Bat algorithm is a popular solution in numerous engineering contexts, owing to its straightforward implementation and the minimal number of parameters required. However, it is prone to local optima, and exhibits limited population diversity. This paper proposes a multi-strategy Bat optimization algorithm to address the aforementioned problems. Firstly, a point set method that can be said to be both good and effective is adopted for the purpose of initializing the population and improving the diversity of the bat population. Secondly, a nonlinear inertia weight is proposed to update the position of the bat population and to adaptively adjust its position based on the characteristics of the bat population. The purpose of this adjustment is to improve the optimization performance of the bat algorithm. Subsequent to this, the crisscross optimization that is both horizontal and vertical is introduced in order to circumvent the possibility of the algorithm becoming trapped in a local optimum. In conclusion, a total of 12 benchmark test functions have been selected for the purposes of experimentation. A comparative analysis of seven distinct algorithms is then undertaken, with the objective of substantiating the feasibility and efficacy of the novel algorithm proposed in this study.

Inspired by the intricate group dynamics of wild gorilla populations, the Artificial Gorilla Troops Optimizer (GTO) represents a novel approach in swarm intelligence. Despite its effectiveness in performing global exploration, GTO is prone to early convergence and can easily become stuck in local optima, especially when addressing optimization problems with intricate constraints and rugged search spaces. To overcome these limitations, this paper introduces the Multi-Strategy Integrated Gorilla Troops Optimizer (MSIGTO), which integrates Latin Hypercube Sampling (LHS), Lévy Flight (LF), and the Cauchy Inverse Cumulative Distribution Operator (CICDO). The diversity of the initial population is enhanced through LHS, and the exploration and convergence characteristics of the algorithm are further improved by LF and CICDO. To validate its effectiveness, MSIGTO is compared with 8 representative population-based optimization algorithms. Experimental evaluations on the 2017 IEEE Congress on Evolutionary Computation (CEC2017) and 2022 IEEE Congress on Evolutionary Computation (CEC2022) benchmark suites demonstrate that MSIGTO achieves a Friedman mean rank of 1.48 on 100 dimensional problems and 1.75 on 20 dimensional problems, respectively. These results indicate superior global exploration capability, convergence efficiency, and solution robustness compared with 8 population-based optimization algorithms. The algorithm’s practicality was further verified on four constrained real-world engineering problems, including the speed reducer design problem, the gear train design problem, the multiple disk clutch brake design problem, and the selective harmonic elimination pulse-width modulation problem for three-level inverters. Overall, the results confirm that MSIGTO is an effective optimizer with broad potential for engineering optimization applications.

Abstract In order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed.

Improving node energy consumption efficiency to increase network survival time is one of the main research priorities in wireless sensor networks (WSNs). This work addresses issues with the current ant colony routing algorithm, including uneven node energy, higher communication cost, and routing loop creation. An improved EEABR algorithm (N-EABR, New Energy-efficient ant based routing) is proposed, based on the existing Energy-efficient ant based routing (EEABR) algorithm. This algorithm adds the combination of "pkt_src" (ant packet source address) and "sq_num" (ant packet sequence number) in the node neighbours list and redefines the pheromone updating equation. By sending brief ant packets, this approach efficiently reduces the loop effect and ensures that node energy is distributed evenly throughout the network.The power control based ant colony routing (PCABR)algorithm, which extracts the RSSI value of "Hello" packets to derive the optimal sending power, to conserve the sending energy, and to avoid energy wastage, is further introduced in this paper in order to reduce the energy consumption of nodes when sending packets. The findings of the simulation demonstrate that the N-EEABR and PCABR algorithms both significantly outperform the other methods in terms of path optimisation accuracy and node energy use efficiency and network energy balance. This study is important because it will increase node energy consumption efficiency and network survival time. It also offers helpful suggestions for optimizing routing algorithms for wireless sensor networks.

To address the security problems caused by malicious nodes in wireless sensor networks, in this study, based on the Bayesian trust models, the adaptive reputation maintenance function was introduced to reduce the influence of the previous node and number of interaction, and the abnormal weakening factor was introduced to reduce the false detection of node by the abnormal behaviors caused by network faults, and combined with the fuzzy evaluation mechanism, to calculate direct trust. In order to improve the reliability of recommendation trust evaluation, the similarity measure theory was adopted to assign weight to different recommendation nodes and redistribute to obtain indirect trust. In order to improve the detection accuracy of the trust model, a weighted factor was adopted to determine the size of the comprehensive trust value jointly by variables in direct and indirect trust. Using the adaptive weighting dynamic updating comprehensive trust value, it could effectively avoid the rapid promotion of trust in a short time, and use the sliding time window to predict the comprehensive trust value. The WSN dynamic trust evaluation and prediction model integrating multiple indicators FSEPM was built. The difference between the predicted trust value and the actual trust value was compared with the trust threshold to judge the node property. Simulation results showed that the trust evaluation model could accurately and reliably evaluate the trust relationship between nodes, detect malicious nodes effectively, and improve the security of wireless sensor networks.

