A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.     

An improved ant colony optimization algorithm for nonlinear resource-leveling problems

The notion of using a meta-heuristic approach to solve nonlinear resource-leveling problems has been intensively studied in recent years. Premature convergence and poor exploitation are the main obstacles for the heuristic algorithms. Analyzing the characteristics of the project topology network, this paper introduces a directional ant colony optimization (DACO) algorithm for solving nonlinear resource-leveling problems. The DACO algorithm introduced can efficiently improve the convergence rate and the quality of solution for real-project scheduling.     

一种改进的机器人路径规划的蚁群算法

针对具有复杂回旋地形结构的机器人路径规划问题, 提出了一种改进的蚁群算法. 该算法引入自适应迁移概率函数实现蚁群具有正、反向运动的能力, 改善了算法的曲折迂回能力; 能见度信息中引入距离启发因素和障碍相交检测机制, 完成路径搜索与避障过程有机结合, 提高算法的搜索效率; 引入贪婪信息素更新策略和节点信息素分布, 降低了数据存储量, 改善了路径规划的效果和算法的收敛速度. 基于不同算法的比较仿真实验, 数值结果证实了该算法的有效性.     

An ant colony optimization algorithm for DNA sequencing by hybridization

The reconstruction of DNA sequences from DNA fragments is one of the most challenging problems in computational biology. In recent years the specific problem of DNA sequencing by hybridization has attracted quite a lot of interest in the optimization community. Several metaheuristics such as tabu search and evolutionary algorithms have been applied to this problem. However, the performance of existing metaheuristics is often inferior to the performance of recently proposed constructive heuristics. On the basis of these new heuristics we develop an ant colony optimization algorithm for DNA sequencing by hybridization. An important feature of this algorithm is the implementation in a so-called multi-level framework. The computational results show that our algorithm is currently a state-of-the-art method for the tackled problem.     

An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem

This paper presents an improved ant colony optimization algorithm (IACO) for solving mobile agent routing problem. The ants cooperate using an indirect form of communication mediated by pheromone trails of scent and find the best solution to their tasks guided by both information (exploitation) which has been acquired and search (exploration) of the new route. Therefore the premature convergence probability of the system is lower. The IACO can solve successfully the mobile agent routing problem, and this method has some excellent properties of robustness, self-adaptation, parallelism, and positive feedback process owing to introducing the genetic operator into this algorithm and modifying the global updating rules. The experimental results have demonstrated that IACO has much higher convergence speed than that of genetic algorithm (GA), simulated annealing (SA), and basic ant colony algorithm, and can jump over the region of the local minimum, and escape from the trap of a local minimum successfully and achieve the best solutions. Therefore the quality of the solution is improved, and the whole system robustness is enhanced. The algorithm has been successfully integrated into our simulated humanoid robot system which won the fourth place of RoboCup2008 World Competition. The results of the proposed algorithm are found to be satisfactory.     

一种面向对象的多角色蚁群算法及其TSP 问题求解

蚁群算法的改进大多从算法本身入手或与其他算法相结合,未充分利用待解决问题所包含的信息,提升效果较为有限.对此,提出一种面向对象的多角色蚁群算法.该算法充分利用旅行商问题(TSP)对象的空间信息,采用k-均值聚类将城市划分为不同类别;同时,对蚁群进行角色划分,不同角色的蚁群针对城市类别关系执行各自不同的搜索策略,增强了蚁群的搜索能力,较大幅度地提高了求解质量.每进行一次迭代,仅各角色最优个体进行信息素更新,防止算法退化为随机的贪婪搜索.将精英策略与跳出局部最优相结合可避免算法的停滞.50个经典TSP实例仿真实验表明:所提出的算法可以在较少的迭代次数内获得或非常接近于问题的已知最优解;对于大规模TSP问题所得结果也远超所对比的算法.     

