Occurrence and Some Ecological Aspects of the Golden Jackal (<i>Canis aureus</i>Linnaeus, 1758) in the Gaza Strip, Palestine

The Golden Jackal (Canis aureus Linnaeus, 1758), which belongs to the Canidae family, is an opportunist carnivore in the Gaza Strip (365 square kilometers). The current study aims at giving notes on the occurrence and some ecological aspects of the species in the Gaza Strip, Palestine. The study, which lasted 14 years (2007-2020), is descriptive and cumulative in its style. It was based on frequent field visits, direct observations and meetings and discussions with wildlife hunters, farmers and other stakeholders. The findings of the study show that Gazans are familiar with the Golden Jackal to the extent that a Gazan family holds the Arabic name of the animal, which is “Wawi”. The Golden Jackal was sometimes encountered and hunted in the eastern parts of the Gaza Strip, which are characterized by the presence of wilderness areas, intensive agriculture, poultry pens and solid waste landfills. Like other a few mammalian faunas, the adult Golden Jackals enter the Gaza Strip through gaps in or burrows beneath the metal borders separating the Gaza Strip from the rest of the Palestinian Territories and Egypt. Gaza zoos were found to harbor tens of Golden Jackals trapped or hunted by clever wildlife hunters using different means such as wire cages known locally as “maltash” and foothold traps with metal jaws that may cause lesions to the trapped animals. Poisoning and shooting were also common methods used to control the jackals and other carnivores causing harm to agriculture and livestock. The animal was known among the Gazans as an omnivore, feeding on wild and domestic animals in addition to plant materials, garbage and carrions. In conclusion, the study recommends the need to raise ecological awareness to preserve the Golden jackal and to adopt safe control measures for jackals and other carnivores, including the construction of protective fences for agricultural fields and animal pens.

Cytological Features of the Normal Ear Canal of Wild Jackals (<i>Canis aureus</i>) and Domesticated Dogs (<i>C. domesticus</i>)

This is the first reported study in which various cytological and microbial components of the ear canal of wild jackals (Canis aureus) were examined and compared with those of domesticated dogs (C. domesticus). It is proposed that the differences between them might be attributable to domestication. The normal cytology of the jackals' ears includes cerumen, keratinous debris, coccoid bacteria and yeast-like organisms similar to domesticated dogs, but the frequencies of these findings differed significantly between the two species. In the jackals the incidences of ceruminous debris and yeasts were significantly lower (p p = 0.004 respectively), while keratinous debris and coccoid bacteria were significantly higher (p < 0.001). During domestication some changes have probably occurred in the dogs' lifestyle that predisposed them to the growth of yeasts in their ears but less to bacterial growth. It is possible that the higher numbers of bacteria might be a result of environmental contamination, because some of the jackals lived near urban centers and feed on garbage.

Optimization of Chiller Loading Problem Using Improved Golden Jackal Optimization Algorithm Leads to Reduction in Energy Consumption

This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.

An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems

Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.

An Efficient Multilevel Threshold Image Segmentation Method for COVID-19 Imaging Using Q-Learning Based Golden Jackal Optimization

From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.

基于改进金豺算法的短期负荷预测

针对电力负荷序列波动性和预测精度不高的问题,提出一种基于变分模态分解、排列熵和改进金豺算法优化双向长短期记忆网络的预测模型。首先,利用变分模态分解重构原始负荷序列,再采用排列熵理论对分解后的子序列进行熵值重组;然后,利用改进金豺算法对双向长短期记忆网络的参数进行优化,并对每个子序列建立预测模型;最后,组合各模型结果得到最终预测值。实验结果表明,本文模型预测精度更高,与真实值拟合度更好。

融合粒子群的改进金豺算法及应用

为了解决传统金豺算法收敛精度低,搜索速度慢等问题,提出一种融合粒子群算法的改进金豺优化算法(PGJO)。首先,采用Chebyshev混沌映射和精英选择策略结合的方式对种群进行初始化,提高初始解质量;然后,结合粒子群优化算法(PSO)思想,提出一个新的搜索方式。采用动态转换策略,判断PGJO采用原Levy方式搜索还是采用新的搜索方式更新个体位置,以提高算法收敛精度;最后,提出了种群收敛监测策略,帮助算法跳出局部最优。将PGJO与其他智能优化算法经过11个基准测试函数对比实验表明,算法性能均优于其他算法。将PGJO应用于无人机路径规划当中,对比其他算法路径长度下降了3.4%,拐点个数减少了21%,验证了该算法的工程应用能力。

