Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.

A composite particle swarm algorithm for global optimization of multimodal functions

During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.

Electromagnetism-Like Mechanism Algorithm with New Charge Formula for Optimization

The electromagnetism-like(EM)algorithm is a meta-heuristic optimization algorithm,which uses a novel searching mechanism called attraction-repulsion between charged particles.It is worth pointing out that there are two potential problems in the calculation of particle charge by the original EM algorithm.One of the problems is that the information utilization rate of the population is not high,and the other problem is the decline of population diversity when the population size is much greater than the dimension of the problem.In contrast,it is more fully to exploit the useful search information based on the proposed new quadratic formula for charge calculation in this paper.Furthermore,the population size was introduced as a new multiplier term to improve the population diversity.In the end,numerical experiments were used to verify the performance of the proposed method,including a comparison with the original EM algorithm and other well-known methods such as artificial bee colony(ABC),and particle swarm optimization(PSO).The results showed the effectiveness of the proposed algorithm.

Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems

This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.

基于用户搜索习惯的“天地图·江苏” POI检索优化机制的设计与实现

详细介绍了"天地图·江苏"的POI检索架构,在此基础上对基于用户搜索习惯的POI检索优化机制进行了阐述,并介绍了具体的实现方法。实践表明,该方法能够使检索结果接近大量用户的搜索期望。

基于衍生搜索政治优化算法解决含可再生能源的多区域经济调度问题

随着可再生能源并入多区域电力系统,其不确定性大大增加了电力系统多区域经济调度的复杂度。如何高效求解含有风力和太阳能的多区域经济调度(multi-areaeconomic dispatch containing wind and solar energy,MAEDWS)问题面临着严峻的挑战。针对现有优化算法在处理MAEDWS问题时存在收敛速度慢和求解精度低等不足,该文提出一种基于衍生搜索的政治优化(derivative search-based political optimizer,DSPO)算法。在政治优化算法的基础上,引入首脑引领策略和衍生搜索机制。前者引领候选解前往更有希望的区域,加快收敛速度;后者在区域获胜者周围衍生邻域解,丰富多样性。该文将DSPO算法和其他6种代表性算法应用于MAEDWS问题,并进行对比分析。收敛曲线和性能指标的结果表明DSPO算法在收敛效率、求解精确度、稳定性方面取得了整体最优。

基于麻雀搜索算法改进的YOLOv7-ECA-SSA模型的车辆检测

为解决复杂背景下小目标车辆检测存在的误检、漏检等现象,创新性提出一种改进YOLOv7网络的目标检测算法。首先,为解决小目标车辆存在次要信息干扰问题,将高效通道注意力(ECA)机制融于YOLOv7模型的主干网络特征层,通过自适应学习来增强目标区域信息权重占比,抑制无关信息;其次,为解决神经网络检测模型训练的超参数随机经验设定性问题,将麻雀搜索算法(SSA)对检测模型训练超参数进行优化,通过内外双循环迭代方式,快速收敛出全局最优学习率,进而得到最优组的权重信息,最终提高小目标车辆检测精度。实验结果表明,基于结构优化、超参数优化的YOLOv7-ECA-SSA检测模型在BDD100K数据集上的检测精度为79.01%,比原始模型提高了5.38%,具备更好的小目标车辆检测性能。

量子计算在地球物理学中的应用

量子计算具有强大的计算能力,被视为一种可能对未来产生颠覆性影响的计算方法,可为多种复杂计算问题的求解提供新思路,目前已在多学科或领域实践与应用。近年来,量子计算逐步应用于地球物理研究,多种量子算法及量子计算机的应用为揭示地球内部构造、探测深部资源等提供了技术支持。量子计算具备高效求解科学问题的能力,在地球物理领域具有巨大的应用潜力。为此,系统分析、阐述量子计算原理并总结了量子算法的发展现状,对其在地球物理数据采集、波场模拟、反问题求解等领域的成果进行归纳、分析;通过建立理论模型并进行反演,验证了量子算法的优越性;最后展望了量子计算未来可能的研究方向。

考虑碳交易机制的海港综合能源系统电-热混合储能优化配置

随着港口电气化进程逐渐加速,单一的港口供能方式正在向多种能源深度融合演变.为响应我国“碳达峰、碳中和”战略目标,进一步提升海港综合能源系统的经济与环境双重效益,提出一种考虑碳交易机制的电-热混合式储能优化配置方案.首先,建立海港综合能源系统模型,并给出计及碳交易市场的交易方案;其次,构建双层优化配置框架,上层优化配置混合式储能容量,下层引入碳交易机制,满足港口综合能源系统低碳经济运行需求;最后,结合网格自适应直接搜索法与自适应混沌粒子群算法优势,利用混合式优化算法对双层优化模型进行求解.以天津港的实际运行数据为例,验证该方法的有效性.算例结果表明,所提方法不仅可以降低系统的投入成本,还能显著减少港区碳排放,从而进一步提升港口经济和环境效益.

