Ant colony optimisation with parameterised search space for the job shop scheduling problem

The job-shop scheduling problem (JSSP) is known to be NP-hard. Due to its complexity, many metaheuristic algorithm approaches have arisen. Ant colony metaheuristic algorithm, lately proposed, has successful application to various combinatorial optimisation problems. In this study, an ant colony optimisation algorithm with parameterised search space is developed for JSSP with an objective of minimising makespan. The problem is modelled as a disjunctive graph where arcs connect only pairs of operations related rather than all operations are connected in pairs to mitigate the increase of the spatial complexity. The proposed algorithm is compared with a multiple colony ant algorithm using 20 benchmark problems. The results show that the proposed algorithm is very accurate by generating 12 optimal solutions out of 20 benchmark problems, and mean relative errors of the proposed and the multiple colony ant algorithms to the optimal solutions are 0.93% and 1.24%, respectively.     

Scatter search heuristic for least-cost design of water distribution networks

The optimization problems of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. Heuristic algorithms such as genetic algorithms, simulated annealing, tabu search and ant colony optimization have been extensively employed over the last decade. This article proposed an optimization procedure based on the scatter search (SS) framework, which is also a heuristic algorithm, to obtain the least-cost designs of three well-known looped water distribution networks (two-loop, Hanoi and New York networks). The computational results obtained with the three benchmark instances indicate that SS is able to find solutions comparable to those provided by some of the most competitive algorithms published in the literature.     

基于改进蚁群算法的二维MUSIC多谱峰搜索研究

针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,加上动态调整搜索范围,使得改进后的蚁群算法可以进行二维MUSIC...     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization,BFACO）算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。     

Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series–parallel systems

The redundancy allocation problem (RAP) is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system reliability given various system-level constraints. As telecommunications and internet protocol networks, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. This paper presents an efficient algorithm to solve this reliability optimization problem. The idea of a heuristic approach design is inspired from the ant colony meta-heuristic optimization method and the degraded ceiling local search technique. Our hybridization of the ant colony meta-heuristic with the degraded ceiling performs well and is competitive with the best-known heuristics for redundancy allocation. Numerical results for the 33 test problems from previous research are reported and compared. The solutions found by our approach are all better than or are in par with the well-known best solutions.     

基于蚁群算法的资源均衡优化决策及其MATLAB实现

资源均衡问题已被证明属于组合优化中的NP-hard问题,随着网络计划的复杂化,传统的数学规划法和启发式算法已很难解决该问题。本文以各种资源标准差的加权之和作为衡量资源均衡的评价指标,建立了资源均衡优化决策的数学模型,其次,自行设计蚁群算法步骤,利用Matlab编程进行实现,将蚂蚁随机分布在可行域中,蚂蚁根据转移概率进行全局搜索或局部搜索,经迭代求解资源平衡的全局最优和对应的各工序的开始工作时间,最后使用单资源均衡和多资源均衡两个算例对算法进行了测试,验证了该算法的有效性。     

Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem

This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.     

用于多目标优化的蚁群算法的构建及其应用

从蚁群的生物学行为出发,将成群募集和海量募集两种机制融入蚁群算法,并针对多目标优化的特性,综合考虑解的被支配度和分散度,抽提出一种启发式规则,用以评价食物源的优劣,进而构建多目标连续蚁群优化算法(MO-CACO).通过两个多目标典型函数的优化测试,验证了MO-CACO具有较强的多目标全局寻优能力,且稳健性良好,所求得的最优解集的多目标值能均匀地逼近Pareto最优前沿的各部分.将MO-CACO用于二甲苯异构化装置的操作优化,取得了满意的结果,MO-CACO可为化工过程多目标决策提供支持.     

An ant colony system-based hybrid algorithm for square root concave cost transhipment problems

Concave cost transhipment problems are difficult to optimally solve for large-scale problems within a limited period of time. Recently, some modern meta-heuristics have been employed for the development of advanced local search based or population-based stochastic search algorithms that can improve the conventional heuristics. Besides these meta-heuristics, the ant colony system algorithm is a population-based stochastic search algorithm which has been used to obtain good results in many applications. This study employs the ant colony system algorithm, coupled with some genetic algorithm and threshold accepting algorithm techniques, to develop a population based stochastic search algorithm for efficiently solving square root concave cost transhipment problems. The developed algorithms are evaluated with a number of problem instances. The results indicate that the proposed algorithm is more effective for solving square root concave cost transhipment problems than other recently designed local search based algorithms and genetic algorithm.     

A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks

The multistage hybrid flow-shop scheduling problem with multiprocessor tasks has been found in many practical situations. Due to the essential complexity of the problem, many researchers started to apply metaheuristics to solve the problem. In this paper, we address the problem by using particle swarm optimization (PSO), a novel metaheuristic inspired by the flocking behaviour of birds. The proposed PSO algorithm has several features, such as a new encoding scheme, an implementation of the best velocity equation and neighbourhood topology among several different variants, and an effective incorporation of local search. To verify the PSO algorithm, computational experiments are conducted to make a comparison with two existing genetic algorithms (GAs) and an ant colony system (ACS) algorithm based on the same benchmark problems. The results show that the proposed PSO algorithm outperforms all the existing algorithms for the considered problem.     

