BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

A Hybrid Algorithm Based on Comprehensive Search Mechanisms for Job Shop Scheduling Problem

The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing.Aiming at the job shop scheduling problem,a hybrid algorithm based on comprehensive search mechanisms(HACSM)is proposed to optimize the maximum completion time.HACSM combines three search methods with different optimization scales,including fireworks algorithm(FW),extended Akers graphical method(LS1+_AKERS_EXT),and tabu search algorithm(TS).FW realizes global search through information interaction and resource allocation,ensuring the diversity of the population.LS1+_AKERS_EXT realizes compound movement with Akers graphical method,so it has advanced global and local search capabilities.In LS1+_AKERS_EXT,the shortest path is the core of the algorithm,which directly affects the encoding and decoding of scheduling.In order to find the shortest path,an effective node expansion method is designed to improve the node expansion efficiency.In the part of centralized search,TS based on the neighborhood structure is used.Finally,the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature.

A new hybrid algorithm for global optimization and slope stability evaluation

A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.

Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm

We present an improved hybrid genetic algorithm to solve the two-dimensional Eucli-dean traveling salesman problem (TSP), in which the crossover operator is enhanced with a local search. The proposed algorithm is expected to obtain higher quality solutions within a reasonable computational time for TSP by perfectly integrating GA and the local search. The elitist choice strategy, the local search crossover operator and the double-bridge random mutation are highlighted, to enhance the convergence and the possibility of escaping from the local optima. The experimental results illustrate that the novel hybrid genetic algorithm outperforms other genetic algorithms by providing higher accuracy and satisfactory efficiency in real optimization processing.

An Evolutionary Algorithm with Multi-Local Search for the Resource-Constrained Project Scheduling Problem

This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.

混合白鲸优化算法求解柔性作业车间调度问题

针对柔性作业车间调度问题(flexible job-shop scheduling problem,FJSP),提出一种混合白鲸优化算法(hybrid beluga whale optimization,HBWO)对其求解,旨在最小最大化完工时间。采用既定策略改进标准白鲸优化算法(beluga whale optimization,BWO),加快其收敛速度;基于机器选择和工序排序问题设计双层编码方案,解决FJSP离散化问题;采用主动编码及种群初始化策略,提高求解质量;基于工序的开始和结束时间确定关键路径和关键块,注重各工序时间维度;引入贪心思想至基于关键路径的混合变邻域搜索策略中,加大勘测搜索空间及减少无效搜索;此外,引入遗传算子防止算法陷入局部最优;通过35个标准算例的仿真实验与分析,证明了算法在求解FJSP问题中具有有效性。

混合遗传变邻域搜索算法求解柔性车间调度问题

针对考虑生产成本的柔性作业车间调度问题(flow job shop scheduling problem, FJSP),以完工时间与加工成本为优化指标,提出一种求解FJSP的混合遗传变邻域搜索算法。根据个体适应度对种群分割,结合自适应交叉概率改进子代种群产生方式;设计两种邻域结构增强算法的局部搜索能力;提出一种基于动态交叉变异概率的优化算法流程提高求解效率。运用提出的算法求解基准实例与实际问题测试,验证了算法的有效性。

面向多车场冷链物流配送的改进正余弦算法

以冷链物流为对象,研究了一类考虑多中心联合配送和硬时间窗约束的调度问题。基于问题描述建立了以最小化总成本为目标的数学模型。提出了改进正余弦算法(enhanced sine-cosine algorithm,ESCA)以获取当前问题的满意解。结合问题特征创建了融合构造式规则的编解码方法,并辅以个体评估方法实现模型与正余弦算法(sine-cosine algorithm,SCA)的适配。同时,将反向学习机制嵌入ESCA的初始化流程,旨在提升初始解的性能。在种群进化方面,构建了融合双种群机制、非线性参数调节和随机扰动的混合进化机制以平衡寻优过程的全局探索和局部挖掘行为,并通过离散邻域搜索方法避免搜索停滞。开展了案例研究和算法对比实验,结果验证了ESCA算法的良好性能。

面向高维投资组合的多目标优化算法

针对高维投资组合优化问题,提出了一种基于非支配排序和混合搜索的多目标优化算法。考虑到现有进化算法在大规模问题处理上受限于其广泛的搜索空间,引入了基于分解的策略。该策略通过分析个体与参考点的距离,有效地将种群划分为三个子群体。为提升种群多样性并避免局部最优,算法结合了个体的位置特征,并采用了混合局部和全局搜索策略。此外,通过基于分解的双重环境选择机制,有效生成优质解。在包含100、500和1000个决策变量的LSMOP实验中,该算法展现出超越多个先进进化算法的性能。最后,应用该算法于包含交易成本的CVaR模型,并与其他三种多目标进化算法进行比较,进一步证实了其在实际应用中的优势。