鲸鱼优化算法研究与应用进展

鲸鱼优化算法WOA是一种根据概率收敛的新型群体智能优化算法,具有原理简单易实现、参数量少易设置和全局与局部开发分别控制易平衡等特点。系统地分析WOA的基本原理和算法性能影响因素,重点论述现有的算法改进策略和算法混合策略的优点及局限性,并阐述了WOA在支持向量机、人工神经网络、组合优化和复杂函数优化等方面的应用与发展。最后,结合WOA的特点及其应用成果,对WOA的发展方向进行了展望。

基于PWLCM和秃鹰俯冲机制改进的野狗优化算法

针对野狗优化算法(Dingo Optimization Algorithm,DOA)收敛速度偏慢和寻优精度较低等问题,提出一种基于PWLCM和秃鹰机制改进的野狗优化算法(Improved Dingo Optimization Algorithm,IDOA)。首先,使用具有遍历性的分段线性混沌映射(Piecewise Linear Chaotic Map,PWLCM)初始化野狗种群,有效增加野狗种群多样性。其次,在迫害策略中引入秃鹰俯冲机制,加快野狗捕获猎物的速度,加强算法探索局部的能力。最后,在食腐策略引入螺旋搜索因子,增强算法的局部寻优能力,提升算法的寻优速度和求解精度。仿真实验数据、消融实验以及Wilcoxon秩和检验均表明,与其他对比算法相比,提出的IDOA在所有测试函数上有着更佳的寻优速度以及寻优精度;与其他改进的野狗优化算法相比,所提出的IDOA展现出更好的整体性能。

融合多源数据的粤港澳大湾区生态系统健康影响机制研究

从人地耦合视角出发,基于“活力—组织力—恢复力—贡献力”(VORS)模型,综合评价2000—2020年粤港澳大湾区生态系统健康状况。融合人口密度、POI等多源数据,利用最优参数的地理探测器揭示大湾区生态系统健康影响因素的作用机制。结果表明:(1)2000—2020年大湾区平均生态系统健康指数由0.279下降到0.255,湾区中部生态系统健康恶化加剧。(2)生态系统健康的空间集聚特征明显,热点区集中分布在北部肇庆、惠州市;冷点区集中分布在佛山、广州市,且城市交界处的生态系统健康指数变化最为剧烈。(3)人口密度成为影响大湾区生态系统健康的主导因子,其与各类城市设施用地交互作用下对生态系统健康的扰动最为剧烈。公共设施用地、工业设施用地、商业设施用地以及居住用地成为影响大湾区生态系统健康的关键因子,因其对原始自然环境和生态组分的破坏,加剧了生态系统健康的恶化。

Feedback Mechanism-driven Mutation Reptile Search Algorithm for Optimizing Interpolation Developable Surfaces

Curvature lines are special and important curves on surfaces.It is of great significance to construct developable surface interpolated on curvature lines in engineering applications.In this paper,the shape optimization of generalized cubic ball developable surface interpolated on the curvature line is studied by using the improved reptile search algorithm.Firstly,based on the curvature line of generalized cubic ball curve with shape adjustable,this paper gives the construction method of SGC-Ball developable surface interpolated on the curve.Secondly,the feedback mechanism,adaptive parameters and mutation strategy are introduced into the reptile search algorithm,and the Feedback mechanism-driven improved reptile search algorithm effectively improves the solving precision.On IEEE congress on evolutionary computation 2014,2017,2019 and four engineering design problems,the feedback mechanism-driven improved reptile search algorithm is compared with other representative methods,and the result indicates that the solution performance of the feedback mechanism-driven improved reptile search algorithm is competitive.At last,taking the minimum energy as the evaluation index,the shape optimization model of SGC-Ball interpolation developable surface is established.The developable surface with the minimum energy is achieved with the help of the feedback mechanism-driven improved reptile search algorithm,and the comparison experiment verifies the superiority of the feedback mechanism-driven improved reptile search algorithm for the shape optimization problem.

基于改进鲸鱼优化的地面短基线单站无源定位方法

针对粒子群优化(Particle Swarm Optimization,PSO)算法在解决常规地面单站无源定位系统性能受天线布阵形式约束影响时因局部收敛而导致定位精度低的问题,提出了一种基于改进鲸鱼优化算法(Improved Whale Optimization Algorithm,IWOA)的地面短基线单站无源定位法。利用阵元接收到的相位差信息构建代价函数,通过引入测向信息有效缩小算法的搜索范围,对算法的概率因子和收敛因子进行修正,提升算法计算速率,同时对算法的随机搜索方程进行改进并引入重启机制和局部精细搜索方式,在提升算法搜索能力的同时避免算法陷入局部最优。定位仿真结果表明,该算法在性能稳定性上优于PSO,相比于常规地面单站无源定位方法在定位精度上有了明显提升。

