基于免疫蚂蚁算法的Job-shop调度问题

描述了作业调度问题，借鉴生物免疫机理提出了求解车间调度问题的免疫蚁群算法，该方法在蚂蚁搜索程中，运用免疫机理提取疫苗，并对进化种群进行免疫操作，从而有效地抑制了蚁群算法的“早熟”和搜索效率低下的问题，显著地提高了蚁群算法对全局最优解的搜索能力和收敛速度，给出了免疫蚁群算法的具体步骤，并对算法进行了实例验证。     

基于资源状态蚁群算法的云计算任务分配

针对云计算中的任务分配问题,分析任务资源之间的数学模型,提出一种基于资源状态蚁群算法,相对一般蚁群算法,加入虚拟机实时状态,更精确地表达云计算任务分配的问题.通过CloudSim工具设计仿真实验,实验结果表明,与最近Cristian Mateos提出的蚁群改进算法相比,该算法在任务完成时间、算法稳定收敛方面取得了较好表现,以RR算法为基准,该算法提高后的时间比例稳定在RR算法任务完成时间的60％～65％,稳定性提高4.7倍.     

Diversity control in ant colony optimization

Optimization inspired by cooperative food retrieval in ants has been unexpectedly successful and has been known as ant colony optimization (ACO) in recent years. One of the most important factors to improve the performance of the ACO algorithms is the complex trade-off between intensification and diversification. This article investigates the effects of controlling the diversity by adopting a simple mechanism for random selection in ACO. The results of computer experiments have shown that it can generate better solutions stably for the traveling salesmen problem than AS_rank which is known as one of the newest and best ACO algorithms by utilizing two types of diversity.     

Solving software project scheduling problems with ant colony optimization

Software project scheduling problem (SPSP) is one of the important and challenging problems faced by the software project managers in the highly competitive software industry. As the problem is becoming an NP-hard problem with the increasing numbers of employees and tasks, only a few algorithms exist and the performance is still not satisfying. To design an effective algorithm for SPSP, this paper proposes an ant colony optimization (ACO) approach which is called ACS-SPSP algorithm. Since a task in software projects involves several employees, in this paper, by splitting tasks and distributing dedications of employees to task nodes we get the construction graph for ACO. Six domain-based heuristics are designed to consider the factors of task efforts, allocated dedications of employees and task importance. Among these heuristic strategies, the heuristic of allocated dedications of employees to other tasks performs well. ACS-SPSP is compared with a genetic algorithm to solve the SPSP on 30 random instances. Experimental results show that the proposed algorithm is promising and can obtain higher hit rates with more accuracy compared to the previous genetic algorithm solution.     

基于聚类分析的增强型蚁群算法

针对蚁群算法存在的早熟收敛、搜索时间长等不足,提出一种增强型蚁群算法.该算法构建了一优解池,保存到当前迭代为止获得的若干优解,并提出一种基于邻域的聚类算法,通过对优解池中的元素聚类,捕获不同的优解分布区域.该算法交替使用不同簇中的优解更新信息素,兼顾考虑了搜索的强化性和分散性.针对典型的旅行商问题进行仿真实验,结果表明该算法获得的解质量高于已有的蚁群算法.     

Integrated process planning and scheduling by an agent-based ant colony optimization

This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm.     

An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups

In many real-world production systems, it requires an explicit consideration of sequence-dependent setup times when scheduling jobs. As for the scheduling criterion, the weighted tardiness is always regarded as one of the most important criteria in practical systems. While the importance of the weighted tardiness problem with sequence-dependent setup times has been recognized, the problem has received little attention in the scheduling literature. In this paper, we present an ant colony optimization (ACO) algorithm for such a problem in a single-machine environment. The proposed ACO algorithm has several features, including introducing a new parameter for the initial pheromone trail and adjusting the timing of applying local search, among others. The proposed algorithm is experimented on the benchmark problem instances and shows its advantage over existing algorithms. As a further investigation, the algorithm is applied to the unweighted version of the problem. Experimental results show that it is very competitive with the existing best-performing algorithms.     

An algorithm based on ant colony optimization for the minimum connected dominating set problem

Ant colony optimization is a well established metaheuristic from the swarm intelligence field for solving difficult optimization problems. In this work we present an application of ant colony optimization to the minimum connected dominating set problem, which is an NP-hard combinatorial optimization problem. Given an input graph, valid solutions are connected subgraphs of the given input graph. Due to the involved connectivity constraints, out-of-the-box integer linear programming solvers do not perform well for this problem. The developed ant colony optimization algorithm uses reduced variable neighborhood search as a sub-routine. Moreover, it can be applied to the weighted and to the non-weighted problem variants. An extensive experimental evaluation presents the comparison of our algorithm with the respective state-of-the-art techniques from the literature. It is shown that the proposed algorithm outperforms the current state of the art for both problem variants. For comparison purposes we also develop a constraint programming approach based on graph variables. Even though its performance deteriorates with growing instance size, it performs surprisingly well, solving 315 out of 481 considered problem instances to optimality.     