基于改进粒子群算法的木材板材下料方法

木材板材在家具行业应用广泛,以绿色环保、节约能源为目的的木材板材优化下料已经成为研究的热点。木材板材下料优化问题属于二维矩形下料问题,是一种具有高度计算复杂性的问题。本研究主要针对单规格木材板材进行矩形零件下料问题,在木材板材长和宽都大于零件长和宽的情况下,通过建立二维下料的数学模型,采用标准粒子群算法、变邻域搜索算法、粒子群混合变邻域搜索算法分别进行求解,并以某企业的下料实例进行分析计算。首先,利用标准粒子群算法求解单规格板材下料问题;其次,利用变邻域搜索算法求解单规格板材下料问题。在获得局部最优解的基础上改变其邻域结构再进行局部搜索,找到另一个局部最优解,如此不断迭代,直到满足算法的终止条件,获得全局最优解;最后,利用粒子群变邻域搜索混合算法求解单规格板材下料问题。针对粒子群算法局部搜索能力较差、容易过早收敛的问题和具有较好包容性的特点,将变邻域搜索的思想融入粒子群算法中,使结果更加趋向全局最优。结果表明:粒子群变邻域搜索混合算法相比粒子群算法和变邻域算法效率都有显著提升,能显著提高该木材板材的利用率,增加企业经济效益。

多水源灌溉系统的管网布置与管径协同优化研究

基于多水源灌溉系统,考虑系统水资源分配并确定管网拓扑结构与连接管道尺寸,以最小化系统流量分配成本和管道安装成本之和为目标函数建立混合整数规划模型。设计一种混合启发式算法将局部搜索与精确算法结合,协同优化管网布置与管网设计两个阶段,分析各节点之间的连接情况与连接管径,并为需水节点分配流量。通过不同规模的测试算例验证协同优化算法能有效降低多水源灌溉系统的建设成本。

部分充电策略下多中心混合车队联合配送路径优化

城市物流电动车与燃油车混合运输场景中,运输资源共享调度和充电策略联合优化方面存在不足。基于此,综合考虑客户时间窗、混合动力车队、电动车部分充电策略、多中心间联合配送机制和碳排放等实际因素,研究带时间窗和部分充电的多中心混合车队绿色车辆路径问题。以车辆固定成本、运输成本、充电成本、碳排放成本和时间惩罚成本之和最小化为目标构建优化模型,并设计混合改进遗传-变邻域搜索算法进行求解。基于湖南省某物流企业的实际数据进行仿真实验,验证了上述模型及算法的有效性,并从配送模式、车队配置和充电策略3个方面进行了敏感性分析。研究结果表明:1)联合配送模式有助于加强配送中心间的协同合作,促进运输资源共享调度,降低物流配送成本并减少碳排放,是一种经济环保的配送模式。2)电动车充电时间过长会影响客户时间满意度下降,且对纯电动车队而言,这一影响更为显著。3)混合车队相比纯电动车队具有更低的配送成本和更高的客户满意度,相比纯燃油车队在降低配送成本和减少碳排放方面更有优势。合理的车队配置不仅能减少企业运营成本,还可以同时兼顾客户利益和环境利益。4)在物流配送中采用部分充电策略能有效节省充电时间并提升客户服务体验。研究成果可为物流企业进行运输资源联合调度和配送方案优化决策提供参考依据。

混合进化算法求解多环节资源配置优化问题

资源配置优化问题是制造业价值链管理的基础问题。然而,现有研究多集中在生产环节,对制造全生命周期的整体考虑不足。研究考虑多环节的制造全生命周期资源配置优化问题(MLCRAOP),旨在通过优化研发设计、生产制造、运维服务和配套设备供应环节的服务资源,提升全生命周期的资源配置客户满意度。将时间、成本、质量指标纳入目标函数构建整数规划模型,提出一种混合进化算法用于求解MLCRAOP。通过在设计案例上的对比实验,验证了混合进化算法具有优异的性能。

基于改进布谷鸟搜索算法的光伏最大功率点跟踪策略

实际工程中,光伏阵列在随机变化的环境中会出现局部遮光的情况,从而导致光伏阵列的功率-电压特性曲线会呈现多峰值状态,传统的最大功率点跟踪(maximum power point tracking, MPPT)算法易陷入局部最优解,追踪速度和精准度无法得到满足。针对这一问题,提出一种基于布谷鸟搜索算法(cuckoo search algorithm, CS)和电导增量法(conductivity increment method, CI)结合的光伏MPPT算法,在算法前期利用布谷鸟搜索算法将大步长和小步长交替使用使得全局搜索能力增强,找到全局最大功率点所处区域附近;在后期,采用步长小、控制精度高的CI进行局部寻优,快速准确地锁定到最大功率点。在MATLAB/Simulink中搭建仿真模型,并与原始布谷鸟搜索算法和粒子群优化(particle swam optimization, PSO)算法进行比较。仿真结果表明,将CS与CI结合的算法使得收敛速度更快,精度更高,稳定状态时功率曲线的波动更小。