采用金豺优化的LLC谐振变换器自抗扰稳压控制

为解决LLC谐振变换器采用传统PID控制时抑制负载冲击能力差的问题,提出采用金豺优化算法(golden jackal optimization,GJO)的改进线性自抗扰控制方法。该方法基于状态空间表达式,结合降阶模型中的参数信息,改进扩张状态观测器的系数矩阵。利用频域拟合的方法获得线性自抗扰控制器参数初值,避免了繁复的人工调参过程。使用GJO算法对线性自抗扰控制器参数寻优,进一步改善控制器性能。实验结果表明,相较于PSO-PID控制器,GJO-MLADRC控制器有效提高了LLC谐振变换器在系统扰动较大的情况下的动态性能,在负载突增、突降2种工况下,调节时间分别缩短了1.5、1.0 ms,输出电压最大波动量分别减小0.10、0.05 V,改进后的LADRC控制策略有效地抑制了恒压模式下负载波动对输出电压的扰动。

基于变分模态分解和IGJO-SVR的网络舆情预测

网络舆情演化趋势预测在当今的网络环境中对相关政府部门监管舆情发展和维持社会舆论稳定具有十分重要的现实意义。本文针对网络舆情数据的特殊性以及考虑模型预测结果的精确性,使用变分模态分解(VMD)和改进后的金豺优化支持向量回归(IGJO-SVR)构建网络舆情演化趋势预测模型,并以“北溪”事件相关舆情数据为案例进行实证研究,对比结果表明,本文所构建的预测模型精度显著优于其余模型。基于变分模态分解VMD和IGJO-SVR的网络舆情热度预测模型具有较为优秀的预测精度,在实际工作中可为相关政府部门提供切实有效的舆情态势研判和决策帮助。

基于金豺优化PID的直流电机调速控制系统

针对传统直流(DC)有刷电机调速系统适应性不强、抗干扰能力和稳健性差等缺点,设计了基于金豺算法优化比例-积分-微分(PID)的直流有刷电机控制系统.在建立电机数学模型的基础上,将金豺优化(GJO)算法应用于PID参数整定,实现对电机的控制,并分别与传统试凑法、反向传播(BP)-PID算法和麻雀算法(SSA)-PID进行对比.仿真结果表明:所设计的控制算法在调节时间上减少了12 ms,超调量降低了3.689%,受到干扰后的调节时间减少了17 ms,表现出更快的调节转速、更强的抗干扰能力和更好的稳健性,为直流有刷电机调速控制提供了一种有效的方案.

多策略融合改进的金豺优化算法及其在马斯京根模型参数估计中的应用

针对金豺优化算法在解决复杂或高维优化问题时易陷入局部最优、收敛速度慢和计算精度低等不足,提出一种基于多策略融合改进的金豺优化算法(Multi strategy fusion improved Golden Jackal Optimization Algorithm,MGJO)。首先,通过引入混沌映射策略初始化种群代替随机参数,使得算法能够在搜索空间中生成具有良好多样性的初始解,避免初始种群分布偏离最优值;其次,提出一种非线性变化的动态惯性权重使搜索过程更加符合实际情况,有效平衡了算法的全局搜索和局部搜索能力;最后,引入柯西变异的位置更新策略使其充分利用最优个体的引导作用提高种群多样性,以有效探索未知区域避免算法陷入局部最优。为了验证改进的金豺优化算法的寻优精度、收敛性能和稳定性,选择了8个不同特征的基准测试函数进行试验。结果表明,在8个基准测试函数中,改进的金豺优化算法的平均值、标准差、最优值都取得了最优的结果。此外,Wilcoxon符号秩检验的结果表明改进的金豺优化算法在统计学上是显著优越的。通过实例应用表明,基于多策略融合改进的金豺优化算法可以有效地估算出马斯京根模型的参数,优化效果明显优于粒子群优化算法、正弦余弦优化算法和金豺优化算法,进一步验证了多策略融合改进的有效性和改进算法在参数优化中的优越性,为更精确估计非线性马斯京根模型参数提供了一种有效的新方法。

