Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.

Traveling Salesman Problem Using an Enhanced Hybrid Swarm Optimization Algorithm

The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research.To alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is introduced.Specifically,the adaptive elite-guided search mechanism enhances the balance between exploration and exploitation.The modified mutualism phase reinforces the information interaction among particles and local optima avoidance.The cooperation of these two mechanisms boosts the overall performance of the basic EO.The AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite.Experimental results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and accuracy.In addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)environments.The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network （WSN）. We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN

To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.

Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues

A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.

An algorithm for earthwork allocation considering non-linear factors

For solving the optimization model of earthwork allocation considering non-linear factors,a hybrid algorithm combined with the ant algorithm(AA)and particle swarm optimization(PSO)is proposed in this paper.Then the proposed method and the LP method are used respectively in solving a linear allocation model of a high rockfill dam project.Results obtained by these two methods are compared each other.It can be concluded that the solution got by the proposed method is extremely approximate to the analytic solution of LP method.The superiority of the proposed method over the LP method in solving a non-linear allocation model is illustrated by a non-linear case.Moreover,further researches on improvement of the algorithm and the allocation model are addressed.

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

基于AIS轨迹和改进蚁群算法的船舶航线规划方法

在保证船舶航线安全的前提下,以最短航程为目标,提出基于AIS轨迹和改进蚁群算法的船舶航线规划方法。对船舶AIS数据进行预处理,去除船舶AIS数据中的冗余数据,完成船舶AIS数据提纯;采用基于粒子群与K均值混合聚类算法的核心转向点筛选与识别方法,筛选并识别船舶AIS数据中船舶航线核心转向点数据;通过基于改进蚁群算法的航线规划方法,以核心转向点数据为基础,构建航线网络,在此网络中,通过人工势场法对蚁群算法进行改进,对船舶航线进行寻优,实现船舶航线规划。经实验验证,本文方法能够规划出安全合理的船舶航线。

基于改进天牛须群落的卫星光网络路由算法

针对当前卫星光网络路由算法波长利用率低,通信成功率低,导致路由效率低下的问题,提出一种改进天牛须群落的卫星光网络路由算法(BS-ACRWA)。该方法使用波长矩阵乘的形式生成与当前卫星节点相邻节点的波长冲突度,求得最大公有波长数,同时考虑时延和卫星节点负载状态构建约束优化模型,降低无效路由的次数。在路由阶段对天牛须群落算法进行改进,引入蚁群算法信息素机制,在搜索方向上充分考虑卫星之间链路有限的特性,对搜索方向进行更新,提高算法效率。仿真结果表明:与SARWA算法、CL-ACRWA算法和Dijkstra算法相比,BS-ACRWA算法将波长利用率提高了0.05、0.11、0.23,同时在平均时延、丢包率、阻塞率、路由成功率等方面具有更好的性能。

考虑电动汽车充电负荷及储能寿命的充电站储能容量配置优化

提出了一种优化电动汽车充电站储能容量配置的方法。该方法考虑了季节性电动汽车充电负荷波动与光伏出力之间的关系,并且考虑了储能寿命。论文利用蒙特卡罗法考虑了不同类型电动汽车的多种影响因素,对整体负荷进行预测。以每日运行成本最低为优化目标,在考虑四季光伏出力和储能寿命的影响下,采用了3种算法对目标函数进行优化,以得到最佳的光储充电站储能配置方案。研究以西北某地区为例。结果表明:冬季下综合成本为3.0432×10^(6)元,相比于其余3个季节综合成本最低;采用遗传算法时,在综合成本相差不多时,获得的储能配置最优,储能容量为22.82 MWh,储能功率为7.31MW,从而得到光储充电站最优的储能容量配置。

