Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.

Improved Fruit Fly Optimization Algorithm for Solving Lot-Streaming Flow-Shop Scheduling Problem

An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to determine the splitting of jobs and the sequence of the sub-lots simultaneously. Based on the encoding scheme,three kinds of neighborhoods are developed for generating new solutions. To well balance the exploitation and exploration,two main search procedures are designed within the evolutionary search framework of the iFOA,including the neighborhood-based search( smell-vision-based search) and the global cooperation-based search. Finally,numerical testing results are provided,and the comparisons demonstrate the effectiveness of the proposed iFOA for solving the LSFSP.

Design and Implementation of Dynamic High-Speed Switches in Super Base Station Architectures

Novel centralized base station architectures integrating computation and communication functionalities have become important for the development of future mobile communication networks.Therefore,the development of dynamic high-speed interconnections between baseband units(BBUs)and remote radio heads(RRHs)is vital in centralized base station design.Herein,dynamic high-speed switches(HSSs)connecting BBUs and RRHs were designed for a centralized base station architecture.We analyzed the characteristics of actual traffic and introduced a switch traffic model suitable for the super base station architecture.Then,we proposed a data-priority-aware(DPA)scheduling algorithm based on the traffic model.Lastly,we developed the dynamic HSS model based on the OPNET platform and the prototype based on FPGA.Our results show that the DPA achieves close to 100%throughput with lower latency and provides better run-time complexity than iOCF and HE-iSLIP,thereby demonstrating that the proposed switch system can be adopted in centralized base station architectures.

Multi-objective optimization for draft scheduling of hot strip mill

A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Hybrid heuristic algorithm for multi-objective scheduling problem

This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.

ANCAEE: A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.

Task scheduling for multi-electro-magnetic detection satellite with a combined algorithm

Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.

Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

Clustering-Inverse: A Generalized Model for Pattern-Based Time Series Segmentation

Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling （IPPS） problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS （MOIPPS） problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatch- ing rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

A Multi-Objective Clustered Input Oriented Salp Swarm Algorithm in Cloud Computing

Infrastructure as a Service(IaaS)in cloud computing enables flexible resource distribution over the Internet,but achieving optimal scheduling remains a challenge.Effective resource allocation in cloud-based environments,particularly within the IaaS model,poses persistent challenges.Existing methods often struggle with slow opti-mization,imbalanced workload distribution,and inefficient use of available assets.These limitations result in longer processing times,increased operational expenses,and inadequate resource deployment,particularly under fluctuating demands.To overcome these issues,a novel Clustered Input-Oriented Salp Swarm Algorithm(CIOSSA)is introduced.This approach combines two distinct strategies:Task Splitting Agglomerative Clustering(TSAC)with an Input Oriented Salp Swarm Algorithm(IOSSA),which prioritizes tasks based on urgency,and a refined multi-leader model that accelerates optimization processes,enhancing both speed and accuracy.By continuously assessing system capacity before task distribution,the model ensures that assets are deployed effectively and costs are controlled.The dual-leader technique expands the potential solution space,leading to substantial gains in processing speed,cost-effectiveness,asset efficiency,and system throughput,as demonstrated by comprehensive tests.As a result,the suggested model performs better than existing approaches in terms of makespan,resource utilisation,throughput,and convergence speed,demonstrating that CIOSSA is scalable,reliable,and appropriate for the dynamic settings found in cloud computing.

双高箱办理站装卸设备协同调度研究

为提高双高箱列车装卸作业效率,针对双高箱办理站不设主作业区、“完全间接装卸”、装卸作业多流向及出站箱同步转运的特点,考虑设备能力、作业序列、轨道吊安全距离约束等现实约束,详细拆分设备间的衔接、共用及等待时间构成,以最小化完成装卸作业用时、装卸线轨道吊等待时间最小化和作业均衡率最大化为优化目标,构建轨道吊、AGV、空箱堆垛机和智能举升机间的协同调度模型,并基于遗传算法和模拟退火算法设计混合算法求解模型。通过实例验证模型及算法的有效性、混合算法和多目标协同优化的优越性,分析表明双高箱办理站布局及装卸作业模式可行,优化指标间两两无单向相关性。最后通过灵敏度分析验证模型可用于装卸设备配置优化,以实现双高箱办理站低成本高效运作。

