Competitive Performance Research Articles

Deep Ensemble Learning using Transfer Learning for Food Classification

Aim: Deep learning models, such as deep convolutional neural networks (CNNs), have undergone extensive scrutiny in the context of food classification because of their exceptional feature extraction capabilities. Background: Similarly, ensemble-based learning approaches have exhibited great potential for achieving effective supervised classification. Objective: We suggest an innovative approach to improve the effectiveness of deep learningbased food classification. Method: Our proposal involves a novel deep learning ensemble framework that draws inspiration from the fusion of deep learning models with ensemble learning based on random subspaces. The random subspaces play a role in diversifying the ensemble system in a straightforward but impactful way. Moreover, to enhance the classification accuracy even more, we explore transfer learning, employing the migration of acquired weights from a single classifier to another (namely, CNNs). This approach expedites the process of learning. Result: Results from experiments conducted using well-established food datasets illustrate that the suggested deep learning ensemble system delivers competitive performance compared to state-of-the-art techniques, as evidenced by its classification accuracy. Conclusion: The amalgamation of deep learning and ensemble learning holds substantial promise for dependable food categorization.

Low-Light Image Enhancement via Dual Information-Based Networks

Recently, deep-learning-based low-light image enhancement (LLIE) methods have made great progress. Benefiting from elaborately designed model architectures, these methods enjoy considerable performance gains. However, the generalizability of these methods may be weak, and they may suffer from the overfitting risk in the case of insufficient data as a result. At the same time, their complex model design brings serious computational burdens. To further improve performance, we exploit dual information, including spatial and channel (contextual) information, in the high-dimensional feature space. Specifically, we introduce customized spatial and channel blocks according to the feature difference of different layers. In shallow layers, the feature resolution is close to that of the original input image, and the spatial information is well preserved. Therefore, the spatial restoration block is designed for leveraging such precise spatial information to achieve better spatial restoration, e.g., revealing the textures and suppressing the noise in the dark. In deep layers, the features contain abundant contextual information, which is distributed in various channels. Hence, the channel interaction block is incorporated for better feature interaction, resulting in stronger model representation capability. Combining the U-Net-like model with the customized spatial and channel blocks makes up our method, which effectively utilizes dual information for image enhancement. Through extensive experiments, we demonstrate that our method, despite its simplicity of design, can provide advanced or competitive performance compared to some state-of-the-art deep learning- based methods.

Combining Signals for EEG-Free Arousal Detection during Home Sleep Testing: A Retrospective Study.

Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. Objectives: The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. Methods: We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. Results: The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. Conclusions: This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection.

The feasibility of using epoxy resin reinforced with sugar cane bagasse residue for a high performance coatings: a cost of goods sold and blue ocean analysis

The natural and biodegradable characteristics of sugarcane bagasse (SCB) make it a promising ingredient for composite materials. Researchers have explored ecological and low-cost materials, identifying SCB as a green option with low pollution levels. This study investigates the strategic position for SCB and epoxy composite when applied as high-performance coatings. Epoxy resin is valued for its adhesion, strength and durability. Integrating sustainable, cost-effective reinforcements is vital for enhancing these properties and reducing environmental impact. SCB, a sugar industry waste, has favorable characteristics like fibrous structure and polymer compatibility. In this research, SCB residue was processed and incorporated into epoxy resin. Both analisys the Cost of Goods Solds (COGS) and a Blue Ocean Strategy analysis identified new market opportunities, positioning this composite as a sustainable, high-performance solution in coatings. It´s relevant to point that SCB could offer substantial improves on corrosion resistance, offering a sustainable alternative with competitive performance and cost.

Total Polyphenol Content and Antioxidant Activity of Leaves and Fine Roots as Indicators of Drought Resistance in the Native Quercus robur and Alien Quercus rubra

Research Highlights: Environmental abiotic stressors generate secondary stresses in plants, such as osmotic and oxidative stresses, which negatively influence their normal growth, development, and metabolism. Research about other non-enzymatic components with antioxidant capacity has recently focused on polyphenols. However, their role as indicators of drought and shade tolerance in woody species leaves and roots has been poorly explored or was limited to leaves only. Background and Objectives: Under a scenario of increasing drought, understanding the seedling responses in terms of total polyphenols and their antioxidant activity, in particular at the fine root system level, may help to elucidate the native–alien species interaction. Materials and Methods: At the beginning of July, 5-month-old native Quercus robur and alien Quercus rubra seedlings were transferred indoors to the growth chamber and subjected to progressive soil drying for 21 days. Results: The decrease in soil water content was more pronounced for Q. robur (9%) than for Q. rubra (34% of field capacity). Leaf water potential significantly decreased over time in Q. robur but did not differ from the control in Q. rubra. The total polyphenol concentration in Q. robur was markedly lower in the leaves and significantly higher in the fine roots than in Q. rubra. For the leaves, both species showed markedly higher values if well-watered, and the values significantly decreased in response to drought only in Q. rubra. In contrast, the fine root values for both species were markedly higher if droughted and decreased significantly in time only in Q. robur. Differently from the polyphenol concentration, the antioxidant capacity of Q. rubra was always higher in both the leaves and fine roots. Conclusions: The higher antioxidant activity of the alien species Q. rubra revealed by this work, combined with its isohydric behaviour, could further shed some light on our understanding of its competitive performance at the seedling stage against the native Q. robur.