A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks.

针对烟花算法在无线传感器网络节点部署过程中易陷入局部最优导致节点分布不均匀、后期收敛速度慢等问题,本文提出一种基于μ律爆炸算子的烟花虚拟力混合算法(μFW–VFA).首先,采用μ律特性曲线重新定义爆炸算子,增强烟花间的差异性,通过动态调整μ值使烟花爆炸的数目和幅度随迭代次数动态调整,以平衡烟花局部和全局的寻优能力.其次,引入虚拟力调节停滞烟花内传感器节点的位置信息,加速烟花种群进化,增强算法跳出局部最优的能力,提高算法收敛速度.仿真实验表明,经μFW–VFA部署后,网络的重叠区域和监测盲区显著减少,有效提升了网络覆盖率并压缩节点移动距离.

针对传统电能质量监测分析系统通信线路和数据分析等方面问题,基于ZigBee和LabVIEW设计了一套电能质量监测分析系统.由ADS8364和TMS320F2812构成系统的数据采集模块,应用CC2430构建数据通信模块,通过LabVIEW平台实现电压偏差、频率偏差、谐波等电能质量指标的分析.结果表明,该系统能够实现电能质量的监测和分析,且误差满足国家电能质量标准规定,具有一定的实用价值.

The openness of Wireless Sensor Networks (WSNs) layout makes them vulnerable to attacks. One of the most threatening attacks is the wormhole attack. The wormhole attack is difficult to be detected since it causes false routing by private tunnels and damages to WSNs in data transmission. Therefore, in order to resist wormhole attacks and improve network performance, this paper proposes a detection algorithm integrated with the node trust optimization model (NTOM-DA) against wormholes in WSNs. First, add the node whose number of neighbors exceeds the threshold to the list of suspicious nodes, and then the exclusive neighbors of suspicious nodes communicate with each other. Mark the path whose hops exceed the wormhole threshold as the path to be tested. Establish a trust model to calculate the node trust and evaluate the path trust. The simulation results show that NTOM-DA is superior in detecting wormhole attacks. Even in the face of a network with high node density and high attack degree, it still has a high detection rate and a low false-positive rate, which effectively guarantees the safe and reliable operation of WSNs.

Aiming at the problem that the searching effect of cognitive radio spectrum allocation optimization algorithm leads to the low total benefit of the system, we propose a binary firefly spectrum allocation strategy based on logistic mapping, because the binary firefly algorithm is prone to fall into the local optimum. Random moving step size and random number, as the random terms of position updating formula of the firefly algorithm, is optimized by the logistic mapping and the optimization results are modified to make the algorithm jump out of local optimum quickly; The binary conversion of firefly position is carried out in an adaptive way to enhance the exploration ability of the algorithm in the early stage and the development ability in the later stage. Simulation results show that compared with BFA and BPSO algorithms, the total system benefit of the proposed algorithm is improved by 8.19% and 11.97% respectively, and it can achieve more efficient spectrum allocation.

针对烟花算法后期收敛速度慢,易陷入局部最优等问题,本文通过引入μ律特性曲线将适应度值映射为适应度等级重新定义爆炸算子,动态调整μ的值以及缩放因子F,更好的平衡了算法的局部开采和全局搜索能力,同时借助基于信息交流的映射策略,增强映射后火花的搜索效率,提高了算法的收敛速度.文中选用12个通用的基准测试函数对μFWA算法与其它四种算法进行对比,实验结果表明,μFWA算法的单峰函数和多峰函数的寻优结果理想,寻优效率高,全局搜索和局部开采能力更加平衡.

为提高风电功率短期预测的准确性,针对KNN(K-Nearest neighbor algorithm)算法在风电功率预测中的不足,提出了基于K-means和改进KNN算法的风电功率短期预测方法;利用K-means聚类方法确定风电历史样本的类别,对KNN算法中搜索相似历史样本集的方式进行了改进和优化,构建了预测模型,并采用C/S架构实现了预测系统的设计;该系统具有自修正功能,能够随着预测次数的增加,不断修正预测模型,逐渐降低预测的误差率;以吉林省某风电场历史数据为样本进行了仿真分析,结果显示该算法与其它算法相比平均绝对误差和均方根误差最大下降1.08％和0.48％,运算时间提升了 5.45％,在风电功率超短期多步预测中具有推广应用价值.