混合策略改进的金豺优化算法

针对金豺优化算法(golden jackal optimization,GJO)在求解复杂优化问题时存在收敛速度慢和易陷入局部最优等不足,提出一种混合策略改进的金豺优化算法(improved golden jackal optimization,IGJO)。在算法的最优解停滞更新时,引入柯西变异策略,增强种群多样性和提升算法陷入局部最优的逃逸能力;提出一种基于权重的决策策略,通过对金豺个体赋予不同权重进行种群位置更新的决策,加快算法的收敛速度。对8个基准测试函数以及部分CEC2017测试函数进行寻优实验,结果表明改进算法具有更好的优化性能和收敛速度;进一步地,将改进算法应用于支持向量回归(support vector regression,SVR)模型的参数优化,并在选取的5个UCI(University of California,Irvine)数据集上进行实验,验证了改进算法的有效性。

基于GJO特征量优选的AO-RF的变压器故障诊断模型

在变压器故障诊断过程中,进行合理的特征优选,将有助于提高诊断模型的诊断精度,为此,文中提出了一种基于金豺优化算法(golden Jackal optimization,GJO)特征量优选与AO-RF的变压器故障诊断模型。首先,采用GJO对构建的21维变压器油中溶解气体特征量进行优选;然后,根据GJO得到的特征优选结果,采用天鹰算法(aquila optimizer,AO)优化随机森林(random forest,RF)的变压器故障诊断模型对变压器故障进行诊断,并与不同特征量、不同故障诊断模型的诊断结果进行了对比。实验结果表明:GJO优选特征量相比21维原始特征、三比值法、无编码比值法以及AO优选特征量的故障诊断准确率可提高1.12%~25.78%,kappa系数可提高0.02~0.24;AO-RF故障诊断模型较RF、SVM、ELM、SSA-RF、WOA-RF、GJO-RF模型的诊断准确率可提高1.84%~15.86%,kappa系数可提高0.02~0.16,验证了所提方法的有效性和准确性。

VMD结合小波包信息熵和GJO-SVM的电机轴承故障诊断

针对电机滚动轴承故障特征难以提取从而导致诊断准确率低的问题,提出了一种基于变分模态分解(Variational Modal Decomposition,VMD)结合小波包信息熵(Wavelet Packet Information Entropy,WPIE)的特征提取方法,并采用金豺优化(Golden Jackal Optimization,GJO)算法优化后的支持向量机(Support Vector Machine,SVM)进行电机滚动轴承的故障诊断。首先,利用VMD将采集到的信号进行分解,依据局部极小包络熵筛选出最优本征模态(Intrinsic Mode Function,IMF)分量;其次,利用小波包将最优IMF分量再分解,并提取信息熵作为特征向量矩阵;最后,采用GJO算法对支持向量机中的惩罚参数和核参数进行寻优选择,建立GJO-SVM故障诊断模型,将特征向量矩阵输入金豺算法优化支持向量机(GJO-SVM)故障诊断模型中进行故障诊断。将VMD结合小波包信息熵特征提取与VMD结合近似熵特征提取进行对比试验,试验结果表明,VMD结合小波包信息熵特征提取精度提高了2.5%,其特征提取更加优越;将金豺算法优化支持向量机(GJO-SVM)与粒子群优化(Porticle Swarm OPtimization,PSO)算法支持向量机(PSO-SVM)、果蝇优化算法(Fruit fly Optimation Algorithm,FOA)支持向量机(FOA-SVM)进行对比试验,试验结果表明,GJO-SVM其平均准确率达到99.16%,较PSO-SVM、FOA-SVM分别提高了2.5%、3.61%。金豺算法优化支持向量机(GJO-SVM)可以更加有效提取并诊断滚动轴承故障。

求解函数优化和特征选择的改进金豺狼优化算法

针对基本金豺狼优化算法(Golden Jackal Optimization,GJO)在解决高维优化问题时存在计算精度低、开发能力弱、容易陷入局部最优的缺点,提出一种改进GJO算法(I-GJO)。在改进算法中,设计一种基于正弦函数的非线性能量因子替代原随机递减能量因子,以平衡算法在搜索过程中的全局探索和局部开发能力。在算法迭代后期引入翻筋斗学习策略,从而扩大群体搜索范围和改善解的精度。为了验证I-GJO算法的有效性,选取6个基准函数优化问题进行数值实验,并与灰狼优化、海鸥优化算法和基本GJO算法比较。结果表明,I-GJO获得较高的精度和较快的收敛速度。最后利用I-GJO算法求解特征选择问题,对16个基准数据集的数值结果显示,改进算法能有效去除冗余特征和提高分类精度。

