Empirical Review of Standard Benchmark Functions Using Evolutionary Global Optimization

We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization. It turns out that some of them are not very useful as challenging test functions, since they neither allow for a discrimination between different variants of genetic operators nor exhibit a dimensionality scaling resembling that of real-world problems, for example that of global structure optimization of atomic and molecular clusters. The latter properties seem to be simulated better by two other types of benchmark functions. One type is designed to be deceptive, exemplified here by Lunacek’s function. The other type offers additional advantages of markedly increased complexity and of broad tunability in search space characteristics. For the latter type, we use an implementation based on randomly distributed Gaussians. We advocate the use of the latter types of test functions for algorithm development and benchmarking.

生存进化阶段性搜索微粒群算法及其可靠性冗余分配优化应用

为高效解决含有异质冗余的多态系统(MSS)可靠性优化问题,并弥补微粒群优化(PSO)算法易早熟收敛的不足,从作用力方式和种群拓扑结构两方面对算法进行改进。改进PSO算法中单一的作用力方式,设置前后两个搜索阶段,对应两个搜索阶段分别构造平衡引斥力方式和双层引力(个体和全局最优解引力、中间适应度微粒引力)方式,提出阶段性搜索微粒群(SPSO)算法;利用生物个体“择友而交”和优胜劣汰的生存体系构建生存进化(SE)拓扑结构,以结构演化和算法进化并行方式将该拓扑结构融入SPSO算法,提出生存进化阶段性搜索微粒群(SPSO-SE)算法,进一步提升算法的优化性能;利用Benchmark函数对所提算法与PSO的改进算法进行测试对比,结果表明,所提SPSO-SE算法具有更好的寻优能力。采用SPSO-SE算法对串-并联和桥式结构的多态系统的可靠性冗余分配问题进行优化,得到的系统结构费用更低、可靠度更高。

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

Operations Improvement Function and Organisational Adaptability of Petroleum Tank Farms in South-South, Nigeria

The study examined the nexus between operations improvement function (dimensioned by contingency planning, benchmarking and continuous improvement processes) and organisational adaptability of Petroleum tank farms in South-South, Nigeria. The contingency theory and the theory of routine dynamics underpinned the study, and positivism was the underlying philosophy. The study adopted the cross-sectional survey through the use of questionnaire. 820 middle and top-level managers constituted the elements of the population, and the Krejcie & Morgan’s formula was used to determine the sample size of 262 respondents. Structural Equation Modeling was deployed to test the hypotheses at a 0.05 significance level. The results showed that contingency planning;benchmarking and continuous improvement processes all have a significant positive relationship with organisational adaptability of Petroleum tank farms in South-South, Nigeria. The study concludes that Petroleum tank farms’ operations should focus on the adoption of contingency planning, benchmarking and continuous improvement processes to enhance organisational adaptability. Therefore, it is recommended that the management of Petroleum tank farms should put in place mechanisms to advance continuous improvement processes by allocating the necessary amount of resources, such as energy, time and money, in order to promote the continuous development of the continuous improvement systems. Furthermore, managers of Petroleum tank farms should make better the adoption of contingency planning, ensuring that there is as much necessary training and information for employees on how to act during a crises situation, in order to evaluate safety and prepare in advance for recovery from disasters.

多策略融合改进的蜣螂优化算法

针对标准蜣螂优化算法(DBO)存在的全局探索能力欠缺、收敛精度低及易陷入局部最优等不足,提出了一种融合多策略的改进蜣螂优化算法(MSDBO).首先,引入社会学习策略引导推球蜣螂进行位置更新,提高了算法全局探索能力,避免算法陷入局部最优;其次,提出一种方向跟随策略,建立起小偷蜣螂与推球蜣螂个体间的交互,提高了寻优精度;最后,引入环境感知概率,引导小偷蜣螂合理采用方向跟随策略,兼顾了性能与时间消耗.在12个基准测试函数上进行求解分析,并与其他优化算法进行对比,证明了MSDBO的寻优性能明显优于对比算法,在压力容器设计优化问题上的结果验证了MSDBO求解实际工程约束优化问题的有效性.

