Publications by authors named "Heming Jia"

Atmospheric turbulence results in the degradation of performance in optical communications, with the scintillation phenomenon significantly influencing the optical link performance. Various physical parameters influence optical scintillation, such as the atmospheric refractive index structure constant, optical transmission distance, turbulence intensity, and anisotropy. In classical theoretical predictions, the anisotropic factor is often assumed to be constant over the long term.

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Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability.

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The prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a unique mode of information exchange. They divide into several small groups to search for food based on special signals and build caves around the food sources.

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The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions.

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Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully preserve data granularity while additionally making use of point dependency.

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This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO.

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This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.

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The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of Aquila birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially in high complex ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search.

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One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems.

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This paper presents an improved teaching-learning-based optimization (TLBO) algorithm for solving optimization problems, called RLTLBO. First, a new learning mode considering the effect of the teacher is presented. Second, the Q-Learning method in reinforcement learning (RL) is introduced to build a switching mechanism between two different learning modes in the learner phase.

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The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own.

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Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed.

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Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM).

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This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems.

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Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population.

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Article Synopsis
  • This paper discusses a new hybrid algorithm designed to enhance multi-level threshold image segmentation, a common technique used for segmenting color images.
  • The method addresses the challenges of finding the optimal threshold by optimizing the calculation of neighborhood mean and median values.
  • Experimental results using the CEC2015 dataset and classic images demonstrate significant improvements in segmentation performance across various metrics, indicating increased efficiency and effectiveness of the proposed approach.
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Multilevel thresholding has important research value in image segmentation and can effectively solve region analysis problems of complex images. In this paper, Otsu and Kapur's entropy are adopted among thresholding segmentation methods. They are used as the objective functions.

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With the rapid development of computer technology, data collection becomes easier, and data object presents more complex. Data analysis method based on machine learning is an important, active, and multi-disciplinarily research field. Support vector machine (SVM) is one of the most powerful and fast classification models.

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Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years.

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In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task.

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