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Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

International Journal of Intelligent Computing and Cybernetics 2023年第2期16卷 250-276页

作者： Qingxia Li Xiaohua Zeng wenhong wei School of Computer and Information Dongguan City CollegeDongguanChina School of Economics and Trade Guangzhou Xinhua UniversityDongguanChina School of Computer Dongguan University of TechnologyDongguanChina

Purpose-Multi-objective is a complex problem that appears in real life while these objectives are conflicting.The swarm intelligence algorithm is often used to solve such multi-objective problems.Due to its strong search ability and convergence ability,particle swarm optimization algorithm is proposed,and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems.However,the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence.Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm.Therefore,this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approach-In this paper,the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.Findings-In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm,this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization.Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/value-In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently,this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

关键词： Particle swarm optimization Cauchy variation Crowding distance Multi-objective Pareto

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Application of LSTM model optimized by individual-ordering- basedadaptivegeneticalgorithmin stock forecasting

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International Journal of Intelligent Computing and Cybernetics 2023年第2期16卷 277-294页

作者： Yong He Xiaohua Zeng Huan Li wenhong wei School of Computer Dongguan University of TechnologyDongguanChina School of Economics and Trade Guangzhou Xinhua UniversityDongguanChina

Purpose-To improve the accuracy of stock price trend prediction in the field of quantitative financial trading,this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory(LSTM).Design/methodology/approach-In this paper,an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.Findings-The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models.Furthermore,the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.Originality/value-(1)The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination.Compared with other models,it has higher accuracy in predicting the up and down trend of stock prices in the next day.(2)Adopting real coding,elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm,the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

关键词： Stock price prediction Long-short-term memory Adaptive genetic algorithm Machine learning

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A multi-objective multicast routing optimization based on differential evolution in MANET

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International Journal of Intelligent Computing and Cybernetics 2018年第1期11卷 121-140页

作者： wenhong wei Yong Qin Zhaoquan Cai School of Computer Science Dongguan University of TechnologyDongguanChina Department of Computer Science Huizhou UniversityHuizhouChina

Purpose–The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem.In mobile ad hoc network(MANET),multicast routing is a non-deterministic polynomial-complete problem that deals with the various objectives and constraints.Quality of service(QoS)in the multicast routing problem mainly depends on cost,delay,jitter and bandwidth.So the cost,delay,jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols.However,mobile node battery energy is finite and the network lifetime depends on node battery energy.Ifthe batterypowerconsumptionishigh inany one ofthe nodes,the chances ofnetwork’s lifereduction due to path breaks are also more.On the other hand,node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time.Hence,the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem.Design/methodology/approach–Recently,many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem,although it obviously is a multi-objective optimization problem.In the MOMR-DE,the network lifetime,cost,delay,jitter and bandwidth are considered as five objectives.Furthermore,three QoS constraints which are maximum allowed delay,maximum allowed jitter and minimum requested bandwidth are included.In addition,we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth,minimize cost,delay and jitter.Findings–Two sets of experiments are conducted and compared with other algorithms for these problems.Thesimulation results showthat our proposedmethod is capableof achieving faster convergence andis more preferable for multicast routing in MANET.Originality/value–In MANET,most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem.However,this pape

关键词： Differential evolution Quality of service Mobile ad hoc network Multicast routing

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EXTENDED CLUSTERING COEFFICIENTS：GENERALIZATION OF CLUSTERING COEFFICIENTS IN SMALL-WORLD NETWORKS

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Journal of Systems Science and Systems Engineering 2007年第3期16卷 370-382页

作者： Wenjun XIAO wenhong wei weidong CHEN Yong QIN Behrooz PARHAMI Department of Computer Science South China University of Technology Guangzhou 510641 China Department of Electrical & ComputerEngineering University of California Santa Barbara CA 93106-9560 USA