Design of high-performing hybrid meta-heuristics for unrelated parallel machine scheduling with machine eligibility and precedence constraints

This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.     

单线超市车辆配置与路径问题优化研究

为解决工位对物料需求紧迫程度不同,进而影响配送优先顺序的问题,提出考虑工位配送优先级的单线超市车辆配置及配送路径优化模型和求解算法。首先,建立以物料需求紧迫系数表示配送优先顺序,以单线超市车辆配置最少与路径最短为目标的数学模型;其次,提出加入控制搜索因子的改进蚁群算法对该问题进行求解,通过在迭代不同时期设置不同大小的控制搜索因子来改变节点选择概率,提高最优解搜索速度的同时避免算法陷入局部最优;最后,通过算例分析,验证了模型的正确性,同时也证明了改进蚁群算法能够实现对物料需求紧迫程度高的工位优先配送,较基本蚁群算法在计算结果及算法运行时间上具有优势。     

霍夫变换耦合蚁群优化图像边缘提取算法

目的为解决图像边缘提取方法中由于噪声浸染导致边缘定位精确度降低、边缘信息丢失和虚假边缘等不足,提出基于霍夫变换(HT)耦合蚁群优化(ACO)图像边缘的提取方法。方法对输入图像进行霍夫变换,消除噪声和线段间隔对图像边缘的影响;计算图像像素梯度和像素圆形邻域统计均值的差值,构建二者之间的权重函数,并作为蚁群的信息素和启发信息;利用蚁群优化算法,引导蚁群搜索图像边缘,完成图像边缘提取。结果实验表明,与当前边缘提取技术相比,文中算法具有更高的提取精度与效率,可获取完整、细节丰富的边缘,有效地降低了噪声影响。结论所提算法具有较强的抗噪性能,能进一步改善边缘提取精度,能够较好地用于包装条码识别与图像处理领域。     

基于改进蚁群算法的码垛机器人路径规划应用研究

目的为解决蚁群算法在码垛机器人路径规划中存在的收敛速度慢、容易陷入局部最优等问题,提出一种人工势场和蚁群算法相结合的方法。方法首先,根据码垛机器人机械手在人工势场中不同节点所受到的合力,对初始信息素进行不均匀分布,以解决蚁群算法初期由于缺乏信息素导致的无效路径搜索。其次,在启发函数的设计中引入码垛机器人机械手在下一节点所受到的合力,以解决蚁群算法容易陷入局部最优的问题。最后,对信息素的更新策略进行改进。按照寻得路径的长度不同,对每次迭代完成后信息素的增量成比例进行更新,并设置最大、最小值,以解决迭代后期路径上信息素过大而使蚁群算法陷入局部最优的问题。结果改进后的蚁群算法收敛速度提升了约51%,寻找到的最短路径提升了约10%。和其他改进的蚁群算法相比,在综合性能上也有一定程度上的提高。结论改进后的蚁群算法收敛更快,寻找的最优路径更短。     

带时间窗的同时取送货车辆路径问题求解算法

为了整合物流配送过程的退货与送货服务,依据实际情况建立带时间窗的同时取送货车辆路径规划模型,设计一种基于K-means聚类处理的Q-Leaning自启发式蚁群算法解决此类问题。根据配送服务的特性,在基本的K-means算法上作相应的改进,同时提高蚁群算法的局部搜索能力,完成两算法的合理衔接。选用相关文献数据和标准算例进行实验,验证所提算法具有较好的性能,可以解决所描述的此类问题。     

Reliability optimization using multiobjective ant colony system approaches

The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages.     

Non-dominated archiving multi-colony ant algorithm for multi-objective optimization: Application to multi-purpose reservoir operation

Multi-objective optimization using heuristic methods has been established as a subdiscipline that combines the fields of heuristic computation and classical multiple criteria decision making. This article presents the Non-dominated Archiving Ant Colony Optimization (NA-ACO), which benefits from the concept of a multi-colony ant algorithm and incorporates a new information-exchange policy. In the proposed information-exchange policy, after a given number of iterations, different colonies exchange information on the assigned objective, resulting in a set of non-dominated solutions. The non-dominated solutions are moved into an offline archive for further pheromone updating. Performance of the NA-ACO is tested employing two well-known mathematical multi-objective benchmark problems. The results are promising and compare well with those of well-known NSGA-II algorithms used in real-world multi-objective-optimization problems. In addition, the optimization of reservoir operating policy with multiple objectives (i.e. flood control, hydropower generation and irrigation water supply) is considered and the associated Pareto front generated.     

求解连续空间优化问题的扩展粒子蚁群算法

扩展蚁群算法是蚁群算法创始人Dorigo提出的一种用于求解连续空间优化问题的最新蚁群算法,但该算法的收敛速度参数和局部搜索参数取值缺乏理论指导,因此其性能受算法参数影响较大.本文提出一种求解连续空间优化的扩展粒子蚁群算法,将粒子群算法嵌入到扩展蚁群算法中用于在线优化扩展蚁群算法参数,减少了参数人为调整的盲目性.从而改善扩展蚁群算法的寻径行为.通过将本文提出的算法与遗传算法、克隆选择算法、蚁群算法、扩展蚁群算法对5种典型测试函数优化的结果对比表明,本文算法在搜索速度和全局搜索能力方面均优于其它算法.     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.