二维矩形Strip Packing问题的算法研究与改进

二维矩形Strip Packing问题的约束条件及目标函数与基本型二维矩形Packing问题类似,都是在有限的矩形容器中,有效地摆放各个矩形块,以最大化容器利用率为目标。为了解决这一NP-hard问题,该文在邓见凯、王磊提出的拟人型全局优化算法的基础上进行了深入的算法研究与改进。针对Strip Packing问题特点,提出了QHG(Quasi-Human Group)算法,其核心改进涵盖了多个方面,包括扩充初始点集合、删除和替换评价标准以及扩大邻域空间搜索范围。和单个局部极小值点的迭代相比,对局部极小值点集合进行迭代所生成布局优度更高,跳坑策略用于跳出局部极小值点,将搜索引向有希望的区域,优美度枚举有望进一步提高布局优度。通过这些措施,QHG算法更好地模拟人类决策过程,提高了全局搜索的效率。为评估QHG算法性能,对8组标准问题实例(C组、N组、NT组、CX组、NP组、ZDF组、2sp组、bwmv组)进行了大量实验。实验结果表明,QHG算法生成的布局优度优于当前国际文献中的几种较先进算法,展现了其在Strip Packing问题上的卓越性能。

Optimization algorithm based on kinetic-molecular theory

Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.

Optimizing combination of aircraft maintenance tasks by adaptive genetic algorithm based on cluster search

It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.

Grid-Based Path Planner Using Multivariant Optimization Algorithm

To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.

基于禁忌搜索的启发式算法求解球体Packing问题

为求解具有NP难度的球体Packing问题,通过将禁忌搜索方法与基于自适应步长的梯度下降法和二分法相结合,提出了一个启发式算法。对50个等球算例进行了实例测试,算法改进了其中44个算例的目前最优结果。大量的实例计算结果表明,该启发式算法是求解球体Packing问题的一个有效算法。

正三角形容器内等圆Packing问题的启发式算法

等圆Packing问题研究如何将n个单位半径的圆形物体互不嵌入地置入一个边长尽量小的正三角形容器内,作为一类经典的NP难度问题,其有着重要的理论价值和广泛的应用背景.模拟退火算法是一种随机的全局寻优算法,通过将启发式格局更新策略与基于梯度法的局部搜索策略融入模拟退火算法,并与二分搜索相结合,提出一种求解正三角形容器内等圆Packing问题的启发式算法.该算法将启发式格局更新策略用来产生新格局和跳坑,用梯度法搜索新产生格局附近能量更低的格局,并用二分搜索得到正三角形容器的最小边长.对41个算例进行测试的实验结果表明,文中算法改进了其中38个实例的目前最优结果,是求解正三角形容器内等圆Packing问题的一种有效算法.

基于禁忌搜索的启发式算法求解圆形packing问题

求解具有NP难度的圆形 packing问题具有很高的理论与实用价值 现提出一个有效的启发式方法 ,求解了货运中常遇到的矩形区域内的不等圆 packing问题 此算法首先将圆按给定的优先级分组 ,然后逐组地用拟物拟人法放置圆 ,并且在整个过程中利用了禁忌搜索法的思想 ,通过禁止重复前面已做的工作 ,使搜索能有效地逃离局部极小值的陷阱 ,提高了搜索效率 实验结果表明 。

基于格局变换策略的不等圆Packing问题求解算法

采用基于格局变换策略的算法ACP-Solver求解不等圆Packing问题。ACP-Solver由连续优化方法、格局变换算子和接收准则组成。连续优化方法可从任一初始格局收敛至对应的局部最优格局。格局变换算子将当前格局变换为新格局。接收准则决定是否接收变换所得格局。基于24个国际公开算例的计算实验表明,ACP-Solver能在可接受的计算时间内改进或持平绝大多数算例的当前最优记录。实验结果表明了ACP-Solver的高效性能。

一种求解Packing问题概率控制搜索行为的启发式算法

研究一种求解圆形和圆形与矩形混合Packing问题的启发式算法.借鉴Agent概念,赋予待布物具有跳跃、交换、旋转、移动和容器缩放等5种搜索行为,在寻优过程中以概率机制控制上述各搜索行为,并给出寻优过程中启用该搜索行为的时机及其操作顺序,该概率控制机制的适应性控制参数由待布物之间干涉信息决定.该法纯用上述搜索行为寻优,不辅以其他优化方法.该Packing问题数值实验结果表明,算法是可行和有效的.

