Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field

An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.

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.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends

Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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.

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.

Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization

The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

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。

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。

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。

地震场景下无人机群路径规划与任务分配均衡联合优化

无人机(UAV)群路径规划和任务分配是UAV群救援应用的核心,然而传统方法分开求解路径规划与任务分配,导致资源分配不均。为了解决上述问题,结合UAV群的物理属性与应用环境因素,改进蚁群算法(ACO),提出联合并行蚁群(JPACO)模型。首先,借助分级信息素增强系数机制更新信息素,以提高JPACO任务分配均衡性和能耗均衡性;其次,设计路径平衡因子和动态概率转移因子优化蚁群模型易陷入局部收敛的情况,从而提高JPACO的全局搜索能力;最后,引入集群并行处理机制,以降低JPACO运算耗时。将JPACO与自适应动态蚁群算法(ADACO)、扫描动态蚁群算法(SMACO)、贪婪策略蚁群算法(GSACO)和交叉蚁群算法(IACO)在公开数据集CVRPLIB上对比最优路径、任务分配均衡、能耗均衡和运算耗时。实验结果表明:与IACO和ADACO相比,JPACO处理小规模运算的最优路径平均值分别降低7.4%和16.3%;处理大规模运算的求解耗时与GSACO、ADACO相比降低8.2%和22.1%。以上结果验证了JPACO在处理小规模运算时能够改善最优路径,处理大规模运算时任务分配均衡、能耗均衡和运算耗时明显优于对比算法。

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。

基于蚁群优化算法的无线传感器网络节能路由策略

针对无线传感器网络,设计一种基于蚁群优化算法的无线传感器网络节能路由策略。考虑无线传感器网络具有带宽有限、电能有限、内存低等限制性,基于元启发式影响因子对WSN路由进行建模,模拟蚂蚁的觅食行为进行路由搜索,计算转发路径。通过引入期望跳数、信号接收强度指示、剩余能量等,并综合信息素对下一跳概率函数进行设计,从而实现跳数优化、节能优化的目的,仿真证明了该路由策略的有效性。

基于智能优化方法的工业机器人时间最优轨迹规划方法

随着工业自动化进程的加速,工业机器人的轨迹规划问题愈发凸显其重要性。由于传统轨迹规划方法在时间最优性方面的局限性,本文致力于研究基于智能优化算法的工业机器人时间最优轨迹规划。本文阐述了轨迹规划的定义并建立了相应的数学模型。通过对比传统方法与智能优化算法,重点探讨了基于粒子群优化和蚁群优化的时间最优轨迹规划方法,分别描述了问题并提出了求解策略。研究结果显示,智能优化算法在提升工业机器人运动效率和精度方面具有显著优势。

一种改进ANN的GNSS高程曲面拟合方法研究

在精细化区域高程系统构建过程中高程基准转化模型研究中,主要使用机器学习算法对传统曲面拟合方法进行替代,即使用非线性机器学习模型来实现GNSS高程系统的拟合工作。本文分别利用蚁群优化算法、遗传算法和粒子群优化算法对ANN模型进行优化,使用某矿区观测站实测的GNSS和水准数据对优化算法效果进行验证。实验结果表明:在观测区域较大和高程异常不规则的情况下,使用优化算法对ANN模型进行优化均取得较好效果。粒子群算法优化后的ANN模型更加适用于对小区域内GNSS高程曲面拟合上的应用,有效提升了高程拟合精度。

A Swarm Intelligence Networking Framework for Small Satellite Systems

Recent development of technologies and methodologies on distributed spacecraft systems enable the small satellite network systems by supporting integrated navigation, communications and control tasks. The distributed sensing data can be communicated and processed autonomously among the network systems. Due to the size, density and dynamic factors of small satellite networks, the traditional network communication framework is not well suited for distributed small satellites. The paper proposes a novel swarm intelligence based networking framework by using Ant colony optimization. The proposed network framework enables self-adaptive routing, communications and network reconstructions among small satellites. The simulation results show our framework is suitable for dynamic factors in distributed small satellite systems. The proposed schemes are adaptive and scalable to network topology and achieve good performance in different network scenarios.