一种多策略改进鲸鱼优化算法的混沌系统参数辨识

针对混沌系统参数辨识精度不高的问题,以鲸鱼优化算法(whale optimization algorithm,WOA)为基础,提出一种多策略改进鲸鱼优化算法(multi-strategy improved whale optimization algorithm,MIWOA)。采用Chebyshev混沌映射选取高质量初始种群,采用非线性收敛因子和自适应权重,提高算法收敛速度,为了避免算法陷入局部最优,动态选择自适应t分布或蚁狮优化算法更新后期位置,提高处理局部极值的能力。通过对10个基准函数和高维测试函数进行仿真试验,表明MIWOA具有良好的稳定性和收敛精度。将MIWOA应用于辨识Rossler和Lu混沌系统参数,仿真结果优于现有成果,表明本文MIWOA辨识混沌系统参数的高效性和实用性。

混合多策略改进的樽海鞘群算法及其应用

针对标准的樽海鞘群算法(salp swarm algorithm, SSA)在寻优过程中易出现局部最优和收敛速度慢等问题,提出一种混合多策略改进的樽海鞘群算法(ISSA)。利用佳点集策略生成初始种群,使个体均匀分布于搜索空间;将反向学习的思想融入到领导者位置更新中,提高算法的搜索精度;加入自适应t分布,利用迭代次数iter作为其自由度参数,改善算法的全局探索能力;引入精英反向学习,筛选更好的种群,避免陷入局部最优。通过一组基准函数和Wilcoxin秩和检验来检测改进算法的性能,实验结果表明,改进算法的探索能力和优化精度都得到明显改善且算法之间存在显著差异,通过实际机械设计案例进一步验证ISSA算法的有效性。

适应度反向学习的平衡灰狼算法及其应用

针对传统灰狼优化算法位置更新时勘探与开发失衡,收敛速度慢且陷入局部最优的问题,提出一种改进的灰狼算法(balanced grey wolf algorithm based on fitness back learning,BGWO),引入非线性控制参数,增强算法前期勘探能力,加速收敛;在种群迭代阶段采用重心反向学习的最优适应度权重更新策略,平衡算法的勘探与开发。16组基准函数测试结果表明,改进后算法能自适应跳出局部最优,在加快算法收敛速度的同时提高全局收敛能力与精度。将BGWO应用于PV型旋风分离器粒级效率GBDT(gradient boosting decision tree)的建模,提高了GBDT的精度,模型相关系数0.980,均方误差0.00079,BGWO-GBDT与GBDT、PSO-GBDT和GWO-GBDT相对比,建模精度和稳定性明显提高,验证了BGWO的有效性。

煤炭企业契约化成本管理承包成本指标的确定与考核:以河南省正龙煤业有限公司城郊煤矿为例

近年来,煤炭企业纷纷引入了契约化成本管理,替代了传统的行政命令式的成本管理模式。通过“契约化”的方式,煤炭企业内部建立起了全员积极参与的成本管理机制,有效解决了推诿扯皮、思想懒惰、责任意识不强等管理问题。但随着契约化成本管理模式的深入推进,如何确定承包成本指标并进行考核已成为能否有效实施契约化成本管理的关键问题。本文系统地总结了河南省正龙煤业有限公司城郊煤矿实施契约化成本管理的实践经验。首先,阐述了契约化成本管理整体目标的制定流程,以及如何将契约化成本管理整体目标分解为各项承包成本指标并划分给专业系统与职能科室等责任主体;其次,对契约化成本管理中成本对标考核的流程、分工等进行了介绍,并说明了如何依据契约化成本考核结果落实契约化工资兑现;最后,探讨了推行契约化成本管理的保障措施。城郊煤矿契约化成本管理承包成本指标的确定与考核体系的有效实施,激发了各责任主体主动参与成本管理,为煤炭企业成本管控提供了宝贵的实践经验。

基于混沌映射的改进金枪鱼群优化算法对比研究

Kubernetes作为当前云资源管理的标准平台,因其默认调度机制的局限性,目前普遍采用基于群智能优化算法的改进方法进行Pod的调度。而针对群智能优化算法存在的寻优性能易受初值影响、迭代后期容易早熟收敛等问题,选择金枪鱼群优化(Tuna Swarm Optimization,TSO)作为基础算法,根据混沌映射具有的遍历性、随机性等特点,提出了基于混沌映射的种群初始化优化方案。选择目前研究中普遍涉及的Tent、Logistic等多种混沌映射,分别对金枪鱼种群进行初始化,以提高初始种群的多样性。通过一系列基准测试函数进行仿真实验,对比基于不同混沌映射的改进金枪鱼群优化算法的实验结果,证明了基于混沌映射的优化方案可以有效提高原始TSO算法的收敛速度和寻优精度。

改进麻雀搜索算法优化BP神经网络的短时交通流预测

针对BP神经网络预测短时交通流量过于依赖初始参数的问题,提出一种基于改进麻雀搜索算法(ISSA)来优化BP神经网络的短时交通流预测模型(ISSA-BP)。针对标准麻雀搜索算法(SSA)易收敛于原点,容易陷入局部最优等问题,对麻雀群体中的发现者和部分加入者的位置更新公式分别进行改进,改进后的发现者将基于搜索维度的大小和当前最优值的位置来进行全局搜索,部分加入者将根据其与最优位置之间的距离来进行全局搜索。通过实验对BP,PSO-BP,SSA-BP,ISSA-BP 4种短时交通流预测模型的预测效果进行对比分析,结果显示,ISSA-BP短时交通流预测模型的误差最小,ISSA-BP模型相较BP模型在MAE评价指标上的预测精度提升了48.85%,有着更好的预测精度。

