A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security

With increasing concern about global warming and haze, environmental issue has drawn more attention in daily optimization operation of electric power systems. Economic emission dispatch (EED), which aims at reducing the pollution by power generation, has been proposed as a multi-objective, non-convex and non-linear optimization problem. In a practical power system, the problem of EED becomes more complex due to conflict between the objectives of economy and emission, valve-point effect, prohibited operation zones of generating units, and security constraints of transmission networks. To solve this complex problem, an algorithm of a multi-objective multi-population ant colony optimization for continuous domain (MMACO_R) is proposed. MMACO_R reconstructs the pheromone structure of ant colony to extend the original single objective method to multi-objective area. Furthermore, to enhance the searching ability and overcome premature convergence, multi-population ant colony is also proposed, which contains ant populations with different searching scope and speed. In addition, a Gaussian function based niche search method is proposed to enhance distribution and accuracy of solutions on the Pareto optimal front. To verify the performance of MMACO_R in different multi-objective problems, benchmark tests have been conducted. Finally, the proposed algorithm is applied to solve EED based on a six-unit system, a ten-unit system and a standard IEEE 30-bus system. Simulation results demonstrate that MMACO_R is effective in solving economic emission dispatch in practical power systems.     

基于改进蚁群算法的最短路径问题研究及应用

蚁群系统作为一种蚁群算法是解决最短路径问题的一种行之有效的方法.然而,它自身也存在着一些缺陷,主要针对基本蚁群算法易陷入局部最优这一缺陷对其进行改进,集中体现在初始信息素求解和信息素更新这两方面.为了进一步了解改进蚁群算法的优点,进行了实验仿真:将改进的蚁群算法应用子模拟医疗救护GIS中,利用GIS的网络分析功能对城市道路网络的最短路径选择算法进行了深入地探讨研究,并以山西省太原市的交通路线作为实例进行研究.计算机仿真结果表明,改进的蚁群算法在解决最短路径问题时较基本蚁群算法的性能好,它具有一定的理论参考价值和现实意义.     

求解图着色问题的量子蚁群算法

针对经典的图着色问题,在蚁群算法的基础上结合量子计算提出一种求解图着色问题的量子蚁群算法. 将量子比特和量子逻辑门引入到蚁群算法中,较好地避免了蚁群算法搜索易陷入局部极小的缺陷,并显著加快了算法的运算速度. 通过图着色实例的大量仿真实验,表明算法对图着色问题的求解是可行的、有效的,且具有通用性.     

改进蚁群算法优化周期性车辆路径问题

周期性车辆路径问题(PVRP)是标准车辆路径问题(VRP)的扩展,PVRP将配送期由单一配送期延伸到T(T＞1)期,因此,PVRP需要优化每个配送期的顾客组合和配送路径。由于PVRP是一个内嵌VRP的问题,其比标准VRP问题更加复杂,难于求解。本文采用蚁群算法对PVRP进行求解,并提出采用两种改进措施——多维信息素的运用和基于扫描法的局部优化方法来提高算法的性能。最后,通过9个经典PVRP算例对该算法进行了数据实验,结果表明本文提出的改进蚁群算法求解PVRP问题是可行有效的,同时也表明两种改进措施可以显著提高算法的性能。     

启发式蚁群算法及其在高填石路堤稳定性分析中的应用

模仿蚁群之群体行为而发展起来的蚁群算法 ( ACA) ,目前多用于求解组合优化问题 .为了让 ACA能求解复杂边坡工程问题 ,本文对蚁群算法的结构和蚂蚁转移概率的确定方法进行改进 ,得到一个新的评价复杂边坡稳定性的方法——启发式蚁群算法 ( HACA) ,探讨了 HACA在高填石路堤稳定性分析中的应用 .     

Evaluation of the multiobjective ant colony algorithm performances on biobjective quadratic assignment problems

The difficulty of resolving the multiobjective combinatorial optimization problems with traditional methods has directed researchers to investigate new approaches which perform better. In recent years some algorithms based on ant colony optimization (ACO) metaheuristic have been suggested to solve these multiobjective problems. In this study these algorithms have been reported and programmed both to solve the biobjective quadratic assignment problem (BiQAP) instances and to evaluate the performances of these algorithms. The robust parameter sets for each 12 multiobjective ant colony optimization (MOACO) algorithms have been calculated and BiQAP instances in the literature have been solved within these parameter sets. The performances of the algorithms have been evaluated by comparing the Pareto fronts obtained from these algorithms. In the evaluation step, a multi significance test is used in a non hierarchical structure, and a performance metric (P metric) essential for this test is introduced. Through this study, decision makers will be able to put in the biobjective algorithms in an order according to the priority values calculated from the algorithms’ Pareto fronts. Moreover, this is the first time that MOACO algorithms have been compared by solving BiQAPs.     

