RIME: A physics-based optimization Su, Hang; Zhao, Dong; Heidari, Ali Asghar ...
Neurocomputing (Amsterdam),
05/2023, Letnik:
532
Journal Article
Recenzirano
•A novel global optimization algorithm, rime optimization algorithm (RIME), is proposed.•RIME simulates the growth and crossover behavior of the rime-particle population.•The performance of RIME is ...adequately discussed based on benchmark functions.•RIME has better convergence accuracy and speed compared to other methods.•RIME is applied to solving five practical engineering optimization problems.
This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME. The RIME algorithm implements the exploration and exploitation behaviors in the optimization methods by simulating the soft-rime and hard-rime growth process of rime-ice and constructing a soft-rime search strategy and a hard-rime puncture mechanism. Meanwhile, the greedy selection mechanism in the algorithm is improved, and the population is updated in the stage of selecting the optimal solution to enhance the exploitation capability of the RIME. In the experimental, this paper conducts qualitative analysis experiments on the RIME to clarify the characteristics of the algorithm in the process of finding the optimal solution. The performance of RIME is then tested on a total of 42 functions in the classic IEEE CEC2017 and the latest IEEE CEC2022 test sets. The proposed algorithm is compared with 10 well-established algorithms and 10 latest improved algorithms to verify its performance advantage. In addition, this paper designs experiments for the parametric analysis of RIME to discuss the potential of the algorithm in running different parameters and handling different problems. Finally, this paper applies RIME to five practical engineering problems to verify its effectiveness and superiority in real-world problems. The statistical and comparison results show that the RIME is a strong and competitive algorithm. The source code of the RIME11Source codes of RIME algorithm are also available at: https://codeocean.com/capsule/4472937/tree and https://www.mathworks.com/matlabcentral/fileexchange/124610-rime-a-physics-based-optimization and https://github.com/aliasgharheidaricom/RIME-A-physics-based-optimization. algorithm and associated files are publicly accessible at https://aliasgharheidari.com/RIME.html.
The RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. ...In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.
Under the goals of carbon neutrality and peak carbon emissions, photovoltaic (PV) power generation is widely valued for its clean and green characteristics. However, the uncertainty and randomness of ...PV power pose challenges to energy management. Therefore, this study proposed a novel bimodal feature fusion network-based deep learning model with an intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. First, a threshold-guided iNNE-based outlier detection and repair method is designed for preprocessing PV data. Second, a bimodal feature fusion network was proposed to extract global and local features from PV power sequences, and the environmental factors-based rime optimization algorithm with growth mutation strategy and humidity perception mechanism was devised to optimize model's hyperparameters. Additionally, a photovoltaic power interval prediction model with a volatility segmentation strategy was introduced. Finally, the effectiveness of the proposed model, algorithm, and strategies was validated using measured datasets. The results demonstrated that under various weather conditions, the proposed model achieved point prediction evaluation metrics with an R2 exceeding 98 % and a prediction interval evaluation metric with a Prediction Interval Coverage Probability of 85.07 %. The obtained outcomes contribute to providing a basis for decision-making in the scientific scheduling and management of PV power systems.
•The proposal of a bimodal feature fusion network-based deep learning model.•The proposal of a threshold-guided iNNE-based outlier detection and repair method.•The proposal of EFRIME with growth mutation and humidity perception mechanism.•The introduction of the TAP-MSIP model with the VSS strategy.
