一种新型求解多序列比对问题的方法

多序列比对问题是生物信息科学中一个非常重要且具挑战性的课题,并已经被证明属于问题.为了克服以往算法中的求解速度慢的缺点,本文提出了一种基于遗传算法和蚁群算法的算法来求解的新方法,在单独使用遗传算法的基础上再使用蚁群算法来进行局部搜索以便更快速地求得解.实验结果表明,遗传-蚁群算法能有效地求解多序列比对问题.     

求解多重序列比对问题的蚁群算法

多重序列比对是生物信息学特别是生物序列分析中一个重要的基本操作。提出求解多重序列比对问题的蚁群算法,利用人工蚂蚁逐个选择各个序列中的字符进行配对。在算法中,蚂蚁根据信息素、字符匹配得分以及位置偏差等信息决定选择各序列中字符的概率,通过信息素的更新与调节相结合的策略较为有效地解决了局部收敛的问题,加强了算法寻求全局最优解的能力。另外在该算法的基础上,提出了基于分治策略的多序列比对蚁群求解算法,不但减少了原算法的计算时间,而且显著改善了算法所求得的解的质量。     

On the design of hybrid bio-inspired meta-heuristics for complex multiattribute vehicle routing problems

This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta-heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as main approach and GA as local search. GA-ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO-GA approach outperforms the others. Considering the cost, ACO, GA, and GA-ACO provide better results. Regarding computation time, GA and GA-ACO have been found the most competitive among the benchmark.     

Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks

This paper presents a new hybrid algorithm, which executes ant colony optimization in combination with genetic algorithm (ACO-GA), for type I mixed-model assembly line balancing problem (MMALBP-I) with some particular features of real world problems such as parallel workstations, zoning constraints and sequence dependent setup times between tasks. The proposed ACO-GA algorithm aims at enhancing the performance of ant colony optimization by incorporating genetic algorithm as a local search strategy for MMALBP-I with setups. In the proposed hybrid algorithm ACO is conducted to provide diversification, while GA is conducted to provide intensification. The proposed algorithm is tested on 20 representatives MMALBP-I extended by adding low, medium and high variability of setup times. The results are compared with pure ACO pure GA and hGA in terms of solution quality and computational times. Computational results indicate that the proposed ACO-GA algorithm has superior performance.     

A novel method for alignment of two nucleic acid sequences using ant colony optimization and genetic algorithms

We describe a new method for pairwise nucleic acid sequence alignment that can also be used for pattern searching and tandem repeat searching within a nucleic acid sequence. The method is broadly a hybrid algorithm employing ant colony optimization (ACO) and the simple genetic algorithm. The method first employs ACO to obtain a set of alignments, which are then further processed by an elitist genetic algorithm, which employs primitive selection and a novel multipoint crossover-mutation operator to generate accurate alignments. The resulting alignments show a fair amount of accuracy for smaller and medium size sequences. Furthermore, this algorithm can be used rather quickly and efficiently for aligning shorter sequences and also for pattern searching in both nucleic acid and amino acid sequences. Furthermore, it can be used as an effective local alignment method or as a global alignment tool. On improvement of accuracy, this method can be extended for use towards multiple sequence alignment.     

A feature selection method based on modified binary coded ant colony optimization algorithm

Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.     

基于蚁群遗传算法的氨基酸序列比对方法

蚁群遗传算法是在蚁群算法的基础上用遗传算法对其参数进行优化而产生的一种改进算法。把蚁群遗传算法应用于生物信息学中的氨基酸序列比对上,从而提出了一种新颖的蚁群遗传序列比对算法,实验结果表明这种新颖的序列比对算法是非常有效的。     

渐进方法结合蚁群算法求解多序列比对问题

在所有多重序列比对算法中,渐进比对方法由于简单的算法和高效的计算在生物信息学中得到了广泛的应用。但是渐进方法最大的缺点是在早期阶段形成的错误不能在后期的计算中纠正过来。针对这个问题,我们设计了ProAnt比对算法,即渐进方法和蚁群算法相结合来求解多重序列比对问题。首先,对输入的多个序列进行预处理,用蚁群算法和概率一致性更新计算出所有字符对在最终比对中出现的概率,称为“后验概率”,计算后验概率是为了预防早期错误的发生。然后我们将后验概率作为字符对之间的匹配得分,用渐进方法得到最终的比对结果。用BAliBASE数据库对算法进行测试,实验结果显示,该算法能够在保持合理的运算时间的前提下显著改善渐进比对方法的正确性。     

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO?+?MLP and GA-ACO?+?MLP hybrid models is compared with principal component analysis (PCA)?+?MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO?+?MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).     

进化蚁群优化理论实现障碍距离分析

借鉴了机器人路径规划问题的解决思路,将遗传算法中交叉算子引入到蚁群优化算法的路径寻优过程,提出了一种基于进化蚁群优化算法的障碍距离分析算法。实验结果表明,该方法不仅能处理复杂形状的障碍,与基于遗传算法的障碍距离计算方法相比,具有较好的路径寻优能力,并且能够很好地降低搜索陷入局部最优的可能性。     

基于凸策略优胜劣汰蚁群算法的机器人路径规划

针对机器人在已知静态工作环境中运动路径的快速选择和判优, 提出一种改进蚁群优化算法. 该算法首先对机器人的静态工作空间进行凸策略处理, 从环境上降低了搜索的盲目性和落入陷阱的可能性, 并在此基础上加入一种优胜劣汰策略, 进一步提高了算法的时间性能、最佳性能和鲁棒性. 实验结果表明改进的蚁群优化算法不仅克服了易于陷入局部最优解及运算量大的缺陷, 而且也大幅度提高了算法的运算效率.     

