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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. 相似文献
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Wu Deng Rong Chen Bing He Yaqing Liu Lifeng Yin Jinghuan Guo 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(10):1707-1722
This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA–PSO–ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA–PSO–ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS–ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases. 相似文献
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A memetic ant colony optimization algorithm for the dynamic travelling salesman problem 总被引:1,自引:1,他引:0
Michalis Mavrovouniotis Shengxiang Yang 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2011,15(7):1405-1425
Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms. 相似文献
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Kazem Gheysari Abdollah Khoei Behboud Mashoufi 《Expert systems with applications》2011,38(4):3632-3639
Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies’ behavior. This paper presents design and CMOS implementation of the ant colony optimization based algorithm for solving the TSP problem. In order to implement ant colony optimization algorithm in CMOS, we will present a new algorithm. This algorithm is based on the original ant colony optimization but it can be implemented in CMOS. Briefly, pheromone matrix is transformed on the chip area and ants move up-down through the pheromone matrix and they make their decisions. Finally ants select a global path. In previous researches only pheromone values is used, but select the next city in this paper is based on heuristics value and pheromone value. In definition of problem, we use heuristics value as a matrix. Previous researches could not be used for wide type of optimization problem but our chip gives heuristics value initially and we can change initial value of heuristics value according to the optimization problem so this capability increases the flexibility of ACO chip. Simple circuit is used in blocks of our chip to increase the speed of convergence of ACO chip. We use Linear Feedback Shift Register (LSFR) circuit for random number generator in ACO chip. ACO chip has capability of solving the big TSP problem. ACO chip is simulated by HSPICE software and simulation results show the good performance of final chip. 相似文献
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The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Ant colony optimization (ACO) has been successfully used to solve TSPs and many associated applications in the last two decades. However, ACO has problem in regularly reaching the global optimal solutions for TSPs due to enormity of the search space and numerous local optima within the space. In this paper, we propose a new hybrid algorithm, cooperative genetic ant system (CGAS) to deal with this problem. Unlike other previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to subsequent ACO iterations, this new approach combines both GA and ACO together in a cooperative manner to improve the performance of ACO for solving TSPs. The mutual information exchange between ACO and GA in the end of the current iteration ensures the selection of the best solutions for next iteration. This cooperative approach creates a better chance in reaching the global optimal solution because independent running of GA maintains a high level of diversity in next generation of solutions. Compared with results from other GA/ACO algorithms, our simulation shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the global optimal solution, and quality of average optimal solutions, particularly for small TSPs. 相似文献
