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.     

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 algorithm for continuous optimization: application to feed-forward neural network training

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.

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.     

基于蚁群优化算法的BP神经网络的RPROP混合算法仿真的研究

本文对蚁群优化算法的BP神经网络中的RPROP混合算法进行了研究,提出了利用蚁群优化算法,结合RPROP混合算法解决无线网络传感器中如何处理信息服务点中大量的冗余数据、网络运行速度等相关问题,通过建立系统构架及信息服务点,证明该算法能够延长BP神经网络的生命周期,加快BP神经网络的收缩速度,能够将网络中信息服务点的重复数据进行有效的合并处理,并及时过滤掉非正常信息服务点的数据,减少数据服务点的能量消耗,期训练过程中迭代次数改善明显,解决BP神经网络的学习、训练时间冗余等问题,同时具有较强的计算、寻优等能力,提高了网络分类正确率和运行的效率,是一种较为实用的算法,完全能够满足日益增长的无线互联网终端的运行需要。     

Subdomain generation using emergent ant colony optimization

Finite elements mesh decomposition is a well known optimization problem and is used to split a computationally expensive finite elements mesh into smaller subdomains for parallel finite elements analysis.The ant colony optimization is a type of algorithm that seeks to model the emergent behaviour observed in ant colonies and utilize this behaviour to solve combinatorial problems. This technique has been applied to several problems, most of which are graph related because the ant colony metaphor can be most easily applied to such types of problems. This paper examines the application of ant colony optimization algorithm to the partitioning of unstructured adaptive meshes for parallel explicit time-stepping finite elements analysis.The concept of ant colony optimization technique in addition to the notion of swarm intelligence for finding approximate solutions to combinatorial optimization problems is described. This algorithm combines the features of the classical ant colony optimization technique with swarm intelligence to form a model which is an artificial system designed to perform a certain task.The application of the ant colony optimization for partitioning finite elements meshes based on triangular elements using the swarm intelligence concept is described. A recursive greedy algorithm optimization method is also presented as a local optimization technique to improve the quality of the solutions given by the ant colony optimization algorithm. The partitioning is based on the recursive bisection approach.The mesh partitioning is carried out using normal and predictive modes for which the predictive mode uses a trained multi-layered feedforward neural network that estimates the number of triangular elements that will be generated after finite elements mesh generation is carried out.The performance of the proposed hybrid approach for the recursive bisection of finite elements meshes is examined by decomposing two mesh examples and comparing them with a well known finite elements domain decomposer.     

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.     

双向反馈蚁群算法在网络负载均衡问题的研究

针对网络资源管理中的负载均衡与优化问题,提出一种双向反馈蚁群算法,用蚂蚁数量代表网络资源流量,通过蚂蚁间信息素的相互作用和动态控制来实现网络流量分担到多条可用路径。将蚁群算法扩展为双向反馈的蚁群算法,蚂蚁判断各条路径上的信息素浓度的同时,考虑可选链路的负载情况,决定选择要走路径,使得蚂蚁相对均衡地分布在可选链路上。仿真实验结果表明,双向反馈蚁群算法比原蚁群算法在缩短自适应时间,减少丢包率,提高负载均衡效率方面都具有更好的性能。     

Inducing decision trees with an ant colony optimization algorithm

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. The proposed algorithm is compared against three decision tree induction algorithms, namely C4.5, CART and cACDT, in 22 publicly available data sets. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of both C4.5 and CART, which are well-known conventional algorithms for decision tree induction, and the accuracy of the ACO-based cACDT decision tree algorithm.     