Hybrid multiple instance learning network for weakly supervised medical image classification and localization

Weakly supervised medical image analysis is of great significance for computer-aided diagnosis due to the difficulty in obtaining accurately labeled medical data. In this paper, we proposed a new Multi-instance Learning (MIL) framework called HybridMIL integrating CNN Convolutional Neural Networks (CNN) and Broad Learning Systems (BLS). Our HybridMIL can overcome several challenging issues over existing MIL methods based on either CNN or BLS alone: (i) Multiple levels (i.e., different resolutions) of feature information can be simultaneously extracted through a newly proposed instance-level feature enhancement (IFE) module; (ii) Global-level semantic information contained in the deep layers can be better represented under the global-level semantic enhancement (GSE) module; (iii) Hybrid feature fusion (HFF) module is newly designed to effectively fuse and align the multi-level outputs of IFE and global-level semantic information of GSE for subsequent classification and localization tasks. The proposed HybridMIL is evaluated on various public medical and MIL benchmark datasets. The results indicate that HybridMIL surpasses other recent MIL models in terms of classification and localization performance by up to 8.5% and 9.0%, respectively. Lastly, we demonstrate the highly competitive performance of HybridMIL in general MIL problems, going beyond weakly supervised medical image analysis.

Optimizing Spatial Complexity of the Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec)

In this article, we propose an optimized version of the Hard Decision Decoder based on Hash and Syndrome Decoding (HSDec) decoder, named Reduced Memory Space of HSDec (RMS-HSDec), which uses less memory space. In this article, we aim to reduce the spatial complexity of the HSDec decoding algorithm while preserving its error correction capabilities. Our methodology involves allocating only the essential memory space for correctable error patterns and optimizing the hashing mechanism to effectively handle potential collisions. While maintaining the integrity of error correction, this new method guarantees memory reduction rates of over 96 % for the BCH(63, 39, 9) code and over 84 % for the QR(47, 24, 11) code compared to HSDec. Simulations were conducted to evaluate the performance of RMS-HSDec on various BCH and QR codes over AWGN and Rayleigh channels. The results demonstrated significant memory reduction rates and coding gains ranging from 0.8 dB to 2.8 dB over the AWGN channel and from 14 dB to 32 dB over the Rayleigh channel, confirming the robustness of the algorithm under different channel conditions. Comparative analyses showed that RMS-HSDec maintains competitive performance with existing decoders while offering effective error correction. These findings confirm the robustness of the RMS-HSDec algorithm under different channel conditions. Overall, the proposed decoder proves to be an effective solution, optimizing memory usage without compromising error correction capabilities, making it ideal for high-density data applications and environments with limited memory resources.

Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection

The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

Knapsack air assisted electrostatic sprayer for agricultural formulations

An experimental prototype of a backpack-type rechargeable battery-powered air-assisted electrostatic sprayer was designed and tested for performance while preserving techno-commercial competence and affordability for India's marginal and small farmer communities. The designed prototype electrostatic sprayer achieved good levels of charge induction on the spray particles at various electrode potentials (1 to 12 kV). At a charging electrode potential of 9 kV and a nozzle discharge rate of 2 mL s−1, electrostatically charged spray had a maximum charge to mass ratio (CMR) of 1.79 mC kg−1. The Electric Ducted Fan (EDF) used for high velocity air assistance was capable of transporting charged spray droplets onto distant targets such as orchard trees and field crops with a spray throw of up to 5 m. The high-pressure atomization method produced fine droplets within a Volume Median Diameter (VMD) range of 90 to 100 µm, resulting in better charge induction and wrap-around effect. As the prototype features fewer moving mechanical components, reduced total noise and vibrations, it promises operator comfort in the long run while requiring less system maintenance. Environmental contamination can be minimized as the large quantity of harmful chemicals be prevented from drifting into the soil and nearby waterbodies. The competitive performance and lower investment could encourage majority of the Indian famers to upgrade with the developed air assisted electrostatic spraying system contributing to agro-socio-economic welfare.