一种家庭安保机器人运动路径规划的蚁群算法

由于家庭居住环境复杂,家庭安保机器人导航问题难于解决。使用传统蚁群算法,家庭安保机器人容易陷入搜索家庭环境局部极值的困境,无法找出在复杂环境下家庭最优的运动路径。因此,家庭安保机器人设计方案引入混沌理论改良局部个体的质量,利用混沌扰动,能够避免家庭机器人陷入搜索家庭环境局部极值的困境,由最初的混沌行为过渡到群体智能行为,使家庭安保机器人找到最优的运动路径。经仿真实验表明,在复杂的家庭环境下,家庭安保机器人也可以安全避障。     

An ant colony optimization algorithm for the bi-objective shortest path problem

Multi-objective shortest path problem (MOSP) is an extension of a traditional single objective shortest path problem that seeks for the efficient paths satisfying several conflicting objectives between two nodes of a network. MOSP is one of the most important problems in network optimization with wide applications in telecommunication industries, transportation and project management. This research presents an algorithm based on multi-objective ant colony optimization (ACO) to solve the bi-objective shortest path problem. To analyze the efficiency of the algorithm and check for the quality of solutions, experimental analyses are conducted. Two sets of small and large sized problems that generated randomly are solved. Results on the set problems are compared with those of label correcting solutions that is the most known efficient algorithm for solving MOSP. To compare the Pareto optimal frontiers produced by the suggested ACO algorithm and the label correcting algorithm, some performance measures are employed that consider and compare the distance, uniformity distribution and extension of the Pareto frontiers. The results on the set of instance problems show that the suggested algorithm produces good quality non-dominated solutions and time saving in computation of large-scale bi-objective shortest path problems.     

An adaptive ant colony optimization algorithm for constructing cognitive diagnosis tests

A critical issue in the applications of cognitive diagnosis models (CDMs) is how to construct a feasible test that achieves the optimal statistical performance for a given purpose. As it is hard to mathematically formulate the statistical performance of a CDM test based on the items used, exact algorithms are inapplicable to the problem. Existing test construction heuristics, however, suffer from either limited applicability or slow convergence. In order to efficiently approximate the optimal CDM test for different construction purposes, this paper proposes a novel test construction method based on ant colony optimization (ACO-TC). This method guides the test construction procedure with pheromone that represents previous construction experience and heuristic information that combines different item discrimination indices. Each test constructed is evaluated through simulation to ensure convergence towards the actual optimum. To further improve the search efficiency, an adaptation strategy is developed, which adjusts the design of heuristic information automatically according to the problem instance and the search stage. The effectiveness and efficiency of the proposed method is validated through a series of experiments with different conditions. Results show that compared with traditional test construction methods of CDMs, the proposed ACO-TC method can find a test with better statistical performance at a faster speed.     

针对混合变量优化问题的协同进化蚁群优化算法

针对由连续变量和分类变量构成的混合变量优化问题（MVOP）,采用协同进化策略来对混合变量决策空间进行搜索,提出了一种协同进化蚁群优化算法（CACOA_MV）。CACOA_MV分别采用连续和离散蚁群优化（ACO）策略生成连续和分类变量子种群,通过合作者来对连续和分类变量子向量进行评价,分别对连续和分类变量子种群进行更新来实现对混合变量决策空间的高效协同搜索。进一步地,利用信息素平滑机制增强对分类变量解空间的全局探索能力,并设计了一种面向协同进化框架的“最佳+随机合作者”的重启策略来提高协同搜索效率。与混合变量的蚁群（ACO_MV）算法和种群规模线性变小的差分进化-蚁群混合变量优化算法（L-SHADE_ACO）的比较表明,CACOA_MV能够进行更有效的局部开发,从而提高最终结果在目标空间中的近似精度;与基于集合的混合变量差分进化算法（DE_MV）相比较,CACOA_MV能够在决策空间中更好地逼近全局最优解,具有更好的全局探索能力。综上,采用协同进化机制的CACOA_MV能有效保持全局探索和局部开发的平衡,从而具有更好的寻优性能。     

一种求解函数优化的自适应蚁群算法

针对多极值连续函数优化问题,提出了一种自适应蚁群算法。该方法将解空间划分成若干子域,根据蚂蚁在搜索过程中所得解的分布状况动态的调节蚂蚁的路径选择策略和信息量更新策略,求出解所在的子域,然后在该子城内确定解的具体值。仿真结果表明谊算法具有不易陷入局部最优、解的精度高、收敛速度快、稳定性好等优点,其性能优于基本遗传算法以及克隆选择算法。     

求解多目标优化问题的改进蚁群算法

蚁群算法是一种模拟蚂蚁行为进行优化的启发式优化算法,该算法在许多领域已经得到应用.针对多目标优化问题优化与求解较困难的问题,提出一种嵌入变尺度算法的改进蚁群算法用于求解,为蚁群算法在连续空间中的应用提供了怂一个可行的方案.给出了该算法的详细定义及实现步骤,实例仿真表明,该算法能加快收敛速率,对连续空间的蚁群算法研究具有重要的意义.     