考虑入库径流和负荷需求不确定性的水库优化调度研究

为量化水库入库径流和电网负荷需求的不确定性,分析二者对水库优化调度过程的影响,本文以三峡水库为例,引入鲁棒优化理论,建立径流和负荷的多面体不确定集合,结合k-means聚类算法对各不确定情景下随机模拟的入流和负荷情景进行聚类处理。建立以电站实际出力与计划出力偏差最小、总发电量最大和下游适宜生态流量改变度最小为目标的多目标优化调度模型。多目标粒子群算法求解结果表明:在考虑水库入流和负荷需求不确定性的前提下,各情景下的水库水位升降变化规律与实际水位变化规律基本相同,且与实际水位相比,水库能在更多时段维持较高水位运行,提高了三峡电站在蓄水期的整体发电水平。

基于改进风光场景聚类联合虚拟储能的源网荷储低碳优化调度

针对源网荷储优化调度时分布式电源的间歇性引起主动配电网节点电压波动以及有功网损增加等问题,提出一种改进风光场景聚类联合虚拟储能的源网荷储低碳优化调度方法。首先,为有效应对风光出力不确定的挑战,提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)的迭代自组织数据分析算法(iterative self-organizing data analysis techniques algorithm,ISODATA),并以DB指数、Dunn指数和轮廓系数对风光场景聚类效果的优劣进行评价。其次,引入阶梯式碳交易机制,建立以主动配电网综合运行成本最低为目标的优化调度模型。同时,提出用户参与满意度和电网依赖度两种运行评价指标。最后,在IEEE 33节点系统上进行算例仿真分析,结果验证了DBSCAN-ISODATA算法的合理性。并且所提低碳优化调度方法能够有效降低系统碳排放量与运行成本,实现了主动配电网低碳经济稳定运行。

基于BBO优化K-means算法的WSN分簇路由算法

针对无线传感器网络中传感器节点能量有限、网络生存期短的问题,提出一种基于生物地理学算法优化K-means的无线传感器网络分簇路由算法BBOK-GA。成簇阶段,通过生物地理学优化算法改进K-means算法,避免求解时陷入局部最优。根据能量因子和距离因子设计了新的适应度函数选举最优簇首,完成分簇任务。数据传输阶段,则利用遗传算法为簇首节点搜寻到基站的最佳数据传输路径。仿真结果表明,相较于LEACH、LEACH-C、K-GA等算法,BBOK-GA降低了网络能耗,提高了网络吞吐量,延长了网络生存周期。

基于麻雀搜索算法的梯级泵站优化调度

针对梯级泵站系统运行效率普遍偏低、能耗损失较大等问题,建立了梯级泵站优化调度模型,引入了寻优能力强、搜索精度高的麻雀搜索算法(SSA),对算法中安全阈值和侦察者占比两参数进行了比选;据此提出了基于SSA算法的梯级泵站优化调度方法,并将其应用于密云水库调蓄工程中的三级泵站优化调度研究。结果表明,三种不同流量工况下,相较于现状方案,PSO及GA算法所得优化方案其系统运行效率可提升0.03%~0.18%,年运行成本可节省¥9,700~¥69,500。利用SSA算法所获优化方案在两项指标改进方面更为突出,可达到0.98%~1.20%的效率提升及¥369,000~¥443,900的年运行费用的节省,验证了SSA算法在梯级泵站优化调度中的可行性和高效性,可为梯级泵站优化调度提供一种合理可靠的方法。

改进GAN模型在基站流量预测及5G节能中的应用

【目的】为了更精准地预测5G基站的流量,分析潮汐现象,提出一种优化的生成对抗网络(generative adversarial network,GAN)模型流量预测方法,并将其用于实际基站的定时控制中。【方法】GAN的生成器利用差分演化灰狼算法优化长短时记忆网络(long short term memory networks,LSTM),判别器使用门控循环神经网络(gated recurrent unit,GRU)进行判别,生成器和判别器利用不断地对抗训练达到均衡从而提高了5G基站流量的预测精度;其次,利用改进人工蜂群优化k-means++算法,将其用于输出最优基站定时时间,达到最大限度节能的目的。【结果】实验结果表明,与现有模型相比,所提预测模型有更高的预测精度,定时控制功能可极大地节约能耗。

基于龙格库塔算法的梯级泵站优化运行

梯级泵站在调水、灌溉与生态修复等建设中发挥了巨大作用。为提高梯级泵站运行效率、节约运行费用,结合某梯级泵站明渠引水工程,以总效率最高为目标建立了梯级泵站优化调度模型,提出了一种基于龙格库塔算法(RUN)的优化调度方法。计算结果表明,与该泵站现状运行方案相比,采用RUN算法计算所得方案可提高运行效率0.73%,节省运行费用271195元/年,优于利用PSO、GA算法所得优化结果,证实了所提方法可良好地服务于梯级泵站优化运行。