为抵御无线传感器网络中的虫洞攻击,提高网络性能.本文根据虫洞攻击下节点邻居数目出现的异常情况,对邻居数目超出阈值的可疑节点进行筛选,然后令其专有邻居集中的节点相互通信,记录路径跳数,将跳数超出虫洞阈值的路径标记为待测路径;借助贝叶斯信誉模型计算该待测路径上中间节点的直接信任值,并结合邻居数目、处理延时、节点能量、包转发率等信任因素对节点的间接信任值进行评判,进而获得该节点的综合信任值;通过将路径跳数与中间节点的综合信任相结合,计算待测路径的路径信任评价量,并依据受虫洞攻击节点的路径特性合理设定信任阈值,提出一种融合节点信誉度和路径跳数的虫洞攻击检测策略(wormhole attack detection strategy integrating node creditworthiness and path hops,WADS-NC&PH),以检测无线传感器网络中的虫洞攻击.仿真结果表明,WADS-NC&PH对虫洞攻击的检测具有显著效果,即使面对高攻击度的网络,该策略仍能有效检出虫洞攻击并移除虚假链路,提高无线传感器网络的安全性和可靠性.

Wireless sensor network (WSN) nodes owing to their openness, are susceptible to several threats, one of which is dishonest recommendation attacks providing false trust values that favor the attacker. In this letter, a malicious node detection strategy is proposed based on a fuzzy trust model and artificial bee colony algorithm (ABC) (FTM-ABC). The fuzzy trust model (FTM) is introduced to calculate the indirect trust, and the ABC algorithm is applied to optimize the trust model for detecting dishonest recommendation attacks. Besides, fitness function includes recommended deviation and interaction index deviation to enhance the effectiveness. Simulation results reveal the improved FTM-ABC maintains a high recognition rate and a low false-positive rate, even if the number of dishonest nodes reaches 50%.

针对传统D-S证据理论地图匹配算法在面对城市密集路网时匹配点易出现波动、准确率下降等问题,提出一种改进的基于D-S证据理论的动态匹配算法,完善了传统D-S证据理论中的候选路段概率公式,可针对不同道路类型自适应调整其权重参数.仿真实验表明:改进后算法的定位点匹配准确率较其他算法提高2％左右,单点匹配时间可减少0.5 ms左右,能高效快捷实现复杂城市路网的定位点精准匹配.

为尽可能提高系统的网络收益及网络资源的利用率,针对原有蚁群算法搜索时间长、收敛速度慢及信息素单一等问题,提出一种基于时间效率的多态蚁群优化算法,借助信息素的增强型积累,为蚁群算法中蚂蚁的行动提供依据,并将其运用到认知无线电动态频谱接入中.以最大网络公平性和网络收益总和作为目标函数的仿真试验表明:改进后的算法能显著地提高系统的网络效益,保证系统的公平性,与此同时,节省了认知用户的搜索时间,使认知用户能更快速地接入可用信道,改进后的算法在加快收敛速度的同时,使得系统吞吐量也显著增加,提升了系统的整体性能.

Wireless sensor network (WSN) works in a complex environment. To interdict the malicious nodes which attacks the safety of network, such as interrupt attacks and selective forwarding attacks, based on TS-BRS reputation model, a model for malicious node identification based on MNRT-OEP&RS algorithm is constructed. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then the similarity of between the benchmark reputation sequence and cycle reputation sequence sets the dynamic reputation double threshold are calculated in order to identify the malicious nodes by dynamically considering the information forwarding behavior. The simulated results show that the improved algorithm can guarantee the security of wireless sensor networks in complex environments effectively with above 90% recognition of malicious nodes and below 8% false positive rate.

Wireless sensor network (WSN) works in a complex environment where it is difficult for people to reach or work. The openness of nodes leads to security threats vulnerable to various attacks. The trust and reputation model can be applied in WSN to reduce damage caused by malicious nodes. However, there is a high false-positive rate in trust and reputation models because a node with less reputation due to the communication environment is judged as a malicious one directly. This paper presents a trust & reputation-based malicious node identification strategy with environmental parameters (TRS&EP) to interdict the malicious nodes, such as interrupt attack nodes and selective forwarding attack nodes. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then TRS&EP estimates benchmark trust according to the environmental parameters. The Gaussian radial basis function is simplified to calculate the similarity between the benchmark trust sequence and cycle reputation sequence. Furthermore, TRS&EP sets three reputation intervals and an adoptive threshold span to identify the malicious nodes by dynamically considering the work environment and states of nodes. The simulated results show that TRS&EP improves the recognition of malicious nodes above 1% compared to comparison algorithms and reduces the false-positive percentage by more than 1%.

针对传统D-S证据理论地图匹配算法中将交叉路段当成普通路段处理所造成的误匹配、不匹配、匹配精度低等问题,提出一种交叉路段背景下改进的D-S证据理论地图匹配算法.利用距离阈值剔除异常定位点,并用插值法补全被剔除异常定位点所产生的空缺,生成网格索引及简化误差椭圆公式确定候选路段以减少匹配时间.针对道路特点结合方向证据确定可信度函数,最后融合方向概率分配函数、距离概率分配函数和可信度函数来改进候选路段概率公式,确定匹配路段,提高匹配精度.实验表明,改进后算法的匹配准确率约97％,与现有地图匹配算法相比较,精度可提高4％左右,单点匹配时间可减少1 ms左右,算法性能得到提升.