基于改进鲸鱼优化算法的血液供应链网络多目标鲁棒优化设计

为解决血液供应链网络设计中的不确定性问题,建立了一种血液供应链网络多目标鲁棒优化设计模型。首先,针对带有5个节点的血液供应链网络,建立考虑安全库存的、目标为成本最小、存储时间最短的优化函数,并采用ε约束、Pareto最优和鲁棒优化方法对已建模型进行处理,将多目标问题转化为单目标鲁棒问题;其次,对原有鲸鱼优化算法（WOA）进行改进,引入差分算法的交叉和变异理念,增强了搜索能力并改善了局限性,从而得到差分鲸鱼优化算法（DWOA）,并采用此算法对处理后的模型求解。通过数值实例,验证当测试问题相同时,优化模型在需求短缺方面比确定模型的短缺量平均少76%。因此,所提优化模型在应对需求短缺时更具优势;通过仿真对比分析图像,得出DWOA相比WOA、粒子群优化（PSO）算法和遗传算法（GA）中断时间更短并且成本更低。     

基于混沌麻雀搜索算法的无人机航迹规划方法

针对无人机(UAV)航迹规划求解计算量大、难收敛等问题,提出了一种基于混沌麻雀搜索算法(CSSA)的航迹规划方法.首先,建立二维任务空间模型与航迹代价模型,将航迹规划问题转化为多维函数优化问题;其次,采用立方映射初始化种群,并使用反向学习策略(OBL)引入精英粒子,增强种群多样性,扩大搜索区域范围;然后,引入正弦余弦算...     

基于改进鲸鱼优化算法的外骨骼机器人步态检测

针对传统的外骨骼机器人步态检测算法中的信息单一化、准确率低、易陷入局部最优等问题，提出基于改进鲸鱼算法优化的支持向量机（IWOA-SVM）的外骨骼机器人步态检测算法，即在鲸鱼优化算法（WOA）中引入遗传算法（GA）的选择、交叉、变异操作，进而去优化支持向量机（SVM）的惩罚因子与核参数，再使用参数优化后的SVM建立分类模型，从而扩大算法的搜索范围，减小算法陷入局部最优的概率。首先，使用混合传感技术采集步态数据，即通过足底压力传感器和膝关节、髋关节角度传感器采集外骨骼机器人的运动数据，并作为步态检测系统的输入；然后，使用门限法对步态相位进行划分并标记标签；最后，将足底压力信号与髋关节、膝关节角度信号融合作为输入，使用IWOA-SVM算法完成对步态的检测。对6个标准测试函数进行仿真实验，并与GA、粒子群优化（PSO）算法、WOA进行比较，数值实验表明，改进鲸鱼优化算法（IWOA）的鲁棒性、寻优精度、收敛速度均优于其他优化算法。通过分析不同穿戴者的步态检测结果发现，准确率可达98.8%，验证了所提算法在新一代外骨骼机器人中的可行性和实用性，并与基于遗传优化算法的支持向量机（GA-SVM）、基于粒子群优化算法的支持向量机（PSO-SVM）、基于鲸鱼优化算法的支持向量机（WOA-SVM）算法进行比较，结果表明，该算法识别准确率分别提高了5.33%、2.70%、1.44%，能够对外骨骼机器人的步态进行有效检测，进而实现外骨骼机器人的精确控制及稳定行走。     