An improved ant colony optimisation heuristic for graph colouring

The focus of this paper is an ant colony optimisation heuristic for the graph colouring problem. We start by showing how a series of improvements enhance the performance of an existing ant colony approach to the problem and then go on to demonstrate that a further strengthening of the construction phase, combined with a tabu search improvement phase, raise the performance to the point where it is able to compete with some of the best-known approaches on a series of benchmark problems.     

B2C电子商务仓库拣货路径优化策略应用研究

当前国内B2C电子商务仓库多为人至物的拣货模式,拣货作业成为其核心作业之一,占据仓库大量时间成本和资金成本,拣货路径优化成为企业亟需解决的问题。本文基于TSP对拣货路径进行建模,利用蚁群算法、模拟退火算法和禁忌搜索对该NP-hard问题进行求解,并同当前企业普遍采用的S型启发式策略进行对比,拣货时间节约13.35%。进一步得出当拣货品数量较少时应采用模拟退火算法求解,而当拣货品数量较大时采用蚁群算法仅进行一次迭代,则可以实现短时间得到相对较优的解。所得结果已应用于某大型电子商务企业,效果明显。     

A parallel improved ant colony optimization for multi-depot vehicle routing problem

This paper presents a method for solving multi-depot vehicle routing problem (MDVRP). First, a virtual central depot is added to transfer MDVRP to the multi-depot vehicle routing problem with the virtual central depot (V-MDVRP), which is similar to a vehicle routing problem (VRP) with the virtual central depot as the origin. An improved ant colony optimization with coarse-grain parallel strategy, ant-weight strategy and mutation operation, is presented for the V-MDVRP. The computational results for 23 benchmark problems are reported and compared to those of other ant colony optimizations.     

A numerical study of MIDACO on 100 MINLP benchmarks

This article presents a numerical study on MIDACO, a new global optimization software for mixed integer non-linear programming (MINLP) based on ant colony optimization and the oracle penalty method. Extensive and rigorous numerical tests on a set of 100 non-convex MINLP benchmark problems from the open literature are performed. Results obtained by MIDACO are directly compared to results by a recent study of state-of-the-art deterministic MINLP software on the same test set. Further comparisons with an established MINLP software is undertaken in addition. This study shows that MIDACO is not only competitive to the established MINLP software, but can even outperform those in terms of the number of global optimal solutions found. Moreover, the parallelization capabilities of MIDACO enable it to be even competitive to deterministic software regarding the amount of (serial processed) function evaluation, while the black-box capabilities of MIDACO offer an intriguing new robustness for MINLP.     

模糊蚁群算法及其在TSP中的应用

在传统蚁群算法的基础上加入了使用模糊规则表更新信息素的策略,提出了一种新的算法——模糊蚁群算法.算法结合了模糊控制中输入输出的模糊化处理和蚁群寻优的特点,为实际问题提供了新的解决手段.文中将模糊蚁群算法应用于TSP问题,通过对中国31个省会城市等实例数据进行的测试,验证表明了新算法具有良好的有效性和鲁棒性.     

一种改进的混合型蚁群算法在TSP问题中的应用

介绍了一种求解TSP问题的算法改进的混合型蚁群算法,该算法在近邻法构造初始解的基础上,使用2-opt局部搜索法对当前解进行改进,在更新全局信息素时采用基于排序的蚂蚁系统对排在前2名的蚂蚁更新全局信息素,且为全局信息素设置最大值和最小值,并使用Matlab仿真求解了kroa200等13个经典tsp问题,得到的结果和最优解的误差很小,并和两种最新改进的蚁群算法以及两种自组织算法进行比较,比较结果充分证明了该改进算法的有效性.     

Revisiting dynamic programming for precedence-constrained traveling salesman problem and its time-dependent generalization