The clustering coefficient C of a network, which is a measure of direct connectivity between neighbors of the various nodes, ranges from 0 （for no connectivity） to 1 （for full connectivity）. We define extended clustering coefficients C（h） of a small-world network based on nodes that are at distance h from a source node, thus generalizing distance-1 neighborhoods employed in computing the ordinary clustering coefficient C = C（1）. Based on known results about the distance distribution Pδ（h） in a network, that is, the probability that a randomly chosen pair of vertices have distance h, we derive and experimentally validate the law Pδ（h）C（h） ≤ c log N / N, where c is a small constant that seldom exceeds 1. This result is significant because it shows that the product Pδ（h）C（h） is upper-bounded by a value that is considerably smaller than the product of maximum values for Pδ（h） and C（h）. Extended clustering coefficients and laws that govern them offer new insights into the structure of small-world networks and open up avenues for further exploration of their properties.

关键词： Clustering coefficient small-world extended clustering coefficient distance distribution

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基于角度轨迹和自适应权重的改进MOEA/D算法

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东莞理工学院学报 2024年第1期31卷 28-36,52页

作者：蒙毅魏文红吴帅东莞理工学院计算机科学与技术学院广东东莞523808

基于分解的多目标优化算法是解决多目标优化问题的有效方法,其主要思想是通过一组均匀分布的权重向量对目标进行加权,形成不同的子问题,在其邻域内通过协同进化得到帕累托最优解集。然而,这些均匀分布的权重向量不能很好地适应所有前沿... 详细信息

基于分解的多目标优化算法是解决多目标优化问题的有效方法,其主要思想是通过一组均匀分布的权重向量对目标进行加权,形成不同的子问题,在其邻域内通过协同进化得到帕累托最优解集。然而,这些均匀分布的权重向量不能很好地适应所有前沿形状的多目标问题,导致解集的多样性下降。为使算法能够高效地判断前沿类型,并有效地调整权重向量,本文提出了基于角度轨迹和自适应权重的改进MOEA/D算法,该算法通过基于角度的聚类方法,降低聚类时间复杂度,并利用聚类-合并策略,提高对前沿划分的准确性。在根据聚类结果判断出前沿类型后,算法根据其前沿类型,采用根据角度划分区域的分配策略,或是根据前沿分段长度和数量分配。这样可以有效地分配计算资源,提高多目标问题求解的效率,通过实验证明,算法的分配策略行之有效,能提高尖峰、长尾和不连续前沿的解集多样性,在收敛性和多样性上均优于对比算法。

关键词：多目标优化 MOEA/D 自适应聚类调整权重

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融合多策略改进的自适应乌鸦搜索算法

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东莞理工学院学报 2024年第1期31卷 44-52页

作者：陈志鹏李环魏文红东莞理工学院计算机科学与技术学院广东东莞523808

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,... 详细信息

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。

关键词：乌鸦搜索算法记忆遗忘机制黄金正弦算法自适应参数工程优化

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基于多策略改进人工蜂群算法的投资组合优化应用

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东莞理工学院学报 2024年第3期31卷 9-16,42页

作者：李世豪魏文红张宇辉吴昊东莞理工学院计算机科学与技术学院广东东莞523808

投资组合问题主要侧重风险与收益的平衡,是多维优化问题,需要寻优能力强且具稳定性的算法。人工蜂群算法以其强大的寻优能力和对参数不敏感的优点而著称,因此适用于解决投资组合问题。本文结合反向学习策略、精英策略和Metropolis算法... 详细信息

投资组合问题主要侧重风险与收益的平衡,是多维优化问题,需要寻优能力强且具稳定性的算法。人工蜂群算法以其强大的寻优能力和对参数不敏感的优点而著称,因此适用于解决投资组合问题。本文结合反向学习策略、精英策略和Metropolis算法策略三种策略,对参数较少的人工蜂群算法进行改进,以解决投资组合的风险与收益平衡问题。在初始化阶段,利用Logistic映射和反向解来提高收敛速度和精度;在雇佣蜂和跟随蜂工作过程中,利用上一代精英个体的位置信息,让蜂群更好地靠近最优蜜源;最后借鉴模拟退火中Metropolis算法,重新设计侦察蜂机制,提高算法的寻优能力。实验表明,本文提出的人工蜂群EABC算法(Elite Artificial Bee Colony),具有较强的寻优能力和较高的稳定性。不管是在30维还是100维中,EABC算法对6个测试函数的测试都可以取得较高的收敛精度。最后,EABC算法在投资组合夏普比率模型的优化中,可以得到可靠的方案,较高的夏普比率。