A Data-driven Race Strategy Tool for Olympic Sailing Competitions

_ Venue-specific training has, over the years, proven to be a key asset for sailing teams performing at major championships and at the Olympics. A comprehensive understanding of the environmental features and possible weather scenarios in an outdoor sport like sailing can provide athletes with a significant strategic advantage. At the same time, GPS tracking is becoming a readily available technology that athletes use both in training and during races to analyse their own and their competitor's performance. This work couples environmental and meteorological data with GPS tracks for Olympic sailing classes, linking weather features with strategic decisions on the race track. We propose a greedy algorithm to search for an optimal route based on weather forecasts to present the best strategy prior to the race. Our results show the potential of this approach to provide valuable decision support for athletes in Olympic sailing competitions, demonstrated for the 470 Olympic class. Keywords Olympic sailing; path planning; data-driven methods; meteorology; A* algorithm

Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics

Physical reservoir computing leverages the dynamical properties of complex physical systems to process information efficiently, significantly reducing training efforts and energy consumption. Magnetic skyrmions, topological spin textures, are promising candidates for reservoir computing systems due to their enhanced stability, non-linear interactions and low-power manipulation. Traditional spin-based reservoir computing has been limited to quasi-static detection or real-world data must be rescaled to the intrinsic timescale of the reservoir. We address this challenge by time-multiplexed skyrmion reservoir computing, that allows for aligning the reservoir’s intrinsic timescales to real-world temporal patterns. Using millisecond-scale hand gestures recorded with Range-Doppler radar, we feed voltage excitations directly into our device and detect the skyrmion trajectory evolution. This method scales down to the nanometer level and demonstrates competitive or superior performance compared to energy-intensive software-based neural networks. Our hardware approach’s key advantage is its ability to integrate sensor data in real-time without temporal rescaling, enabling numerous applications.

Porphyrin-based schiff-base and aminal nitrogen-rich porous organic polymers for capture of SO2 and CO2

Due to its serious hazards to human health and the environment, the deep removal of sulfur dioxide (SO2) has been of great significance. Thus, it is critical to develop high efficient SO2 capture and sequestration materials in gas purification process. Herein, we reported two novel prophyrin-based nitrogen-rich porous organic polymers (POPs), PrPOA-BP and PrPSN-BP, constructed through the simple catalyst-free condensation reaction. Owing to the strong affinity to SO2 from the conjugate-electron macrocycles structure of prophyrin and nitrogen-rich porous networks, also the high porous structure, these two POPs demonstrated excellent SO2 capture and separation performance with the adsorption uptakes up to 18.2 mmol g−1 (273 K, 1 bar), 13.3 mmol g−1 (298 K, 1 bar), 1.68 mmol g−1 (298 K, 0.01 bar). This very competitive performance has far exceeded most of the prior reported nanoporous materials. Meanwhile, the IAST selectivities of SO2/CO2 (10/90, v/v) could reach 107.8 and 72.0 at 273 and 298 K, 1 bar. This study represents a new type prophyrin-based POPs materials and confirms the intrinsic potential for high efficiency SO2 capture and sequestration.

Unleashing the Power of Contrastive Learning for Zero-Shot Video Summarization.

Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Past efforts have invariantly involved training summarization models with annotated summaries or heuristic objectives. In this work, we reveal that features pre-trained on image-level tasks contain rich semantic information that can be readily leveraged to quantify frame-level importance for zero-shot video summarization. Leveraging pre-trained features and contrastive learning, we propose three metrics featuring a desirable keyframe: local dissimilarity, global consistency, and uniqueness. We show that the metrics can well-capture the diversity and representativeness of frames commonly used for the unsupervised generation of video summaries, demonstrating competitive or better performance compared to past methods when no training is needed. We further propose a contrastive learning-based pre-training strategy on unlabeled videos to enhance the quality of the proposed metrics and, thus, improve the evaluated performance on the public benchmarks TVSum and SumMe.