基于自适应鲸鱼优化算法结合Elman神经网络的股市收盘价预测算法

针对Elman神经网络在基于股市网络舆情的收盘价预测中存在的收敛速度慢且预测精度低的问题,提出了结合基于自适应噪声的完全集合经验模态分解(CEEMDAN)的改进鲸鱼优化算法(IWOA)结合Elman神经网络预测模型。首先,通过文本挖掘技术对上海证券交易所股票价格综合指数(SSE)180股的网络舆情进行挖掘和量化,并利用Boruta算法筛选重要属性以降低属性集的复杂度;然后,通过CEEMDAN算法在属性集中添加一定数量特定方差的白噪声,实现属性序列的分解与降噪;同时,利用自适应权重改进鲸鱼优化算法(WOA)以增强其全局搜索及局部开采能力;最后,利用WOA在迭代过程中不断优化Elman神经网络的初始权重和阈值。结果表明:比起单独使用Elman神经网络,所提模型的平均绝对误差(MAE)从358.8120降低至113.0553;与未采用CEEMDAN算法的原始数据集相比,该模型的平均绝对百分比误差(MAPE)从4.9423%降低到1.44531%,说明所提模型有效提高了预测精度,为股市网络舆情的预测提供了一种有效的实验方法。     

Fitness rate-based rider optimization enabled for optimal task scheduling in cloud

ABSTRACT

Not long ago, there has been a dramatic augment in the attractiveness of cloud computing systems that depends computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same physical infrastructure. It is considered as an essential pool of resources, which are offered to users through Internet. Without troubling the fundamental infrastructure, pay-per-use computing resources are provided to the users by the cloud computing technology. Scheduling is a significant dilemma in cloud computing as a cloud provider has to serve multiple users in cloud environment. This proposal plans to implement an optimal task scheduling model in cloud sector as a challenge over the existing technologies. The proposed model solves the task scheduling problem using an improved meta-heuristic algorithm called Fitness Rate-based Rider Optimization Algorithm (FR-ROA), which is the advanced form of conventional Rider Optimization Algorithm (ROA). The objective constraints considered for optimal task scheduling are the maximum makespan or completion time, and the sum of the completion times of entire tasks. Since the proposed FR-ROA has attained the advantageous part of reaching the convergence in a small duration, the proposed model will outperform the other conventional algorithms for accomplishing the optimal task scheduling in cloud environment.     

Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm

This study proposes an Improved Whale Optimization Algorithm (IWOA) that, on the basis of Whale Optimization Algorithm (WOA) designed by Mirjalili and Lewis (2016), introduces Gaussian mutation operator, differential evolution operator, and crowding degree factor to the algorithm framework. Test results with nine classic examples show that IWOA significantly improves WOA’s precision and computing speed. We also model the locating problem of Electric Vehicle (EV) charging stations with service risk constraints and apply IWOA to solve it. This paper introduces service risk factors, which include the risk of service capacity and user anxiety, establishing the EV charging station site selection model considering service risk. Computational results based on a large-scale problem instance suggest that both the model and the algorithm are effective to apply in practical locating planning projects and help reduce social costs.     

改进鲸鱼优化支持向量机的交通流量模糊粒化预测

针对支持向量机（SVM）在交通流量预测中存在波动性且预测精度低的问题,提出了采用模糊信息粒化（FIG）和改进鲸鱼优化算法（IWOA）的SVM模型来预测交通流量的变化趋势和动态区间。首先,对数据处理采用FIG方法进行处理,从而得到交通流量变化区间的上界（Up）、下界（Low）和趋势值（R）;其次,在鲸鱼优化算法（WOA）的种群初始化中采用动态对立学习来增加种群多样性,并引入了非线性收敛因子和自适应权重来增强算法的全局搜索及局部寻优能力,然后建立了IWOA模型,并分析了IWOA的复杂度;最后,以预测交通流量的均方误差（MSE）为目标函数,在IWOA迭代过程中不断优化SVM的超参数,建立了基于FIG-IWOA-SVM的交通流量区间预测模型。在国内和国外交通流量数据集上进行测试的结果表明,在国外交通流量预测上,与基于遗传算法优化的支持向量机（GA-SVM）、基于粒子群优化算法优化的支持向量机（PSO-SVM）和基于鲸鱼优化算法的支持向量机（WOA-SVM）相比,IWOA-SVM模型的平均绝对误差（MAE）分别降低了89.5%、81.5%和1.5%;而FIG-IWOA-SVM模型在交通流量动态区间和趋势预测上与FIG-GA-SVM、FIG-PSO-SVM和FIG-WOA-SVM等模型相比预测精度更高且预测范围更平稳。实验结果表明,在不增加算法复杂度的前提下,FIG-IWOA-SVM模型能够合理地预测交通流量的变化趋势和变化区间,为后续的交通规划和流量控制提供依据。     