一种求解二维矩形Packing问题的拟人型全局优化算法

针对二维矩形Packing问题,提出了基于占角动作的基本算法。以基本算法为基础,提出了三阶段优化的拟人型全局优化算法。在第一阶段生成初始布局。在第二阶段交替调用邻域搜索子程序和跳坑策略子程序对矩形块的优先级排序进行优化。邻域搜索采用交换式和插入式两种邻域结构,避免单一邻域结构的局限性。当搜索遇到局部最优解时,采用跳坑策略子程序跳出局部最优解,将搜索引向有希望的区域。在第三阶段调用优美度枚举子程序对占角动作的选择作进一步优化。提出了两条优度定理。对于六组benchmark测试用例的实验结果表明,算法的整体表现优于当前文献中的先进算法。针对矩形块方向固定的情形,算法对zdf6和zdf7两个问题实例得到了比已有文献记录更优的布局。

Robust optimization for volume variation in timber processing

Volume variation is an uncertainty element which affects timber processing. We studied the volume variation of logs caused by quality defects in traditional timber processing and set up an optimization approach,using a robust optimization method. We used total number of acceptable boards produced to study the relationship between board thickness and raw material logs, using a heuristic search algorithm to control the variation of board volume to improve the output of boards, reduce the quantity of by-products, and lower production costs. The robust optimization method can effectively control the impact of volume variations in timber processing, reduce cutting waste as far as possible using incremental processing and increase profits, maximize the utilization ratio of timber, prevent waste in processing, cultivate the productive type of tree species and save forest resources.

Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

A real-life problem is the rostering of nurses at hospitals.It is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization problem.Handling the real-world nurse rostering problem(NRP)constraints in distributing workload equally between available nurses is still a difficult task to achieve.The international shortage of nurses,in addition to the spread of COVID-19,has made it more difficult to provide convenient rosters for nurses.Based on the literature,heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity,especially for large rosters.Heuristic-based algorithms in general have problems striking the balance between diversification and intensification.Therefore,this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm(EHSA)with the simulated annealing(SA)algorithm called the annealing harmony search algorithm(AHSA).The AHSA is used to solve NRP from a Malaysian hospital.The AHSA performance is compared to the EHSA,climbing harmony search algorithm(CHSA),deluge harmony search algorithm(DHSA),and harmony annealing search algorithm(HAS).The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset.

A Sustainable WSN System with Heuristic Schemes in IIoT

Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more intelligent.However, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and charging.The main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.

A Rule Based Evolutionary Optimization Approach for the Traveling Salesman Problem

The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many existing algorithms, a traditional genetic algorithm will have limited success with this problem class, particularly as the problem size increases. A rule based genetic algorithm is proposed and demonstrated on sets of traveling salesman problems of increasing size. The solution character as well as the solution efficiency is compared against a simulated annealing technique as well as a standard genetic algorithm. The rule based genetic algorithm is shown to provide superior performance for all problem sizes considered. Furthermore, a post optimal analysis provides insight into which rules were successfully applied during the solution process which allows for rule modification to further enhance performance.

Hybrid Optimization Algorithm Based on Wolf Pack Search and Local Search for Solving Traveling Salesman Problem

Traveling salesman problem(TSP) is one of the typical NP-hard problems, and it has been used in many engineering applications. However, the previous swarm intelligence(SI) based algorithms for TSP cannot coordinate with the exploration and exploitation abilities and are easily trapped into local optimum. In order to deal with this situation, a new hybrid optimization algorithm based on wolf pack search and local search(WPS-LS)is proposed for TSP. The new method firstly simulates the predatory process of wolf pack from the broad field to a specific place so that it allows for a search through all possible solution spaces and prevents wolf individuals from getting trapped into local optimum. Then, local search operation is used in the algorithm to improve the speed of solving and the accuracy of solution. The test of benchmarks selected from TSPLIB shows that the results obtained by this algorithm are better and closer to the theoretical optimal values with better robustness than those obtained by other methods.