基于改进蚁群算法的铁路路网最优路径规划

多条件最优路径规划问题是铁路出行查询系统的重要功能之一。将路径规划问题转化为以用户多种条件组合为目标函数的最优化问题,并将改进的蚁群算法应用于该问题,使查询系统能够满足各类用户的查询要求,并给出最优解或次优解。仿真实验表明：该算法的实时性很高,是一种行之有效的方法。     

蚁群优化神经网络的网络流量混沌预测

为了网络流量预测准确性,提出一种蚁群算法（ACO）优化BP神经网络（BPNN）的网络流量混沌预测模型（ACO-BPNN）。对网络流量时间序列进行重构,将BPNN参数作为蚂蚁的位置向量,通过蚁群信息交流和相互协作找到BPNN最优参数,建立网络流量最优预测模型,并采用实测网络流量数据进行有效性验证。结果表明,ACO-BPNN能够准确刻画网络流量变化特性,提高网络流量的预测准确性。     

基于人工蜂群的连续域蚁群优化算法

连续域蚁群优化算法是蚁群优化算法的一个重要研究方向,针对连续域蚁群优化算法(ACOR)计算时间较长、易陷入局部最优的问题,提出了一种基于人工蜂群的连续域蚁群优化算法(ABCACOR)。首先,引入一种替代机制来选择指导解,以替换原来的基于排序的选择方式,目的是节约计算时间和尽可能地保持搜索的多样性;其次,结合人工蜂群算法的搜索策略来提高算法的全局搜索能力,进一步减少计算时间和提高求解精度。通过对大量的测试函数进行仿真实验,结果表明,ABC-ACOR算法较现有的一些连续域蚁群算法具有更好的寻优能力。     

啤酒原料配方的蚁群优化设计研究

啤酒配方优化是提高啤酒企业生产效率的重要途径。但对于配方优化问题,传统的数学优化方法实现较为复杂,缺乏全局最优解搜索的鲁棒性。蚁群算法目前多用于组合优化问题,但它在演化过程中有收敛慢、耗时长的缺点。因此,提出了变尺度蚁群算法,在迭代过程中不断收缩蚂蚁的搜索范围以提高优化效率。并研究了变尺度蚁群算法在啤酒配方优化中的应用,在满足生产指标前提下,实现配方的原料总成本最低。其应用结果表明：针对啤酒配方优化这类连续域问题,变尺度蚁群算法具有更强的全局搜索能力和鲁棒性,并易于实现,具有实际应用价值。     

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.     

多目标同时取送货车辆路径问题的改进蚁群算法

为使同时取送货车辆路径问题(vehicle routing problem with simultaneous pickup and delivery, VRPSPD)的运输成本和各路径间最大长度差最小化,建立同时考虑车辆容量和距离约束的VRPSPD双目标模型,通过软件测试验证了模型准确性.针对问题的特点构造一个嵌入禁忌表、且具有贪婪转移准则的多目标蚁群算法,对蚂蚁产生的解执行多目标迭代局部搜索程序,以在多个邻域上优化该解或产生新的Pareto解.采用响应曲面法拟合算法参数对目标值影响的数学关系,确定最优参数组合.用该算法求得文献中12组Solomon算例的Pareto解集,并以绝对偏向最小化总成本的解与文献中仅最小化总成本的几种算法的计算结果进行比较,结果表明算法可求得权衡各目标且使单一目标近似最优的Pareto解.     

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.     

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。     

An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times

This study introduces the problem of minimizing average relative percentage of imbalance (ARPI) with sequence-dependent setup times in a parallel-machine environment. A mathematical model that minimizes ARPI is proposed. Some heuristics, and two metaheuristics, an ant colony optimization algorithm and a genetic algorithm are developed and tested on various random data. The proposed ant colony optimization method outperforms heuristics and genetic algorithm. On the other hand, heuristics using the cumulative processing time obtain better results than heuristics using setup avoidance and a hybrid rule in assignment.     

用蚁群算法求解带平衡约束的圆形布局问题

采用启发式方法结合演化算法的思路求解带平衡约束的圆形布局问题.首先对传统优化模型进行调整,并探讨了调整的合理性;然后设计一种分步定位的布局方法,在此基础上利用蚁群算法寻优;最后利用局部搜索技术,在传统模型意义下对布局进行了改进.数值实验表明,算法的性能比目前已有的结果有较大的提高.     

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.