一种无线传感器网络蚁群优化路由算法

如何在资源受限的无线传感器网络中进行高效的数据路由是无线传感器网络研究的热点之一.将蚁群优化算法(ACO)应用于无线传感器网络的路由,提出一种无线传感器网络蚁群优化路由算法.该算法利用蚁群的自组织、自适应和动态寻优能力进行网络优化路径的建立与维护,采用Stigmergy的概念来减少控制信息的流量,以实现网络数据的高效传输.仿真分析表明,该算法和DD算法相比在传输延时方面性能相当,在路由代价方面效果显著.另外,该算法还具有可靠性高、适应性强等优点,并能够根据需要实现网络的拥堵控制和能量均衡等综合优化.     

基于趋化校正的哈里斯鹰优化算法

针对哈里斯鹰优化（HHO）算法收敛速度慢、易陷入局部最优的缺点,提出了一种改进HHO算法,即基于趋化校正（CC）的哈里斯鹰优化（CC-HHO）算法。首先,通过计算最优解下降率和变化权重来识别收敛曲线的状态;其次,将细菌觅食优化（BFO）算法的CC机制引入局部搜索阶段来提高寻优的精确性;再次,将生物在运动时的能量消耗规律融入逃逸能量因子和跳跃距离的更新过程中,从而更好地平衡算法的探索与开发;然后,对最优解和次优解的不同组合进行精英选择来拓展算法全局搜索的广泛性;最后,当搜索陷入局部最优时,通过对逃逸能量施加扰动来实现强制跳出。通过10个基准函数对改进算法的性能进行测试,结果显示CC-HHO算法对单峰函数的搜索精度比引力搜索算法（GSA）、粒子群优化（PSO）算法、鲸优化算法（WOA）以及另外4种改进的HHO算法提升超过10个数量级;对多峰函数也有超过1个数量级的优势;在保证搜索稳定性平均提升超过10%的前提下,所提算法的收敛速度明显优于上述几种优化算法,收敛趋势更加明显。实验结果表明,CC-HHO算法有效地提高了原算法的搜索效率和鲁棒性。     

改进麻雀搜索算法在分布式电源配置中的应用

针对麻雀搜索算法（Sparrow Search Algorithm,SSA）在解决高维、非线性的分布式电源（Distributed Generation,DG）优化配置问题中求解精度与稳定性不足的问题,提出一种改进麻雀搜索算法进行求解。通过引入Tent混沌提高初始解的质量,利用Levy飞行策略和柯西高斯变异,增强算法搜索方向的多元性以及跳出局部最优的能力,针对算法在工程应用中产生大量无效麻雀的问题,优化了麻雀位置更新公式,以提高SSA的工程实用性。分别用标准SSA、ISSA、蝴蝶优化算法（Butterfly Optimization Algorithm,BOA）、鲸鱼优化算法（Whale Optimization Algorithm,WOA）测试基准函数,对比验证ISSA的有效性,并将ISSA应用于IEEE33节点系统的DG化配置模型求解,所求的DG配置方案能更大程度地降低配电网有功损耗与电压偏差。     

