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 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.

一种适用于混合三端直流输电线路的故障定位方法

针对因结构复杂导致的混合三端直流输电线路故障定位困难的问题,提出了一种结合变分模态分解算法与改进卷积神经网络(CNN)的故障定位方法(VMD-CNN)。首先,利用PSCAD/EMTDC软件构建混合三端直流输电系统模型,获得故障电流数据,应用克拉克变换对其解耦,获取故障电流的线模分量;其次,对得到的线模分量进行变分模态分解(VMD),得到多个本征模态函数(IMF)分量,选取特征信息最丰富的IMF分量作为VMD-CNN模型的输入;然后,利用高效的分类模型支持向量机(SVM)判别故障发生的区域,将提取到的IMF分量作为SVM输入进行训练学习,可以准确判断出故障发生区域;最后,搭建VMD-CNN模型进行故障定位,挖掘出行波信号中蕴藏的故障信息,同时通过麻雀搜索算法优化CNN中的超参数,实现混合三端直流输电线路的精确定位。仿真结果表明:过渡电阻为100Ω,不同故障位置情况下的定位相对误差均在0.17%以内;故障位置为460 km,不同过渡电阻情况下的定位相对误差均在0.25%以内;过渡电阻为50Ω,不同故障类型情况下的相对误差均在0.3%以内。所提方法能够提升不同故障位置、过渡电阻和故障类型下的定位准确性。

考虑转移时间的多目标双资源柔性作业车间节能调度

针对考虑工人和工件在机器间转移时间的多目标双资源柔性作业车间节能调度问题(MO-DFJESP),构建了以最小化最大完工时间、总能耗、总工人成本和最大工人工作量为优化目标的数学模型。该模型同时还考虑了工人的技能、熟练度和单位成本差异。为了求解MO-DFJESP模型,提出一种多目标混合进化算法(MO-HEATS)。根据MO-DFJESP模型特点,设计了一种多维编码和解码方案以表示问题的可行解。基于sigmoid函数设计了自适应机制,以兼顾MO-HEATS算法的开发和探索能力,并结合禁忌搜索(TS)组件提升局部搜索能力。最后,在仿真算例上进行了消融实验和对比实验,实验结果验证了自适应机制和TS组件对MO-HEATS算法性能具有明显提升作用,且MO-HEATS算法对求解MO-DFJESP模型具有显著优势。

带忽略工序的多目标批量流混合流水车间调度

针对带忽略工序的批量流混合流水车间调度问题,在考虑批次切换调整时间的情况下,以最小化完工时间和机床负荷平衡为优化目标,建立柔性批量分割和调度集成优化模型,提出一种双层改进PSO-GA混合算法。算法提出批量和机器的双层搜索求解框架,外层进行柔性分批,内层搜索排序及调度方案。针对批量分割、工件批排序、机器分配3个问题,设计基于批量、工序和机器的三段式编码,内层将狼群算法的分级和游走策略引入粒子群算法,设计了一种基于PBX(Position-based Crossover)交叉操作的围攻策略以提高算法的局部搜索及寻优能力。通过仿真实验并与几种启发式算法进行对比及实例验证,说明了调度模型和算法的可行性和优越性。

基于改进混合A^(*)算法的自动泊车路径规划方法研究

为了解决在自动驾驶过程中短距离自动泊车场景下,受环境复杂性影响导致传统的A^(*)算法和RS曲线加速搜索算法难以应用的问题,提出了一种加入反向搜索算法的改进混合A^(*)算法;利用地图栅格法和A^(*)算法计算启发值,通过检测车身轮廓线与简化后的障碍物线是否相交判断二者能否相撞,以节省搜索时间;通过控制RS曲线的扩展方向数量,保证路径的平滑性;经MATLAB仿真垂直入库和侧方泊车场景,对改进算法与传统算法进行了对比分析,验证了同等条件下改进的混合A^(*)算法在两种仿真场景的平均搜索时间上分别减少8.18%和20.53%,且能产生更短、更平滑的路径,从而验证了所提基于反向搜索算法的混合A^(*)算法的优越性。

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.

Hybrid Grid DSMC Method for Chemical Nonequilibrium with Rarefied Flow Heating

The influence of chemical nonequilibrium on the thermal characteristics is explored by using the 2Dhybrid grid direct simulation Monte Carlo(DSMC)parallel method.An improved molecule search algorithm is proposed,which can preserve the high efficiency of area search algorithm.This method can overcome the defects of area search algorithm,and give all information about molecules hitting surface.The heat flux calculation method for a rarefied hypersonic flow is established.In addition,the testing methods of chemical reaction probability for five species of mixed gas with limited speed chemical reactions are also selected.To validate the effectiveness of the present method,hypersonic flow around a cylinder is firstly simulated,and subsequently numerical simulations of the heat flux and flow field characteristics around the blunt body at different heights are carried out in two different cases:the thermal nonequilibrium condition and the thermochemical nonequilibrium condition.Numerical results demonstrate the validity and reliability of the proposed methods.

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation Problem

Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.

Hybrid Improved Self-adaptive Differential Evolution and Nelder-Mead Simplex Method for Solving Constrained Real-Parameters

In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE （differential evolution） algorithm and exploitation capability of Nelder-Mead simplex method is presented （HISADE-NMS）. The DE has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles （NP）, scaling factor （F） and crossover control （CR）, which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depends on the characteristics of each objective function, therefore, we have to tune their value in each problem that mean it will take too long time to perform. In the new manner, we present a new version of the DE algorithm for obtaining self-adaptive control parameter settings. Some modifications are imposed on DE to improve its capability and efficiency while being hybridized with Nelder-Mead simplex method. To valid the robustness of new hybrid algorithm, we apply it to solve some examples of structural optimization constraints.

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。