When acquiring language, young children may use acoustic spectro-temporal patterns in speech to derive phonological units in spoken language (e.g., prosodic stress patterns, syllables, phonemes). ...Children appear to learn acoustic-phonological mappings rapidly, without direct instruction, yet the underlying developmental mechanisms remain unclear. Across different languages, a relationship between amplitude envelope sensitivity and phonological development has been found, suggesting that children may make use of amplitude modulation (AM) patterns within the envelope to develop a phonological system. Here we present the Spectral Amplitude Modulation Phase Hierarchy (S-AMPH) model, a set of algorithms for deriving the dominant AM patterns in child-directed speech (CDS). Using Principal Components Analysis, we show that rhythmic CDS contains an AM hierarchy comprising 3 core modulation timescales. These timescales correspond to key phonological units: prosodic stress (Stress AM, ~2 Hz), syllables (Syllable AM, ~5 Hz) and onset-rime units (Phoneme AM, ~20 Hz). We argue that these AM patterns could in principle be used by naïve listeners to compute acoustic-phonological mappings without lexical knowledge. We then demonstrate that the modulation statistics within this AM hierarchy indeed parse the speech signal into a primitive hierarchically-organised phonological system comprising stress feet (proto-words), syllables and onset-rime units. We apply the S-AMPH model to two other CDS corpora, one spontaneous and one deliberately-timed. The model accurately identified 72-82% (freely-read CDS) and 90-98% (rhythmically-regular CDS) stress patterns, syllables and onset-rime units. This in-principle demonstration that primitive phonology can be extracted from speech AMs is termed Acoustic-Emergent Phonology (AEP) theory. AEP theory provides a set of methods for examining how early phonological development is shaped by the temporal modulation structure of speech across languages. The S-AMPH model reveals a crucial developmental role for stress feet (AMs ~2 Hz). Stress feet underpin different linguistic rhythm typologies, and speech rhythm underpins language acquisition by infants in all languages.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Secondary ice production (SIP) plays a key role in the formation of ice particles in tropospheric clouds. Future improvement of the accuracy of weather prediction and climate models relies on a ...proper description of SIP in numerical simulations. For now, laboratory studies remain a primary tool for developing physically based parameterizations for cloud modeling. Over the past 7 decades, six different SIP-identifying mechanisms have emerged: (1) shattering during droplet freezing, (2) the rime-splintering (Hallett–Mossop) process, (3) fragmentation due to ice–ice collision, (4) ice particle fragmentation due to thermal shock, (5) fragmentation of sublimating ice, and (6) activation of ice-nucleating particles in transient supersaturation around freezing drops. This work presents a critical review of the laboratory studies related to secondary ice production. While some of the six mechanisms have received little research attention, for others contradictory results have been obtained by different research groups. Unfortunately, despite vast investigative efforts, the lack of consistency and the gaps in the accumulated knowledge hinder the development of quantitative descriptions of any of the six SIP mechanisms. The present work aims to identify gaps in our knowledge of SIP as well as to stimulate further laboratory studies focused on obtaining a quantitative description of efficiencies for each SIP mechanism.
Radar sounders (RSs) are low-frequency instruments that profile the shallow subsurface of planetary targets to obtain valuable scientific information. The prediction of the RS performance and the ...interpretation of the target properties from the RS data are challenging due to the complex electromagnetic interaction among many acquisition variables. Simulation of RS data can address this issue by modeling the complex interaction and producing simulated radargrams representing the acquisition scenario. In this article, we present an approach to generate databases of geoelectrical models and simulated radargrams corresponding to combinations of: 1) target geoelectrical hypotheses; 2) RS parameters; and 3) acquisition geometry configurations. The proposed approach exploits this database for: 1) predicting the detection performance and sensitivity of the RS and 2) understanding the interpretability of the underlying hypotheses. In order to identify hypothesis combinations that can be unambiguously inverted from the radargrams, we analyze the similarity between pairs of geoelectrical models and between the simulated radargrams, and the statistical distance between radargram features. The approach is demonstrated for the case of Radar for Icy Moons Exploration (RIME), using three selected targets on the Jovian moon Ganymede, with three different simulation techniques. The results are very promising and reveal the effectiveness of the proposed approach in extracting valuable information regarding: 1) the target detection performance of RIME; 2) the sensitivity to the dielectric contrast; 3) the separability of radargram features; and 4) the identification of hypothesis combinations producing significantly different radar response, and thus invertible.
Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in ...medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.
•WDRIME (Whale search prey mechanism and Random mutation strategy-based RIME) is proposed.•WDRIME achieves a great improvement in solution quality and convergence problems.•The performance of WDRIME is verified by comparing it with other excellent algorithms.•WDRIME is applied to multilevel lung cancer image segmentation based on 2D histogram.
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