群体智能算法在图像分割中的应用综述

图像分割的通用方法一直是图像处理领域中的热点和难点。随着人工智能的兴起和发展,群体智能算法成为当下热点研究的方向,将图像分割技术结合群体智能算法成为一种新型有效的改进方法。群智能算法通过模拟自然界的事物或生物的行动规律,将传统的人工智能和群体生物结合,在解空间中搜索最优解,为解决复杂问题提供了新的解决思路。阐述群体智能算法的研究现状和发展过程,将早期的蚁群算法（Ant Colony Optimization,ACO）、经典的粒子群算法（Particle Swarm Optimization Algorithm,PSO）以及较新的麻雀搜索算法（Sparrow Search Algorithm,SSA）为例详细介绍其算法原理方法,并简要表述蝙蝠算法（Bat Algorithm,BA）、鲸鱼优化算法（Whale Optimization Algorithm,WOA）、人工蜂群算法（Artificial Bee Colony Algorithm,ABC）、萤火虫算法（Firefly Algorithm,FA）、布谷鸟搜索法（Cuckoo Search,CS）、细菌觅食算法（Bacterial Foraging Optimization,BFO）和最新的蜉蝣算法（Mayfly Algorithm,MA）的原理,在此基础上,结合国内外文献对上述算法的改进方法和结合图像分割技术的综合改进及应用进行分析总结。将群体智能算法结合图像分割技术的代表性算法提取出来进行列表分析总结,随后概述总结群体智能算法的统一框架、共同特性、不同的差异并提出存在的问题,最后对未来趋势做出展望。     

改进鲸鱼算法及其在路径规划的应用

针对鲸鱼优化算法(WOA)存在的收敛速度慢、收敛精度低和易陷入局部最优等问题,提出了采用非线性收敛因子、协同a的惯性权重、时变独立搜索概率和免疫记忆改进的鲸鱼优化算法(IWTWOA);应用非线性收敛因子、协同a的惯性权重和时变独立搜索概率改进WOA迭代模型,平衡了算法的全局搜索和局部搜索能力,有效避免了陷入局部最优的问题;引入免疫算法的免疫记忆机制,提高了算法收敛速度;选取了15个基准测试函数进行性能测试,结果表明IWTWOA算法在稳定性、计算精度和收敛速度上均有所提高;最终将其应用在路径规划问题中,获得了较好的结果.     

A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

Whale Optimization Algorithm (WOA), as a new population-based optimization algorithm, performs well in solving optimization problems. However, when tackling high-dimensional global optimization problems, WOA tends to fall into local optimal solutions and has slow convergence rate and low solution accuracy. To address these problems, a whale optimization algorithm based on quadratic interpolation (QIWOA) is presented. On the one hand, a modified exploration process by introducing a new parameter is proposed to efficiently search the regions and deal with the premature convergence problem. On the other hand, quadratic interpolation around the best search agent helps QIWOA to improve the exploitation ability and the solution accuracy. Moreover, the algorithm tries to make a balance between exploitation and exploration. QIWOA is compared with several state-of-the-art algorithms on 30 high-dimensional benchmark functions with dimensions ranging from 100 to 2000. The experimental results show that QIWOA has faster convergence rate and higher solution accuracy than both WOA and other population-based algorithms. For functions with a flat or sharp bottom, QIWOA is difficult to find the global optimum, but it still performs best compared with other algorithms.     

Profit maximization based task scheduling in hybrid clouds using whale optimization technique

ABSTRACT

In cloud computing system, task scheduling plays an important key role. The tasks provided by the user to allocate in cloud have to pay for the share of resources that are used by them. The requirement of task scheduling in the cloud environment has become more and more complex, and the amount of resources and tasks is growing rapidly. Therefore, an efficient task-scheduling algorithm is necessary for allocating the task efficiently in the cloud, which can achieve minimum resource utilization, minimum processing time, high efficiency, and maximum profit. In hybrid clouds to maximize the profit of a private cloud while guaranteeing the service delay bound of delay-tolerant tasks is studied in this article. Here, a new metaheuristic technique inspired from the bubble-net hunting technique of humpback whales, namely whale optimization algorithm (WOA), has been applied to solve the task-scheduling problem. Then WOA algorithm is compared with existing algorithms such as artificial bee colony algorithm (ABC) and Genetic algorithm (GA). The experimental result shows that the proposed WOA algorithm greatly increases the efficiency and achieves maximum profit for the private cloud.     

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsu’s fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.     