多目标函数优化的元胞蚂蚁算法

提出一种求解多目标函数优化的元胞蚂蚁算法．该方法将元胞自动机演化规则引入蚂蚁算法,给出了在连续空间多目标函数优化的算法描述,定义了与蚂蚁信息素释放有关的元胞演化规则及蚂蚁邻域的转移概率,并实现了算法的具体步骤．在Matlab环境下,采用该算法对一些典型的测试函数进行求解和验证．实验结果表明,该方法具有向真实的Pareto前沿逼近的效果,是一种求解多目标优化的有效方法．     

改进的蚁群优化算法求解旅行商问题

针对基本蚁群算法在求解旅行商问题时表现的停滞和早熟现象,提出一种带遗忘因子的蚁群优化算法。通过在人工蚂蚁中加入遗忘因子,建立新的状态转移公式,修改信息素更新策略,蚂蚁按照基本蚁群算法的搜索方式工作,结合当前解的最优值误差率,对状态转移方程进行调整,新公式可用于降低最优值误差、提高最优值跟踪能力、修正路径评价模型、计算每条路径到当前最优解的概率。对TSP实例的仿真结果表明,改进算法耗时更短,路径寻优结果更优。     

基于划分的蚁群算法求解货物权重车辆路径问题

考虑单产品分销网络中的车辆路径问题(VRP:vehicle routing problem).与以往诸多研究不同的是,建立了一种带货物载重量的VRP模型(weighted VRP),即车辆在两个顾客之间行驶时的载重量也作为影响运输费用的一个因素考虑.因此,需求量较大的顾客拥有较高的车辆运输优先权.在分析了问题性质的基础上,提出一种基于划分策略的蚁群算法PMMAS求解货物权重车辆路径问题,并与其他常用的启发式算法进行比较分析,表明了算法的有效性.     

简化蚁群算法

针对最大最小蚂蚁系统中信息素下界难以确定以及算法性能易受同构问题影响的缺点,提出一种简化蚁群算法.信息素的上下界被限制在一个固定的区间内,不随目标函数值的更新而改变;信息素的更新量是一个与具体目标函数值无关的常数.所提出的简化算法不仅具有强不变性和平移不变性,而且算法的性能不受信息素下界的影响.针对旅行商问题的仿真实验验证了改进算法的可行性和有效性.     

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.

一种解决访存调度问题的改进蚁群优化算法

存储器的访问调度策略是复杂的,不仅仅要考虑具体的电路时序参数,还有访存节拍数。在分析DRAM的特点以及访存调度策略的基础上,考虑DDR3时序规范,提出一种改进的蚁群优化访问调度策略。采用不同的trace作为测试,同贪婪式调度算法作比较,该算法可以有效降低平均总延迟、提高带宽利用率。     

基于细菌觅食的改进蚁群算法

蚁群算法是模仿蚂蚁觅食行为的一种新的仿生学智能优化算法。针对其收敛速度慢和易陷入局部最优的不足,将细菌觅食算法和蚁群算法相结合,提出一种细菌觅食 蚁群算法。在蚁群算法迭代过程中,引入细菌觅食算法的复制操作,以加快算法的收敛速度;引入细菌觅食算法的趋向操作,以增强算法的全局搜索能力。通过经典的旅行商问题和函数优化问题测试表明,细菌觅食 蚁群算法在寻优能力、可靠性、收敛效率和稳定性方面均优于基本蚁群算法及两种改进蚁群算法。     

用于多维函数优化的实数编码量子蚁群算法*

基于量子计算理论及蚂蚁群体寻优策略,提出了一种用于连续优化问题的新方法——实数编码量子蚁群算法（RQACOA）。针对量子比特编码和二进制编码在连续优化问题上的不足,引入一种新的实数编码表示方法,设计了智能量子蚂蚁,一条染色体携带指定范围内的多个个体信息。智能量子蚂蚁利用量子态纠缠和相干机理,通过叠加、变异及自学习来完成前期进化过程,然后以蚂蚁群体智能寻优方式进一步求解。实验结果表明,该算法具有强的全局寻优能力及快速搜索能力。