solutions and simulates their Darwinian evolution to progressively achieve better solutions. However, it belongs to the memetic category of EAs (Moscato, 1989 ; Molina et al, 2011 ).…”

Section: Methodsmentioning

confidence: 99%

“…They do in geometrical progression with the number of levels until the last one, which is linked to the minimum radius specified by the user (see Figure 3A ). This strategy of progressively reducing the mobility at search is known as cooling in the field of Optimization, and it is inspired by the process of annealing metal (Lindfield and Penny, 2017 ). It promotes exploration at the beginning to find the best zones of the search space, avoids premature stagnation, and forces convergence at the end.…”

Section: Methodsmentioning

confidence: 99%

“…Other examples are the Downhill simplex method and L-BFGS-B, which are included in the open-source optimization framework “Optimizer” (Friedrich et al, 2014 ). However, the use of optimizers relying on randomness and nature-inspired principles with generic and minimal mathematical components (Lindfield and Penny, 2017 ) is also very popular among authors (Van Geit et al, 2008 ). For instance, the referred “Optimizer” framework offers Evolutionary algorithms (EAs) and Simulated Annealing too (Friedrich et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

Marín

Cruz

Ortigosa

et al. 2021

Front. Neuroinform.

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This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of single-neuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

Marín

Cruz

Ortigosa

et al. 2021

Front. Neuroinform.

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“…The Firefly Optimization algorithm developed by Lindfield et al [24] mirrors firefly action to draw in different fireflies. The light intensity of the two fireflies is straightforwardly corresponding to their engaging quality, while the distance between them is contrarily proportionate.…”

Section: Wrapper-based Firefly Optimized Feature Selectionmentioning

confidence: 99%

Swarm Optimization and Machine Learning for Android Malware Detection

Jhansi¹,

Varma²,

Chakravarty³

2022

Computers, Materials &Amp; Continua

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Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning (ML) classifiers such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) & Support Vector Machine (SVM). A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The experimental results yielded an accuracy of 98.87% with just seven features out of hundred API call features, i.e., a massive 93% of data optimization.

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“…As the name suggests, in swarm intelligence-based algorithms, some degree of intelligence is present in the algorithm process while finding the optimal solution. However, in physics-based algorithms, the algorithm process is based on specific laws or principles [3,5,6]. The main advantage of physics-based algorithms compared to others is the most straightforwardness.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

et al. 2022

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This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives.

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Physics Inspired Optimization Algorithms

Cited by 3 publications

References 25 publications

On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

Swarm Optimization and Machine Learning for Android Malware Detection

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

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