ESSAWOA: Enhanced Whale Optimization Algorithm integrated with Salp Swarm Algorithm for global optimization

In this paper, a novel hybrid meta-heuristic algorithm called ESSAWOA is proposed for solving global optimization problems. The main idea of ESSAWOA is to enhance Whale Optimization Algorithm (WOA) by combining the mechanism of Salp Swarm Algorithm (SSA) and Lens Opposition-based Learning strategy (LOBL). The hybridization process includes three parts: First, the leader mechanism with strong exploitation of SSA is applied to update the population position before the basic WOA operation. Second, the nonlinear parameter related to the convergence property in SSA is introduced to the two phases of encircling prey and bubble-net attacking in WOA. Third, LOBL strategy is used to increase the population diversity of the proposed optimizer. The hybrid design is expected to significantly enhance the exploitation and exploration capacity of the proposed algorithm. To investigate the effectiveness of ESSAWOA, twenty-three benchmark functions of different dimensions and three classical engineering design problems are performed. Furthermore, SSA, WOA and seven other well-known meta-heuristic algorithms are employed to compare with the proposed optimizer. Our results reveal that ESSAWOA can effectively and quickly obtain the promising solution of these optimization problems in the search space. The performance of ESSAWOA is significantly superior to the basic WOA, SSA and other meta-heuristic algorithms.

     

改进的鲸鱼优化算法及其应用

为了准确地建立断路器热脱扣器的脱扣时间预测模型,改善脱扣器的稳定性。提出了一种改进的鲸鱼优化算法（IWOA）和支持向量回归（SVR）综合建模的方法。针对鲸鱼优化算法（WOA）具有易陷入局部最优解、收敛速度慢、收敛精度低等问题,提出三个策略改进,并在10个基本测试函数上进行比较,结果证明改进的鲸鱼优化算法具有更高的收敛精度和更快的收敛速度。建立IWOA-SVR脱扣时间预测模型,并优化断路器的生产过程,进而提高断路器的生产效率和产品质量。     

Energy Efficient Cluster Head Selection for Internet of Things

Recently, Internet of Things (IoT) devices are highly utilized in diverse fields such as environmental monitoring, industries, and smart home, among others. Under such instances, a cluster head is selected among the diverse IoT devices of wireless sensor network (WSN) based IoT network to maintain a reliable network with efficient data transmission. This article proposed a novel method with the combination of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. This method considers the distance, energy, delay, load, and temperature of the IoT devices during the operation of the cluster head selection process. Furthermore, the performance of the proposed method is analyzed by comparing with conventional methods such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and GSO algorithms. The analysis related to the existence of the number of alive nodes, convergence estimation, and performance in terms of normalized energy, load, and temperature of the IoT devices are determined. Thus the analysis of our implementation reveals the superior performance of the proposed method.     

一种无线传感器网络目标的最优覆盖算法

无线传感器网络点状目标覆盖的算法中,集合分割算法虽简单,但效率低且仅适用于每个传感器节点能量都相等的网络模型.为此,我们对集合分割算法进行改进,提出一种启发式贪心最优覆盖算法.该算法适用于节点能量正态分布的网络模型,采用了关键目标优先覆盖策略和节点能效最大化策略,延长了网络覆盖生命期,提高了算法的效率.实验表明新算法网络生命期延长80%以上,有更好的适应性和稳定性.     

基于CPSO的无线传感器网络路由优化

研究无线传感器网络路由优化问题,由于无线传感器节点的能量受到限制,通信过程能量损耗,影响网络的性能。传统粒子群算法难以获得最优网络路由方案。为延长网络生存时间,结合粒子群的快速性和混沌的遍历性优点,提出了一种混沌粒子群(CPSO)的无线网络路由优化方法。通过粒子群算法的自组织、动态寻优能力,并通过混沌机制对粒子群进行混沌扰动,增加多样性,加快最优路由优化速度,使网络最优路由和能量消耗间尽量平衡。仿真结果表明,相对于传统优化算法,CPSO提高了无线传感器网络路由优化速度,减少网络能量消耗,有效延长了网络生存时间,为提高整个网络通信效率提供了参考。