基于差分进化粒子群混合算法的多无人机协同区域搜索策略

为提高无人机群在未知环境中的区域搜索效率,提出一种多无人机协同区域搜索策略。首先,根据区域搜索任务需求,建立包含区域覆盖率、区域不确定度、目标存在概率三种属性的区域信息地图;其次,以最大化搜索效率、同时最小化无人机搜索过程中的能耗为目标,建立无人机区域搜索滚动时域优化目标函数,指导无人机在线决策搜索路线;然后针对传统群智能优化算法易陷入局部最优的缺陷,设计差分进化粒子群混合算法在线求解该多目标优化问题,提高算法的寻优性能,从而提高无人机的搜索效率。最后,通过数值仿真实验,对所提算法进行验证,仿真结果表明,文中设计的基于差分进化粒子群混合算法的多无人机协同区域搜索策略与传统的群智能优化算法相比具有更高的区域搜索效率。

基于混合A^(*)搜索和贝塞尔曲线的船舶进港和靠泊路径规划算法

[目的]针对欠驱动无人艇自动进港和靠泊问题,提出一种基于混合A^(*)搜索和贝塞尔曲线的路径规划算法。[方法]该方法通过混合A^(*)搜索在非结构化环境下快速搜索出一条满足无人艇非完整性约束且无碰撞风险的轨迹。在此基础上,基于广义沃罗诺伊图提出曲线优化算法,使得搜索算法得到的轨迹更加平滑且远离环境障碍物,从而引导无人艇在受限水域完成进港任务。同时,针对“最后一公里”靠泊问题,引入四阶贝塞尔曲线,用于生成靠泊路径从而引导船体平稳且精准入泊。[结果]仿真和外场试验结果表明,无人艇能够实现自主避障且精准驶入泊位,靠泊精度指标均小于1.0。[结论]所提路径规划算法能确保欠驱动无人艇实现进港和靠泊任务,可为智能船舶的进一步发展提供思路。

考虑碳交易机制的海港综合能源系统电-热混合储能优化配置

随着港口电气化进程逐渐加速,单一的港口供能方式正在向多种能源深度融合演变.为响应我国“碳达峰、碳中和”战略目标,进一步提升海港综合能源系统的经济与环境双重效益,提出一种考虑碳交易机制的电-热混合式储能优化配置方案.首先,建立海港综合能源系统模型,并给出计及碳交易市场的交易方案;其次,构建双层优化配置框架,上层优化配置混合式储能容量,下层引入碳交易机制,满足港口综合能源系统低碳经济运行需求;最后,结合网格自适应直接搜索法与自适应混沌粒子群算法优势,利用混合式优化算法对双层优化模型进行求解.以天津港的实际运行数据为例,验证该方法的有效性.算例结果表明,所提方法不仅可以降低系统的投入成本,还能显著减少港区碳排放,从而进一步提升港口经济和环境效益.

果蔬采后分级和预冷车辆协同调度模型与算法

新兴的移动式分级、预冷技术应用于果蔬田间采后处理,有助于降低采后损耗,也催生了采后“最先一公里”冷链物流环节协同运作优化问题。以采后分级、预冷环节为例,综合考虑果蔬最佳预冷时间、先分级后预冷的服务顺序等特有协同情景,构建了移动式分级预冷资源协同调度优化模型。与现有模型不同,本研究考虑延迟预冷对果蔬新鲜度的特殊影响,设计了延迟预冷成本函数,在保障产品质量的同时最小化服务运作成本。设计混合遗传算法对模型进行求解,该算法融合了遗传算法与邻域搜索算法,增强混合算法的局部和全局搜索能力。其中,结合问题的双需求特点及关键协同约束,设计了基于双序列的解的表达方式、基于最佳插入策略的交叉算子以及基于三阶段邻域搜索的变异操作,以提高算法的收敛速度与求解质量。通过与标准遗传算法和变邻域搜索算法对比,验证了本文算法在求解大规模算例时可以更快收敛到更高质量的解。基于陕西省洛川县水蜜桃产业的分级预冷数据证明了模型的合理性。本研究有助于把协同运作优化思想引入果蔬采后“最先一公里”冷链物流环节,为降低我国果蔬采后损耗提供创新性解决思路。