陷阱标记联合懒蚂蚁的自适应粒子群优化算法

为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。

多策略遗传算法求解多机器人任务分配问题

针对遗传算法(genetic algorithm,GA)求解多机器人任务分配(multi-robot task allocation,MRTA)时容易陷入局部最优以及效率不高的问题,提出一种多策略遗传算法(简称DIHA-GA)实现对多个任务的合理分配。首先,采用双染色体编码策略来简化编码方式;其次,将种群分成3个部分来使种群在保持随机性的同时增强染色体的质量;再次,采用启发式交叉算子来拓展解的搜索范围,加大算法跳出局部最优的能力;最后,使用自适应交叉概率和变异概率来使算法更快找到最优解。结果表明:在任务数为20和40这2种情况下,DIHA-GA相比于混合粒子群算法(hybrid particle swarm optimization,HPSO)距离平均值分别减少了14.46 m和17.36 m,距离最小值分别减少了14.89 m和20.86 m,这说明DIHA-GA所得解更接近最优解;DIHA-GA比改进蚁群算法(improved ant colony optimization,IACO)所得距离平均值分别减少了21.32 m和18.73 m,距离最小值分别减少了23.43 m和22.32 m,这是由于IACO过早收敛并且难以跳出局部最优导致的。通过比较,验证了DIHA-GA在求解MRTA问题上的有效性。

IACO-GA-IPSO融合算法AUV三维全局路径规划

为了解决传统蚁群算法收敛速度慢,易陷入局部最优,传统粒子群算法搜索精度差,初始路径不规则等问题,提出一种融合了改进蚁群算法(IACO)、改进粒子群算法(IPSO)和遗传算法(GA)的IACO-GA-IPSO路径规划算法。首先定义三维海洋环境模型,将工作空间沿Z轴方向划分成水平的栅格平面;其次建立多标准的路径优劣评价模型;最后由融合算法规划路径:IACO算法生成次优种群,GA算法优化种群多样性,IPSO算法快速收敛到全局最优。实验结果表明,融合算法能充分发挥每种算法的优点,克服种群规模和收敛速度的矛盾,优化初始种群,提高全局搜索能力、局部搜索精度和算法运行效率,加快收敛速度并避免陷入局部最优路径。

基于粒子群和改进蚁群算法的云计算任务调度

针对目前云计算任务调度方法的效率较低和日益多样化的用户服务质量需求等问题,提出一种将粒子群算法和改进蚁群算法结合的混合粒子群蚁群算法(HPSO-ACO),包括建立云计算任务调度模型、用户服务质量模型及虚拟资源节点模型。利用离散型粒子群算法,得到初始解集,转化为蚁群算法信息素的初始值,通过改进蚁群算法的寻径规则和信息素更新规则,得到最终解。通过仿真实验将粒子群算法、蚁群算法和HPSO-ACO算法进行比较,其结果表明,HPSO-ACO算法有效且可行,能够减少任务完成时间和降低完成成本,满足用户服务质量要求。

面向无人机航路的优化算法研究综述

随着无人机任务复杂性以及环境不确定性的不断提高,对航路规划的要求也随之提高,航路规划问题复杂度逐渐增加,由单无人机航路规划向多无人机规划发展,由单任务向多任务发展。针对无人机航路规划问题,从概念内涵、任务建模、算法解析等方面进行了综合分析。针对现有航路规划算法存在的最优路径效果较差、收敛速度慢以及易陷入局部最优等问题,重点分析了A*算法、粒子群算法、遗传算法、蚁群算法在无人机航路规划中的应用及存在的问题,提出了优化改进的方向。

改进蚁群算法在地形跟随航线规划问题中的应用

针对飞机地形跟随航线规划需要,提出了一种基于改进蚁群算法的通用解决方案。该方法通过空间等分的思想将三维地图重构为解空间,并通过一系列改进措施提升蚁群算法效率,包括围绕加强蚁群中最优蚂蚁的正增益、减弱最劣蚂蚁的负增益,设计信息素更新策略;综合考虑可行航路点距离、高度、转弯角度的影响,设计节点移动策略;采用粒子群算法,智能优化求解蚁群算法的核心参数等,实现地形跟随航线的快速生成。通过具体算例验证了该方法的先进性和可行性。