关键词：群体智能优化算法人工蜂群算法精英个体 Metropolis算法夏普比率

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A Parallel Single-Source Shortest Path Algorithm based on Bucket Structure

A Parallel Single-Source Shortest Path Algorithm based on Bu...

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第25届中国控制与决策会议

作者： Qingxia Li wenhong wei Department of Computer City College of Dongguan University of Technology School of Computer Dongguan University of Technology

ISBN: (纸本)9781467355339

This paper focuses on the proposed bucket structure, using communication mechanisms and data structures of Parallel Boost Graph Library and parallel algorithm design method, to propose a parallel single-source shortest path algorithm based on bucket structure. In this algorithm, bucket structure is designed by bucket width parameter, the storage structure is designed by graph partition, information packets are transmitted by asynchronous parallel communication strategy, which makes our algorithm has good parallel speedup ratio and scalability in solving all the single-source shortest path problems. At last the conclusions are verified by experiments.

关键词： Bucket Structure Single-Source Shortest Path Parallel Algorithm.

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Fast Access for ZigBee-enabled IoT Devices Using Raspberry Pi

Fast Access for ZigBee-enabled IoT Devices Using Raspberry P...

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第30届中国控制与决策会议

作者： Ming Tao Xiaoyu Hong Chao Qu Jie Zhang wenhong wei School of Computer Science and Network Security Dongguan University of Technology

With the development of Internet of Things（IoT） technology, the application of IoT is more and more importantly widespread. Meanwhile, how to achieve fast access for various IoT devices, especially for a large-scale of IoT devices, also has been a significant challenge in the development of IoT systems. To address this issue, a fast access protocol for ZigBee-enabled IoT devices is proposed in this paper, in which, the ZigBee module embedded in a registering IoT device could automatically obtain a Personal Area Network（PAN） address and instantly access the target ZigBee wireless network through the signaling interactions among the involved protocol entities. After that, the operation and control of the accessed IoT devices are conducted based on the ZigBee communications. To evaluate the effectiveness and efficiency of this protocol, a prototype system is developed. The experiment and analysis results also have been shown to demonstrate the performance by comparing with the existing access methods.

关键词： IoT ZigBee Fast Access Effectiveness and Efficiency

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用于UAV运动目标搜索的自适应粒子群算法

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计算机工程与应用 2023年第23期59卷 320-333页

作者：杨鸿光张宇辉魏文红东莞理工学院计算机科学与技术学院广东东莞523808

针对运动编码粒子群算法在处理无人机运动目标搜索问题时存在被其他高概率区域混淆、算法搜索成功率不够高的问题,提出了一种基于运动编码的自适应学习策略粒子群优化算法以优化无人机飞行路径。该算法先设计了适应于各种搜索场景的初... 详细信息

针对运动编码粒子群算法在处理无人机运动目标搜索问题时存在被其他高概率区域混淆、算法搜索成功率不够高的问题,提出了一种基于运动编码的自适应学习策略粒子群优化算法以优化无人机飞行路径。该算法先设计了适应于各种搜索场景的初始化方案;再融入聚类算法用以动态划分粒子群,并改进了子群中不同类型粒子的更新方程以适应路径规划中的粒子子群;最后添加了自适应学习策略以控制参数,旨在保持收敛速度的基础上提高搜索到最优路径的概率。在不同搜索场景下的实验结果表明,与运动编码粒子群优化算法相比,算法的检测性能提升了6%。此外,与其他元启发式优化算法的对比结果也展示了算法的优势。

关键词：粒子群优化自适应子群运动目标搜索无人机

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