加入淘汰机制的改进麻雀搜索算法

传统麻雀算法(sparrow search algorithm,SSA)具有搜索精度高、寻优能力强等优点,但同时也存在早熟收敛、迭代过程中容易陷入局部最优值等问题。针对这些问题,提出了一种加入Tent混沌映射和末位淘汰机制的麻雀搜索算法(sparrow search algorithm with tent,TESSA)。采用2N分段Tent混沌映射初始化种群位置。同时在算法迭代后期引入非线性末位淘汰机制,提高其收敛速度和精度。经过与其他4种群智能算法在6个基准函数上求解性能相比,TESSA的收敛速度、寻优精度、标准误差等性能指标有明显的优势。

融合分解和自适应邻域的多目标离散组合优化算法

为了高效获取现实中大规模多目标优化问题解决方案,实现收敛性、多样性和均匀性的平衡逐渐发展为多目标优化的重要目标之一。针对复杂多目标离散组合优化问题,提出了融合分解和自适应邻域的多目标离散组合优化算法(MOALNS)。该算法在问题分解的基础上为各子问题的寻优进程引入大邻域搜索策略与自适应调整机制,形成一套新型的收敛指导准则突破寻优阻力,进而使各子问题在搜索多维解空间的过程中达到全局搜索与局部搜索的平衡。同时,提出为各子问题配置独立算子积分库可有效地调整各子问题的寻优方向,解决由于目标权重不同而造成的求解方向偏差问题,以此实现更为高效、稳定的多目标优化进程。数值实验表明,提出的新型多目标离散组合优化算法在多组标准测试算例与真实案例中均展现出了在收敛性、多样性、均匀性和延展性等方面的良好性能,相较于其他经典多目标优化算法而言更具优势。

基于矢量空间状态优化的GIS机械故障检测方法

GIS机械故障产生的声音信号蕴含了大量设备运行状态信息,基于声纹识别技术信号分析方法是实现GIS带电检测和故障诊断的有效手段。文中提取了GIS不同运行工况下的LPCC和MFCC特征向量,F比计算结果表明MFCC是一种更具区分度的特征向量。在此基础上,利用了粒子群优化算法的全局搜索能力和进化规划算法的局部调节能力,以粒子群优化为主,引入进化规划算法的变异操作,形成了一种基于矢量空间状态优化的混合优化方法。采用文中提出的矢量量化模型对了110 kV GIS不同运行工况下声音信号进行检测和识别,实验结果表明文中方案得到的码书失真度的均值和方差更小,具有更好的优化性能和稳定性,且同一条件下的识别准确率约为90%~96%,优于经典的LBG迭代算法。

数据驱动的粮食产能组合预测模型

针对长短期记忆网络(LSTM)在粮食产能预测上存在超参数众多、长时序列信息丢失以及难以区分主次特征的问题,提出一种数据驱动的粮食产能组合预测模型.在超参数部分,通过引入动态权重和拉普拉斯变异的秃鹰算法(WLBES)对LSTM进行超参数寻优,避免了手动调参的过程.在预测部分,利用岭回归(RR)对预测结果进行残差修正,弥补LSTM数据丢失的缺陷;同时加入注意力机制,以权重大小区分主次特征,提升粮食产能相关性较大特征的重要性.研究结果表明,WLBES-LSTM-RR组合模型与LSTM模型和WLBES-LSTM模型相比,均方根误差(RMSE)分别下降了75%、19%,相较于其他优化LSTM的组合模型,RMSE大幅下降,该组合模型在粮食产能预测上具有更高的预测精度.

基于Attention-CS-LSTM乙烯裂解炉管温度预测

在乙烯生产过程中,针对乙烯裂解炉管温度难监测的情况,需要对传统的温度测量方法进行改进,通过数据模型下的优化操作可以有效预测乙烯裂解炉出口温度,当出现温度波动时进行干预,提高产品效率和生产安全。文章将改进的布谷鸟算法优化LSTM(CS-LSTM)应用于真实工业数据,并与四种模型进行比较。仿真结果表明,采用Attention-CS-LSTM预测准确率明显提高,且具有良好的稳态准确度,该方法的温度预测准确率为95%。

融合多策略改进的自适应乌鸦搜索算法

针对乌鸦搜索算法存在收敛精度低,寻优速度慢,位置更新存在盲目性等缺陷,提出了一种融合多策略改进的自适应乌鸦搜索算法(Adaptive Crow Search Algorithm with Multiple Strategy Improvements,ACSA)。首先,通过引入一种记忆遗忘机制,不仅提高了算法的收敛速度和精度,而且能够保持种群的多样性。当个体乌鸦发现存在跟随者时,引入了黄金正弦算法进行位置更新,克服了位置更新存在盲目性的不足,从而提高了算法的收敛精度。同时改进了自适应感知概率和飞行步长,以此提高算法的寻优速度和精度。将本算法运用于13个基准测试函数和三杆桁架的设计问题,并同其他的算法进行试验对比,并将实验结果进行Wilcoxon秩和检验以及Friedman检验。实验结果表明,改进后的算法在函数优化以及三杆桁架的工程优化问题上,均能够较好地寻优求解,算法的求解精度和收敛速度均得到了一定的提升。