基于对称映射搜索策略的自适应金鹰算法及应用

金鹰优化算法(Golden Eagle Optimizer,GEO)是一种基于种群的元启发式算法,其模拟了金鹰的合作狩猎行为。针对GEO算法中存在的求解精度差和陷入局部最优等问题,文中提出了一种改进MERGEO(Mapped Elitist Reverse GEO)算法。在原算法基础上采用对称映射搜索策略、自适应精英策略和随机反向学习机制这3种方法平衡了算法的探索和开发阶段,获得了规避局部最优能力和较好的优化精度。在10个基准测试函数上对该算法进行独立策略有效性分析、可扩展性分析以及同其他算法的优化性能比较分析。实验结果表明,改进后的MERGEO算法具有较强的竞争力和良好的优化能力。将改进后的算法用于无线传感器网络的覆盖优化问题和压力容器设计问题研究,验证了其实际应用价值。

基于邻域互信息的组合预测最优子集选择算法

为在候选模型集中高效地选择时间序列组合预测的最优子集,提出一种CSPSO-NMI-MRMR最优子集选择算法。利用邻域互信息(neighborhood mutual information, NMI)度量相关性和冗余度,避免数值型数据的离散化,按最大相关最小冗余原则(minimal redundancy and maximal relevance, MRMR)筛选最优子集;邻域互信息中的邻域参数与子集选择效果密切相关,采用CSPSO算法寻找最优邻域参数,充分利用布谷鸟算法(cuckoo search, CS)和粒子群优化算法(particle swarm optimization, PSO)的优势,兼顾搜索效率和全局搜索能力;在寻参过程中设计一种淘汰策略,优化邻域参数的寻优区间并淘汰部分单模型,减少计算量。仿真结果表明,所提方法在预测精度、运行时间和稳健性上效果更优。

考虑伤员健康状态变化的紧急医疗救援路径优化

重大突发事件造成的伤员人数往往超出现场急救、转运和处理能力,邻近医院仅能提供有限的医疗救援服务,高效地转运不同受伤程度的伤员成为紧急医疗救援的关键。以全部伤员的健康状态总和最大为目标,考虑伤员病情随时间动态恶化的程度,提出了一种救护车与社会车辆共同救援的多车场、多车型、多周期紧急医疗救援路径优化模型,并以成都市的部分安置点和医院的实际数据为例,采用启发式邻域搜索算法对模型进行求解,验证了模型的有效性。结果表明:采用共同救援模式可有效提高伤员整体健康水平。研究结果可为重大突发事件下的伤员转运及通道保障提供决策参考。

面向商业物流的条烟快速组包优化

【背景和目的】由于不同条烟的规格差异化和订单方案的多样化,对烟草公司条烟组包工作造成较大困难。【方法】本文选取包数最小化、包型稳定性、包型规整度和拆包均衡性作为组包方案的目标指标,构建三维组包模型,采用包括改进蜜獾算法在内的6种启发式算法求解模型,确定出最优组包方案,满足企业高效组包需求。【结果】(1)本文搭建的三维组包模型适用于解决烟草物流中的复杂组包问题;(2)改进蜜獾算法(IHBA)的寻优效果显著,相比于粒子群算法(PSO),在最优值、最差值、平均值、平均迭代次数方面性能分别提高45%、7%、36.7%、52.1%;【结论】本文构建的最优组包系统能稳定快速地输出最优组包方案,且条烟规格和数量可以根据订单灵活调整,具有较强的适应性和可移植性。

智能化测量学教学辅助系统与组卷策略的设计及研究

针对当前计算机教学辅助系统中存在的组卷难度和题型比例不合理等问题,以测量学课程为例,提出了多约束条件的组合优化模型,解决了自动化组卷的多指标参数问题,为实现科学的组卷策略提供参考依据。该模型综合考虑试卷的总分、难度、曝光率、题型比例、章节知识量、培养目标等多个方面的要求进行量化加权,建立了智能化组卷的总约束方程,构建了多参数约束的组合优化模型。采用计算机模拟仿真的方法建立了题库,分析了三种启发式搜索算法求解模型的解算精度和效率。结果表明,利用遗传算法实现的自动化组卷的整体精度和效率更高,在总分、难度、曝光率、题型比例、章节知识量和培养目标方面的平均偏差分别为0.2%.0.074、0.1%6.1%、8.2%和9.6%,迭代次数在230次以内基本能够达到最优解。