融合动态小孔成像的鲸鱼优化算法

针对鲸鱼优化算法容易陷入局部最优和收敛精度低等缺点,提出了一种融合动态小孔成像策略的改进鲸鱼优化(DPIWOA)算法。动态小孔成像策略与普通的反向学习策略相比,可以产生更多样化的对立点,使用该策略可以加快算法的收敛速度和提高收敛精度,同时也可以避免算法在迭代过程中陷入局部最优。通过23个基准测试函数的实验结果表明,DPIWOA在收敛速度和寻优精度等方面均有提升,验证了改进策略的有效性和实用性。

轮辋侧向刚度对路噪提升相关性研究

为优化某款电动汽车路噪,采用仿真结合测试的方法,以轮辋侧向刚度为评价指标,对其轮辋进行模态仿真和试验对标,验证了轮辋侧向刚度可以在车型开发前期通过仿真计算,并提出具体指标进行相应控制,验证不同侧向刚度对噪声传递函数的影响,优化前后轮辋侧向刚度道路测试验证结果显示,在200~400Hz频段内,轮辋侧向刚度对整车路噪有显著影响。文章开展轮辋侧向刚度对路噪提升相关性研究验证,进一步验证了轮辋侧向刚度提高对路噪改善的影响,为优化电动汽车的NVH路噪提供了理论依据。

混沌映射的粒子群算法分析比较

为解决粒子群算法(Particle Swarm Optimization, PSO)易受初值影响、迭代后期早熟收敛、局部寻优结果不稳定等问题,提出混沌映射粒子种群初始化的方案。即在算法前期根据混沌映射初值敏感性、随机性等特征,基于6种混沌映射对粒子群算法分别进行初始化,进而增加种群多样性和解的覆盖。引入动态惯性权重系数,提高算法收敛速度。通过6个测试函数进行仿真实验,对比不同混沌映射数据结果,实验证明该算法能在不改变原有时间复杂度的基础上较好地提高算法的收敛速度和寻优精度。

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

一种新的全局优化算法:碳循环算法

随着人类科学技术水平的高速发展,在应用研究、工程设计等领域存在维数大、阶数高、目标函数多、约束条件复杂等传统算法难以求解的困难问题需要优化和解决。以计算机运算与解决问题水平的持续发展为基础,元启发式优化算法被提出并被证明解决以上类别的问题要优于传统优化方法。作为对元启发式优化算法的补充,文中提出了一种新的用于连续全局优化的元启发式算法:碳循环算法(Carbon Cycle Algorithm,CCA)。该算法模拟了碳元素的自然循环过程,具体为通过模拟动植物呼吸、动物捕食、动植物死亡、分解者分解以及植物光合作用过程,以此为策略来更好地探索和利用搜索空间。通过与一些著名的优化算法在13个基准函数上的测试对比结果,剖析了该算法的计算收敛过程。测试结果表明,该算法具有一定的竞争力并能够解决具有挑战性的问题,可以在大多数基准函数上提供更好的求解精度。

基于改进樽海鞘群算法的特征选择方法

针对樽海鞘群算法求解精度不高、收敛速度慢、容易陷入局部最优等问题,提出融合多种策略的改进樽海鞘群算法CESSA。在初始化种群时引入无限折叠混沌映射,增加初始种群的多样性;在领导者位置更新处融入非线性收敛算子,提高算法全局勘探收敛速度,增强解的精度;根据适应度值的优劣对追随者位置分别采用收敛算子和随机数扰动方式进行更新。改进算法CESSA与K近邻分类器结合构成特征选择模型CESSAFS并用于数据分类。在8个基准函数和10个UCI数据集上分别对CESSA和CESSAFS算法进行测试,其结果表明,改进算法性能更优。

多策略融合的改进黏菌算法及其应用

针对黏菌算法存在自适应能力有限,抗停滞能力弱等不足,提出多策略融合的改进黏菌算法。采用Bernoulli混沌初始化,丰富种群多样性,提升算法优化精度和收敛速度;提出动态非线性递减策略,动态调节黏菌个体探索幅度,协调并优化算法全局搜索与局部开发能力;结合麻雀算法的预警机制与折射反向学习策略,优化黏菌个体分离觅食过程,防止前期优质个体流失以及后期种群多样性匮乏,提升算法整体抗停滞能力。通过对基准测试函数及部分CEC2017测试函数进行寻优对比实验,测试结果表明改进算法具有更好的寻优精度、稳定性。利用改进算法优化XGBoost参数并将其用于变压器故障诊断,进一步验证了改进策略的有效性及算法的工程实用性。