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首先基于蚁群算法建立了一个多用户检测问题的模型,在这个模型中,蚁群算法得到了简化并且更加利于并行计算.随后将最大-最小的蚂蚁系统用于多用户检测,并通过分析算法的缺陷提出了一种蚁群算法与禁忌搜索相结合的混合算法.通过对多用户检测问题的试验仿真表明,改进算法不仅操作简单,而且全局搜索能力有了显著的提高. 相似文献
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蚁群优化算法是一种能应用于求解旅行商问题(Traveling Salesman Problem,TSP)的智能算法,但蚁群算法在求解TSP路径规划问题中存在收敛速度慢、易陷入局部最优解问题,而将蚂蚁算法的蚁群分组,能增加全局搜索能力,提高求解路径规划性能。通过分析蚁群分组大小与蚁群算法性能的关系,并提出了一种自适应分组蚁群算法,采用一种随迭代分组数减少策略方法,并将其应用于对TSP路径规划问题求解。通过实验结果对比表明,自适应分组蚁群算法在收敛速度和搜索质量方面都有了明显提高。 相似文献
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蚁群算法是受自然界中的蚂蚁觅食行为启发而设计的智能优化算法,特别适合处理离散型的组合优化问题。提出一种求解多处理机调度的蚁群算法,利用一个蚂蚁代表一个处理机来选择任务,并通过分析关键路径及每个任务的最早、最迟开始时间来确定每个任务的紧迫程度,让蚂蚁以此来选择任务。实验证明,该算法可比传统算法取得有更好运行效率的调度策略。 相似文献
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Yuren Zhou 《Evolutionary Computation, IEEE Transactions on》2009,13(5):1083-1092
Ant colony optimization (ACO) is a relatively new random heuristic approach for solving optimization problems. The main application of the ACO algorithm lies in the field of combinatorial optimization, and the traveling salesman problem (TSP) is the first benchmark problem to which the ACO algorithm has been applied. However, relatively few results on the runtime analysis of the ACO on the TSP are available. This paper presents the first rigorous analysis of a simple ACO algorithm called (1 + 1) MMAA (Max-Min ant algorithm) on the TSP. The expected runtime bounds for (1 + 1) MMAA on two TSP instances of complete and non-complete graphs are obtained. The influence of the parameters controlling the relative importance of pheromone trail versus visibility is also analyzed, and their choice is shown to have an impact on the expected runtime. 相似文献
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Swarm-inspired optimization has become very popular in recent years. Particle swarm optimization (PSO) and Ant colony optimization (ACO) algorithms have attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving complex optimization problems. Both ACO and PSO were successfully applied for solving the traveling salesman problem (TSP). Performance of the conventional PSO algorithm for small problems with moderate dimensions and search space is very satisfactory. As the search, space gets more complex, conventional approaches tend to offer poor solutions. This paper presents a novel approach by introducing a PSO, which is modified by the ACO algorithm to improve the performance. The new hybrid method (PSO–ACO) is validated using the TSP benchmarks and the empirical results considering the completion time and the best length, illustrate that the proposed method is efficient. 相似文献
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三种现代优化算法的比较研究 总被引:1,自引:0,他引:1
现代最优化算法比较常见的有遗传算法、蚁群算法、微粒群算法、人工鱼群算法等。本文主要对前三种算法优化性能进行比较研究。首先介绍了三种算法的基本原理,然后总结了各自的优缺点并从原理和参数两个方面对三种算法进行了对比分析,最后以经典TSP问题为例进行了仿真研究并得出了一些指导算法适用范围的结论。 相似文献
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基于粒子群优化的蚁群算法在TSP中的应用 总被引:2,自引:0,他引:2
结合粒子群算法的问题,提出用混合蚁群算法来求解著名的旅行商问题.问题的核心是应用粒子群算法对蚁群算法的控制参数:启发式因子、信息素挥发系数、随机性选择阈值进行优化,以及运用蚁群系统算法寻找最短路径.新算法对于蚂蚁算法中的参数调整大大减低,减少了大量盲目的实验,力求在开发最优解和探究搜索空间上找到平衡点.对旅行商问题的仿真实验表明,新算法的优化质量和效率都优于传统蚁群算法和遗传算法,接近理论最佳值.新算法也可推广用于其他NP问题的求解. 相似文献
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一种快速求解旅行商问题的蚁群算法 总被引:2,自引:0,他引:2
蚁群优化是一种元启发式的随机搜索技术,是目前解决组合优化问题最有效的工具之一.将信息素更新和随机搜索机制的改进相结合,提出一种快速求解旅行商问题的蚁群算法.首先给出了一种新的信息素增量模型,以体现蚂蚁在不同路径上行走时所产生的信息素差异;然后以蚂蚁经过的路径(直线段)作为信息素扩散浓度场的信源,改进了信息素扩散模型,强化了蚂蚁间的协作和交流;最后采用较低复杂度的变异策略对迭代的结果进行优化.在大量通用数据集上的实验表明,该算法不仅能获得更好的最优解,而且收敛速度有显著的提高. 相似文献
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针对蚁群算法在求解旅行商问题时收敛时间长,且易陷入局部最优状态的缺陷,提出一种基于拥挤度的动态信息素蚁群优化策略。该算法引入静态拥挤度和动态拥挤度算子,主动提前预防停滞现象。将拥挤度与状态转移规则相结合,使蚁群状态实时跟随路径搜索情况而改变,提高蚁群自适应能力。针对蚁群路径搜索情况,加入邻域搜索优化规则,缩小搜索区域,结合2-opt局部优化策略,加快蚁群收敛速度。仿真结果表明,本算法既有较高的搜索效率又有较强的全局搜索能力。对比其他优化算法,无论是求解质量、稳定性还是收敛速度都能达到令人满意的效果。 相似文献