基于模态分解和时间卷积网络的瓦斯涌出量组合预测

为有效地分析和处理煤矿中产生的瓦斯涌出数据,实现精准、可靠的回采工作面绝对瓦斯涌出量预测,以提前规避瓦斯灾害,提出自适应噪声完整集成经验模态分解对瓦斯涌出量序列进行分解,对分解得到的各分量分别构建时间卷积网络模型。利用IGJO算法对TCN模型的相关超参数进行寻优,建立各分量的预测模型。使用Logistic混沌映射生成金豺种群,引入柯西-高斯变异算子,更新金豺位置并选择最优位置,增强算法搜索能力,避免种群陷入局部最优。将各分量的预测输出值叠加,得到最终的瓦斯涌出量预测值。测试结果表明,CEEMDAN-IGJO-TCN组合预测方法,降低了预测的复杂度同时提高了预测精度。

基于MSIGJO的网联商用车换道轨迹规划方法

针对网联商用车换道安全性、平顺性较低的问题,提出一种基于多策略改进金豺优化算法(multi-strategy improved golden jackal optimization,MSIGJO)的网联商用车换道轨迹规划方法。首先,基于V2X(vehicle to everything)技术获取智能网联商用车周围状态信息,建立商用车换道安全距离模型;其次,引入商用车换道平顺性、经济性和换道效率作为指标,构建多目标协同优化函数;最后,引入动态权重位置更新策略和翻转策略改进金豺优化算法(golden jackal optimization,GJO),进而提出MSIGJO算法,利用MSIGJO算法求解函数得到最优换道轨迹。研究结果表明:该方法在商用车换道过程中横向跟踪精度提升了12.67%,侧向加速度变化率和质心侧偏角变化率分别降低了11.94%和12.65%,有效提升智能网联商用车换道安全性和平顺性,为智能网联商用车换道轨迹规划研究提供参考。

改建房结构安全的三阶提升预警模型

为研究改建房结构安全问题并提升模型预警精度,采用“文本-图像融合”、“信息再生成”和“智能优化模型参数”3个方法建立三阶提升预警模型。首先构建基础预警模型:选取VGG16、ResNet50等4种图像识别模型进行迁移学习,将性能最优者作为基础预警模型;之后进行第1次预警精度提升:收集测试集中对应的文本信息,经独热编码等预处理后与图像信息“融合”,优选随机森林等5种算法以提升预警精度;然后进行第2次精度提升:采用过采样-深度卷积生成对抗网络(SMOTE-DCGAN)策略提高模型对隐患改建房的“捕捉”能力;最后,使用金豺优化算法进行第3次提升。研究结果表明:DenseNet121模型更能抓取到隐患改建房图像特征;改建房结构安全预警模型最优的是支持向量机(SVM),准确率为82.5%;使用SMOTE-DCGAN策略后,表现最佳的SVM和XGBoost,其隐患改建房的召回率分别提升10和5个百分点;金豺优化算法下的“SMOTE-DCGAN-SVM”准确率、召回率、精确率和F 1值再次提升7.0、7.5、10.5和9.1个百分点。研究结果可为相关部门排查改建房安全隐患提供技术支持。

基于改进金豺算法在磷酸铁锂生产排程中的应用

磷酸铁锂作为一种重要的锂离子电池正极材料,在新能源领域市场需求不断上升。针对磷酸铁锂生产过程中的作业车间调度问题,该文提出了一种基于改进金豺算法的排程方法。磷酸铁锂作为一种重要的电池材料,其生产过程需要合理的调度安排以提高生产效率和产品质量。提出的改进金豺算法通过引入改进策略,能够更好地解决作业车间调度问题。与传统的启发式算法相比,改进金豺算法具有更强的全局搜索能力和高效的搜索速度。此外,该算法还具有灵活的参数调整性,可以根据具体问题的特点和约束条件进行优化,以获得更好的性能和效果。为了验证改进金豺算法在磷酸铁锂生产排程中的应用效果,设计了一系列的仿真实验。通过与传统启发式排程算法的对比表明,改进金豺算法能够在复杂的约束条件下找到更优的调度方案,提高生产效率。具体来说,该算法能够更快地找到全局最优解,减少了调度时间和成本,并提高了交货周期的准确性。