Pre and Post Competition Psychology and its Relationship with Competition Results in Lawn Bowl Disabilities Athletes in Asian Paragames 2022

This research purpose was investigated the psychology of disabilities athletes before and after the competition and the relationship with the results of the lawn bowl competition at the Asian Paragames 2022. The research method used is a survey. The 12 blind athletes (6 men and 6 women) participating in this study were aged 37.67±11.07 years, weight 68.58±10.13 kg, height 165.00±7.07 cm, and had 3.19±1.06 years of experience playing lawn bowls. Data analysis was carried out using paired t-test and regression analysis with significance at p<0.05. The results of the paired t-test revealed significant differences before and after the competition in athletic identity (p = 0.000), anxiety (p = 0.000), self-efficacy in sports (p = 0.000), expectations of success (p = 0.000), and mood profile (p = 0.000). The results of the regression analysis revealed that the regression model which included anxiety, athletic identity, expectations of success, self-efficacy in sports, and mood profile was significant for competition outcomes (F = 101.75, p = 0.000), with 99.92% of competition outcomes explained by variations in psychological variables (R square = 0.992). Based on the relationship, only anxiety shown a negative relationship to the competition results with a regression coefficient of -3.102 for anxiety-state and -0.723 for anxiety-trait. In conclusion, improvements are needed in athletic identity, self-efficacy, expectancy of success, and a positive mood profile to reduce the anxiety level before competition then improve competition performance better in lawn bowl athletes.

Category-sensitive semi-supervised semantic segmentation framework for land-use/land-cover mapping with optical remote sensing images

High-quality land-use/land-cover mapping with optical remote sensing images yet presents significant work. Even though fully convolutional semantic segmentation models have recently contributed to popular solutions, the lack of annotation data may lead to severe degradations in their inference performance. Besides, the category confusion in high-resolution representations will further exacerbate the adverse effects. In this paper, we propose a category-sensitive semi-supervised semantic segmentation framework to address these weaknesses by employing massive unlabeled data. With the perturbations from adopted hybrid data augmentation structures, we first focus on the output space and execute regularization constraints to learn category-specific discriminative features. It is formulated with a consistency self-training procedure where a dynamic class-balanced threshold selection scheme is proposed to provide high-confident pseudo supervisions for each category. In addition, we introduce pixel-wise contrastive learning on the common embedding space from both labeled and unlabeled data domains to further facilitate the semantic dependencies among category features, in which the reliable labels are leveraged as guidance for pixel sample selection. We verify the proposed framework on two benchmark land-use/land-cover datasets, and the experimental results demonstrate its competitive performance to other state-of-the-art semi-supervised methods.

Predominant technical actions used in the European judo championship

Judo is an Olympic combat sport that is divided into male and female weight categories. Each weight category implies significant differences in technical and strategic dimensions, as well as physiological, performance, and body composition among competitors. This study aimed to determine the technical actions predominance used by judokas in the European Judo Championship 2021. We observed 398 judo combats, in the various weight categories male and female, disputed in the European Judo Championship 2021. It was used an observation system in accordance with the classification of judo techniques proposed by Kodokan. An observation system was used according to the Kodokan classification of judo techniques. In Nage-Waza combat, there was a predominance leg technique (Ashi-Waza), arm technique (Te-Waza), hip technique (Koshi-Waza), side sacrifice techniques (Yoko-Sutemi-Waza) and frontal sacrifice techniques (Ma-Sutemi-Waza). When we focused our observation on the effectiveness of applied techniques, we observed more techniques scored with Ippon than Wazari. We can also observe that Shido was the significantly more recurrent punishment during male combats. Keywords: Judo, Kodokan, European Championship, Combat performance, Competitive performance analysis.

Is isometric mid-thigh pull associated with the competitive performance of striking high-level athletes?

Background: The isometric mid-thigh pull test (IMTP) is commonly used in sports performance assessment, but it remains underutilized among combat sports athletes. Aims: to evaluate whether Pan-American medalists (PM) showed differences when compared with other athletes (NM) when performing the IMTP. Methods: a total of 72 Karate [n=35 (♀12)] and Taekwondo [n=37 (♀15)] athletes were measured (22.4±3.7 yrs.; 67.4±11.5 Kg; 1.7±0.1 m; 22.1±2.8 kg/m2). From the total, 32 athletes were PM of their specific combat sport (♀11). All participants performed the IMTP 60-90 days before the competition. The following variables were measured: a) Impulse Net at 50; 100; 150 and 200 ms (N*s); b) absolute (N) and relative peak force (PF; N/Kg) and; c) Rating of force development (RFD) at 50; 100; 150 and 200 ms (N/s). Results: PM showed a better performance on absolute (2,398.5±623.1 vs. 2,096.2±559.3 N; p=0.034) and relative PF (34.6±5.0 vs. 31.2±5.1 N/Kg; p=0.006); RFD at 150 (5,273.4±2,670.8 vs. 3,963.4±1,904.2 N/s; p=0.0018) and 200ms (4,870.3±2,184.6 vs. 4,022.1±1,574.8 N/s; p=0.05). Relative PF (Τ=0.249; p=0.011) and RDF at 150 ms (Τ=0.208; p=0.033) showed a positive and significative correlation with the competition result. Conclusion: PM showed a high PF and RFD at 150 and 200 ms. Furthermore, the competition result was correlated with relative PF and RDF at 150 ms. Based on our findings, we recommend that coaches incorporate the IMTP into their evaluation routine for striking athletes. Keywords: martial arts, athletic performance, elite athlete, task performance and analysis.