The precedence constrained traveling salesman problem (TSP-PC), or the sequential ordering problem (SOP), consists of finding an optimal TSP tour that will also satisfy the namesake precedence constraints, typically specified as a partial order or a directed acyclic graph. Its dynamic programming (DP) solution was proposed as early as 1979, however, by late 1990s, it mostly fell out of use in plain TSP-PC. Revisiting this method, we are able to close one of the long-standing TSPLIB SOP problem instances, ry48p.3.sop, and provide improved bounds on its time complexity. Harnessing the “omnivorous” nature of DP, we prove the validity of DP optimality principle for TSP-PC with both (i) abstract cost aggregation function, which may be the arithmetic + operation as in “ordinary” TSP or $\max$ as in Bottleneck TSP, or any other left-associative nondecreasing in the first argument operation and (ii) travel cost functions depending on the set of pending tasks (“sequence dependence”). Using the latter generalization, we close several TD-SOP (time-dependent TSP-PC) instances based on TSPLIB SOP as proposed by Kinable et al., including rbg253a.sop. Through the restricted DP heuristic, which was originally formulated for time-dependent TSP by Malandraki and Dial, we improve the state-of-the-art upper bounds for all yet unsolved TSPLIB-based TD-SOP instances, including those with more than 100 cities. We also improve worst-case complexity estimates for DP in TSP-PC.     

General variable neighborhood search for the uncapacitated single allocation <Emphasis Type="Italic">p</Emphasis>-hub center problem

In this paper we propose a general variable neighborhood search heuristic for solving the uncapacitated single allocation p-hub center problem (USApHCP). For the local search step we develop a nested variable neighborhood descent strategy. The proposed approach is tested on benchmark instances from the literature and found to outperform the state-of-the-art heuristic based on ant colony optimization. We also test our heuristic on large scale instances that were not previously considered as test instances for the USApHCP. Moreover, exact solutions were reached by our GVNS for all instances where optimal solutions are known.     

军事定向越野路径优化问题建模及混合蚁群算法求解

军事定向越野运动中存在点位多、分布散、时间紧、得分要求高等条件,为在规定时间内找到一条得分高的行进线路,找到衡量和分析运动成绩好坏的标准,文中对军事定向越野中的路径优化问题进行了深入分析,建立了混合整数规划模型,设计了一种混合蚁群算法。算法中,首先由改进蚁群算法找到初始解,然后再利用选择、交叉和变异算子进行解的优化,通过仿真实验和算法对比验证了混合蚁群算法的可行性和优越性。     

An ant colony optimization approach for the multidimensional knapsack problem

Ant colony optimization is a metaheuristic that has been applied to a variety of combinatorial optimization problems. In this paper, an ant colony optimization approach is proposed to deal with the multidimensional knapsack problem. It is an extension of Max Min Ant System which imposes lower and upper trail limits on pheromone values to avoid stagnation. In order to choose the lower trail limit, we provide a new method which takes into account the influence of heuristic information. Furthermore, a local search procedure is proposed to improve the solutions constructed by ants. Computational experiments on benchmark problems are carried out. The results show that the proposed algorithm can compete efficiently with other promising approaches to the problem.     

An innovative flower pollination algorithm for continuous optimization problem

The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has received widespread attention and been applied in various engineering fields. However, the FPA still has certain drawbacks, such as inadequate optimization precision and poor convergence. In this paper, an innovative flower pollination algorithm based on cloud mutation is proposed (CMFPA), which adds information of all dimensions in the global optimization stage and uses the designed cloud mutation method to redistribute the population center. To verify the performance of the CMFPA in solving continuous optimization problems, we test twenty-four well-known functions, composition functions of CEC2013 and all benchmark functions of CEC2017. The results demonstrate that the CMFPA has better performance compared with other state-of-the-art algorithms. In addition, the CMFPA is implemented for five constrained optimization problems in practical engineering, and the performance is compared with state-of-the-art algorithms to further prove the effectiveness and efficiency of the CMFPA.     

集装箱翻箱问题的蚁群算法改进

翻箱问题属于NP难问题,基本蚁群算法在求解该问题上收敛困难且寻优能力低。因此,本文提出了一种适合于翻箱模型的改进型蚁群算法,在概率决策机制、解的重构、信息素更新机制三个方面对基本蚁群算法进行改进。最后通过与其他算法的分析比较,验证了该改进算法的可行性与有效性。     

A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP

The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the ant colony optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and a proposal of a taxonomy for them is presented. In addition, an empirical analysis is developed by analyzing their performance on several instances of the bi-criteria traveling salesman problem in comparison with two well-known multi-objective genetic algorithms.     

ACO classification of thermogram symmetry features for breast cancer diagnosis

Breast cancer is the most commonly diagnosed form of cancer in women. While mammography is the standard modality for detecting breast cancer, it has been shown that medical thermography, which uses cameras with sensitivities in the thermal infrared, is also well suited for the task, especially in dense tissue and for small tumors. In this paper, we present an approach of analysing breast thermograms that first extracts a series of image features describing bilateral (a)symmetry between left and right breast regions, and then uses these features in a subsequent classification step. For the classification, we employ an ant colony optimisation based pattern recognition algorithm that is shown to provide a concise rule base with good classification performance.