A hybrid ant colony optimization algorithm for optimal multiuser detection in DS-UWB system

A hybrid ant colony optimization algorithm is proposed by introducing extremal optimization local-search algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper. ACO algorithms have already successfully been applied to combinatorial optimization; however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of optimization problems. Hence in this paper, a hybrid ACO algorithm, named ACO-EO algorithm, is proposed by introducing EO to ACO to improve the local-search ability of the algorithm. The ACO-EO algorithm is applied to multiuser detection in DS-UWB communication system, and via computer simulations it is shown that the proposed hybrid ACO algorithm has much better performance than other ACO algorithms and even equal to the optimal multiuser detector.     

A new ant colony algorithm for makespan minimization in permutation flow shops

The problem of scheduling in permutation flow shop with the objective of minimizing the maximum completion time, or makespan, is considered. A new ant colony optimization algorithm is developed for solving the problem. A novel mechanism is employed in initializing the pheromone trails based on an initial sequence. Moreover, the pheromone trail intensities are limited between lower and upper bounds which change dynamically. When a complete sequence of jobs is constructed by an artificial ant, a local search is performed to improve the performance quality of the solution. The proposed ant colony algorithm is applied to Taillard’s benchmark problems. Computational experiments suggest that the algorithm yields better results than well-known ant colony optimization algorithms available in the literature.     

基于Q学习量子蚁群的微纳卫星路由算法

在微纳卫星网络中,传统蚁群路由算法不能同时保证数据传输的安全性和网络业务的服务质量,且易陷入局部最优解,收敛速度较慢。为解决上述问题,提出一种实现多目标优化的Q学习量子蚁群路由算法。该算法在选择下一跳节点的转移概率时,将路径的平均信任值和路径的费用作为两个优化目标,构成最优路径的节点性能指标,保证数据传输的安全性和网络业务服务质量。在考虑路径费用函数时,将量子计算引入到状态转移概率计算中,避免陷入局部最优解,并在算法中引入Q学习的思想,将信息素映射成Q学习的Q值,强化算法在动态环境中的学习能力,以提高路由的整体性能。仿真结果表明,与蚁群优化算法和改进的蚁群多约束路由算法相比,Q学习量子蚁群路由算法明显改善包投递率、平均端到端时延和节点平均能耗等性能指标,避免了蚁群算法易陷入局部最优解,提高了收敛速度,可适用于具有高速移动节点的微纳卫星网络。     

基于改进蚁群算法的多序列比对

提出一种基于改进蚁群算法的多序列比对方法。该算法改变了信息素的更新方式、字符的选择方法、蚂蚁在蚁巢和食物之间往返搜索以及随机分配蚂蚁开始序列等。实验结果表明,改进后的算法不仅有效地克服了基本蚁群多序列比对算法中的停滞现象,而且即使在运行的后期,仍然能以极大的概率搜索较好解。     

改进蚁群算法的局部信息动态路径规划

针对传统蚁群算法收敛速度慢、对动态路径变化适应性低的局限性,提出了一种基于局部信息获取策略的动态改进型蚁群算法。该算法利用局部信息获取策略,进行最优局部目标点的获取,然后调用改进蚁群算法获取局部区域内的最优路径,再重复循环获取新的最优局部目标点,直到找到全局目标点;与此同时,将提出的改进型蚁群算法应用于动态路径规划中的路径寻优与避障,仿真结果表明:提出的算法在具有与传统蚁群算法相当的路径优化效果的同时,能够有效适应障碍变化、大大提高了路径规划的收敛速度。     

基于蚁群-遗传融合框架的仓储群机器人任务分配

将智能仓储中的自主移动群机器人订单任务分配,建模成群机器人协同调度的多目标优化问题,将成员机器人完成拣货任务的路径代价和时间代价作为优化目标.设计了蚁群-遗传算法融合框架并在其中求解.该框架中,蚁群算法作为副算法,用于初始种群优化;遗传算法改进后作为主算法.具体地,在遗传算法轮盘赌选择算子后引入精英保留策略,并在遗传操作中加入逆转算子.针对不同数量的订单任务,使用不同规模的群机器人系统进行了任务分配仿真实验.结果表明,在本文所提的融合框架中求解,较分别使用蚁群算法或遗传算法单独求解,性能上具有明显优势,能够发挥蚁群算法鲁棒性好和遗传算法全局搜索能力强的特点,提高智能仓储系统的整体运行效率.     

改进蚁群算法的无线传感器网络路径优化

研究无线传感器网络路径优化问题,针对无线传感器网络(WSN)路径优化问题,在分析了遗传算法和蚁群算法各自优缺点的基础上,通过把蚁群算法作为WSN路径优化的主框架,采用遗传算的选择、交叉和变异算子提高蚁群算法搜索速度,提出一种改进蚁群算法的WSN路径优化方法。仿真结果表明,改进蚁群算法有效地克服了基本蚁群算法的缺陷,提高了WSN路径优化效率和成功率,减少了能理消耗,有效延长了网络生存时间。     

改进的重叠蚁群优化算法

针对蚁群优化算法在进行全局最优解搜索时容易陷入局部最优解和收敛速度缓慢等缺陷,提出了一种有效求解全局最优解搜索问题的重叠蚁群优化算法。该算法通过设置多个重叠的蚁群系统,并对每一个蚁群初始化不同的参数,之后在蚁群之间进行信息素的动态学习,增强了不同蚁群对最优解的开采能力,避免了算法出现早熟现象。仿真实验结果表明,重叠蚁群优化算法在避免陷入局部最优解方面具有良好的效果,是一种提高蚁群算法性能的有效的改进算法。     

A new and efficient ant-based heuristic method for solving the traveling salesman problem

Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.