CPAC: Energy-Efficient Algorithm for IoT Sensor Networks Based on Enhanced Hybrid Intelligent Swarm

The wireless sensor network(WSN)is widely employed in the application scenarios of the Internet of Things(IoT)in recent years.Extending the lifetime of the entire system had become a significant challenge due to the energy-constrained fundamental limits of sensor nodes on the perceptual layer of IoT.The clustering routing structures are currently the most popular solution,which can effectively reduce the energy consumption of the entire network and improve its reliability.This paper introduces an enhanced hybrid intelligential algorithm based on particle swarm optimization(PSO)and ant colony optimization(ACO)method.The enhanced PSO is deployed to select the optimal cluster heads for establishing the clustering architecture.An improved ACO is introduced to realize the data transmission from terminal sensor nodes to the base station.Our proposed algorithm can effectively reduce the entire energy consumption and extend the lifetime of IoT sensor networks.Compared with the traditional algorithms,the simulation results show that the presented novel algorithm in this paper has obvious optimization and improvement in network lifetime and energy utilization efficiency.

A Cluster Based QoS-Aware Service Discovery Architecture Using Swarm Intelligence

The existing mobile service discovery approaches do not completely address the issues of service selection and the robustness faced to mobility. The infrastructure of mobile service must be QoS-aware plus context-aware (i.e.) aware of the user’s required-QoS and the QoS offered by the other networks in user’s context. In this paper, we propose a cluster based QoS-aware service discovery architecture using swarm intelligence. Initially, in this architecture, the client sends a service request together with its required QoS parameters like power, distance, CPU speed etc. to its source cluster head. Swarm intelligence is used to establish the intra and inter cluster shortest path routing. Each cluster head searches the QoS aware server with matching QoS constraints by means of a service table and a server table. The QoS aware server is selected to process the service request and to send the reply back to the client. By simulation results, we show that the proposed architecture can attain a good success rate with reduced delay and energy consumption, since it satisfies the QoS constraints.

基于多目标PSO-ACO融合算法的无人艇路径规划

【目的】针对河蟹养殖过程中,水位变化以及无人艇路径规划算法收敛慢、精度低的问题,为提高算法适应性与寻优能力,提出一种多目标粒子群−蚁群融合的无人艇路径规划算法。【方法】首先,分析蟹塘环境及养殖规律等因素,建立静态水深栅格环境模型;其次,针对覆盖遍历式投饵存在局部点投喂不足及路径次优的问题,通过对惯性参数与学习因子的非线性调整,提出基于多目标的改进粒子群算法(Particle swarm optimization,PSO);然后,调整蚁群算法的初始信息素,并对蚁群算法的信息素挥发因子和启发期望函数自适应改进,提出自适应优化蚁群算法(Ant colony optimization,ACO);最后,为解决单一算法寻优不足,利用融合PSO-ACO算法,实现无人艇多目标全局路径规划。【结果】仿真结果表明:不同环境投饵策略下,PSO-ACO算法在对多目标路径寻优时,不仅环境适应性好,而且提高了寻优效率和精度,运行时间节省了32%,路径距离缩短了9.78%,迭代次数降低了62.88%,拐点数目减少了44.45%。【结论】所提出多目标点的路径规划算法适用于环境可变的蟹塘养殖,具有较好的应用价值。

基于二分法和控制信息素量的改进蚁群算法

为弥补蚁群算法易陷入局部最优、收敛速度较慢等不足,提出一种基于二分法和控制信息素量的改进蚁群算法。在每次迭代结束时,利用二分法放弃行走路程较远的半数蚁群的信息素,使收敛速度得到提高;利用3-opt局部优化方法提高解的精度;通过控制信息素量动态调整蚁群选择路径的概率,避免算法早熟;将改进的算法应用于旅行商问题。实验结果表明,该算法在寻优能力、可靠性、收敛速度以及稳定性方面均表现出明显的优越性。