Scalable platforms using ant colony optimization

Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.     

An ant colony optimization approach to a permutational flowshop scheduling problem with outsourcing allowed

This paper deals with the scheduling problem of minimizing the makespan in a permutational flowshop environment with the possibility of outsourcing certain jobs. It addresses this problem by means of the development of an ant colony optimization-based algorithm. This new algorithm, here named as flowshop ant colony optimization is composed of two combined ACO heuristics. The results show that this new approach can be used to solve the problem efficiently and in a short computational time.     

置换流水车间调度的蚁群优化算法

提出了一种求解置换流水车间调度的蚁群优化算法。该算法的要点是结合了NEH启发式算法和蚁群优化方法。理论论证和对置换流水车间调度问题的基准测试表明了该算法的有效性。     

On solving permutation scheduling problems with ant colony optimization

A new approach for solving permutation scheduling problems with ant colony optimization (ACO) is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ACO algorithm for the single-machine total weighted deviation problem. In the new approach the ants allocate the places in the schedule not sequentially, as in the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown by experiments that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well.     

MSDP with ACO: A maximal SRLG disjoint routing algorithm based on ant colony optimization

Failure resilience is a desired feature in communication networks, and different methods can be considered in order to achieve this feature. One of these methods is diverse Routing. In this paper, we are going to suggest a sort of diverse routing algorithm, which can find two maximal shared risk link group (SRLG) disjoint paths between a source and a destination node. This algorithm is based on ant colony optimization algorithm, which consists of three parts. These parts are graph transformation technique, finding two maximal edge-disjoint routes and reverse transformation. The final routes are always maximal SRLG disjoint. Simulation results show the efficiency of the proposed method.     

Mechanical assembly planning using ant colony optimization

In mechanical assembly planning research, many intelligent methods have already been reported over the past two decades. However, those methods mainly focus on the optimal assembly solution search while another important problem, the generation of solution space, has received little attention. This paper proposes a new methodology for the assembly planning problem. On the basis of a disassembly information model which has been developed to represent all theoretical assembly/disassembly sequences, two decoupled problems, generating the solution space and searching for the best result, are integrated into one computation framework. In this framework, using an ant colony optimization algorithm, the solution space of disassembly plans can be generated synchronously during the search process for best solutions. Finally, the new method’s validity is verified by a case study.     

An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting

This paper proposes several novel hybrid ant colony optimization (ACO)-based algorithms to resolve multi-objective job-shop scheduling problem with equal-size lot splitting. The main issue discussed in this paper is lot-splitting of jobs and tradeoff between lot-splitting costs and makespan. One of the disadvantages of ACO is its uncertainty on time of convergence. In order to enrich search patterns of ACO and improve its performance, five enhancements are made in the proposed algorithms including: A new type of pheromone and greedy heuristic function; Three new functions of state transition rules; A nimble local search algorithm for the improvements of solution quality; Mutation mechanism for divisive searching; A particle swarm optimization (PSO)-based algorithm for adaptive tuning of parameters. The objectives that are used to measure the quality of the generated schedules are weighted-sum of makespan, tardiness of jobs and lot-splitting cost. The developed algorithms are analyzed extensively on real-world data obtained from a printing company and simulated data. A mathematical programming model is developed and paired-samples t-tests are performed between obtained solutions of mathematical programming model and proposed algorithms in order to verify effectiveness of proposed algorithms.     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

一种基于GPU加速的细粒度并行蚁群算法

为改善蚁群算法对大规模旅行商问题的求解性能,提出一种基于图形处理器(GPU)加速的细粒度并行蚁群算法.将并行蚁群算法求解过程转化为统一计算设备架构的线程块并行执行过程,使得蚁群算法在GPU中加速执行.实验结果表明,该算法能提高全局搜索能力,增大细粒度并行蚁群算法的蚂蚁规模,从而提高了算法的运算速度.     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

Structural topology optimization using ant colony optimization algorithm

The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design.     

满足情感蚁群的服务组合优化方法

在服务计算模式下,通过引入情感来改进服务组合效率。首先,建立一种满足行为分析的情感感知空间,并定义认知来推理情感变化情况,使情感与认知能有效的映射。同时,定义了情感衰减和更新机制来保持情感变化稳定性。其次,将所建立的情感机制引入蚁群算法中,形成一种满足情感变化的蚁群算法,并将该算法应用到服务组合中实现优化。最后在Web服务建模本体(WSMO)下提供的VTA中实验表明,该方法有效且可行。