Weakly-supervised Depth Estimation and Image Deblurring via Dual-Pixel Sensors.

Dual-pixel (DP) imaging sensors are getting more popularly adopted by modern cameras. A DP camera captures a pair of images in a single snapshot by splitting each pixel in half. Several previous studies show how to recover depth information by treating the DP pair as an approximate stereo pair. However, dual-pixel disparity occurs only in image regions with defocus blur which is unlike classic stereo disparity. Heavy defocus blur in DP pairs affects the performance of depth estimation approaches based on matching. Therefore, we treat the blur removal and the depth estimation as a joint problem. We investigate the formation of the DP pair, which links the blur and depth information, rather than blindly removing the blur effect. We propose a mathematical DP model that can improve depth estimation by the blur. This exploration motivated us to propose our previous work, an end-to-end DDDNet (DP-based Depth and Deblur Network), which jointly estimates depth and restores the image in a supervised fashion. However, collecting the ground-truth (GT) depth map for the DP pair is challenging and limits the depth estimation potential of the DP sensor. Therefore, we propose an extension of the DDDNet, called WDDNet (Weakly-supervised Depth and Deblur Network), which includes an efficient reblur solver that does not require GT depth maps for training. To achieve this, we convert all-in-focus images into supervisory signals for unsupervised depth estimation in our WDDNet. We jointly estimate an all-in-focus image and a disparity map, then use a Reblur and Fstack module to regularize the disparity estimation and image restoration. We conducted extensive experiments on synthetic and real data to demonstrate the competitive performance of our method when compared to state-of-the-art (SOTA) supervised approaches. Index Terms-Dual-pixel Sensor, Weakly-supervised, Depth Estimation, Deblur and Reblu.

Toward Multilabel Classification for Multiple Disease Prediction Using Gut Microbiota Profiles.

Advancements in high-throughput technologies have yielded large-scale human gut microbiota profiles, sparking considerable interest in exploring the relationship between the gut microbiome and complex human diseases. Through extracting and integrating knowledge from complex microbiome data, existing machine learning (ML)-based studies have demonstrated their effectiveness in the precise identification of high-risk individuals. However, these approaches struggle to address the heterogeneity and sparsity of microbial features and explore the intrinsic relatedness among human diseases. In this work, we reframe human gut microbiome-based disease detection as a multilabel classification (MLC) problem and integrate a range of innovative techniques within the proposed MLC framework, aptly named GutMLC. Specifically, the entity semantic similarity as priori knowledge is incorporated into multilabel feature selection and loss functions by capturing the shared attributes and inherent associations among diseases and microbes. To tackle the issue of label imbalance, both within and between labels, we adapt the focal loss (FL) function for MLC using debiased inverse weighting. Extensive experiment results consistently demonstrate the competitive performance of GutMLC in comparison with commonly used MLC and single-label classification (SLC) algorithms. This work seeks to unlock the potential of gut microbiota as robust biomarkers for multiple disease prediction.

Learning to search promising regions by space partitioning for evolutionary methods

To alleviate the premature, many evolutionary computation algorithms try to balance the exploitation and exploration by controlling the population diversity. However, randomly diversifying a population cannot always guarantee that an algorithm exploits or explores promising regions. To address this issue, a general framework is proposed in this paper for learning promising regions that are made up of subspaces to guide where to exploit and explore by two reinforcement learning systems. The learning mechanism is as follows: (1) To enhance the efficiency of exploitation, an exploitative reinforcement learning system is constructed to estimate the exploitative potential values of subspaces. Accordingly, basins of attraction are approximated by clustering subspaces and historical solutions are selected within the same basin of attraction to generate new solutions. (2) To efficiently explore the solution space, an explorative reinforcement learning system is established to estimate the explorative potential values of subspaces. Accordingly, algorithms are guided to explore subspaces with higher explorative potential values, promoting the discovery of unexploited promising basins of attraction. The framework is implemented into three conventional evolutionary algorithms, and the mechanism and effectiveness of the implemented algorithms are investigated by comprehensive experimental studies. The experimental results show that the proposed algorithms have competitive performances over the other twelve popular evolutionary algorithms.