一种基于改进的蚁群优化算法的三维空间路径搜索算法

针对传统二维平面的随机搜索算法——蚁群优化算法不能满足三维空间路径搜索以及快速性要求等问题,提出了改进的方法。基于栅格离散方法创建空间环境地图,通过引入搜索主方向、可视域及可行域等定义将搜索算法扩展至三维空间,建立了三维空间下的蚁群优化算模型,并给出该方法的搜索流程。而后根据此模型及流程实现了仿真程序,得到仿真结果,并与传统方法做出了分析比较,得出该改进方法具有较快的收敛速度、较好的稳定性和更高的计算效率。

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

Buffer allocation method of serial production lines based on improved ant colony optimization algorithm

Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.

A Reliable Routing Algorithm with Network Coding in Internet of Vehicles

The intelligent vehicle network uses advanced information technology to establish an efficient integrated vehicle transport system, which has received great attention in industry and academia, lnternet of Vehicles （loV） in an urban environment is operated in a wireless environment with high bit error rate and interference. In addition, the wireless link between vehicles is likely to be lost. All of this makes it an important challenge to provide reliable mobile routing in an urban traffic environment. In this paper, a reliable routing algorithm with network coding （RR_ NC） is proposed to solve the above problems. A routing node sequence is discovered in IoV from source to destination by multi-metric ant colony optimization algorithm （MACO）, and then clusters are formed around every node in the sequence. By adding linear encoding into the transmission of data between vehicle＇s clusters, the RR_NC provides much more reliable transmission and can recover the original message in the event of disorder and loss of message. Simulations are taken under different scenarios, and the results prove that this novel algorithm can deliver the information more reliably between vehicles in real-time with lower data loss and communication overhead.

Wireless sensor networks in relic protection:deployment methodology and cross-layer design

This paper describes the deployment optimization technology and the cross-layer design of a surveil-lance WSN system applied in relic protection.Facing the typical technical challenges in the applicationcontext of relic protection,we firstly propose a deployment technology based on ant colony optimization al-gorithm(DT-ACO)to overcome the difficulties in communication connectivity and sensing coverage.Meanwhile,DT-ACO minimizes the overall cost of the system as much as possible.Secondly we proposea novel power-aware cross-layer scheme(PACS)to facilitate adjustable system lifetime and surveillanceaccuracy.The performance analysis shows that we achieve lower device cost,significant extension of thesystem lifetime and improvement on the data delivery rate compared with the traditional methods.

Hopfield neural network based on ant system

Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters. This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

Efficient Configuration Space Construction and Optimization for Motion Planning

The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this paper, we survey some of our recent work on solving two important challenges related to configuration spaces： ~ how to efficiently compute an approximate representation of high-dimensional configuration spaces; and how to efficiently perform geometric proximity and motion planning queries （n high-dimensional configuration spaces. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We also present parallel GPU-based algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPU-based parallel k-nearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning.

Optimization design of drilling string by screw coal miner based on ant colony algorithm

It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat- egy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re- search screw coal mine machine.

Ant colony system algorithm for the optimization of beer fermentation control

Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.

An adaptive ant colony system algorithm for continuous-space optimization problems

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling

This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling(IPPS). Generally, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimization algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.

Ant Colony Optimization for Task Allocation in Multi-Agent Systems

Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm.

A Biologic Behavior Simulation: Living Migration Algorithm (LMA)

Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algorithm (LMA). The original contributions of LMA are three essential attributes of each individual: the minimal life-needs which are the necessaries for survival, the migrating which is a basal action for searching new living space, and the judging which is an important ability of deciding whether to migrate or not. When living space of all individuals can satisfy the minimal life-needs at some generation, they are considered as the optimal living places where objective functions will obtain the optima. LMA may be employed in large-scale computation and engineering field. The paper mostly operates LMA to deal with four non-linear and heterogeneous optimizations, and experiments prove LMA has better performances than Free Search algorithm.

Cross-Layer Design and Ant-Colony Optimization Based Routing Algorithm for Low Earth Orbit Satellite Networks

To improve the robustness of the Low Earth Orbit(LEO) satellites networks and realise load balancing, a Cross-layer design and Ant-colony optimization based Load-balancing routing algorithm for LEO Satellite Networks(CAL-LSN) is proposed in this paper. In CALLSN, mobile agents are used to gather routing information actively. CAL-LSN can utilise the information of the physical layer to make routing decision during the route construction phase. In order to achieve load balancing, CALLSN makes use of a multi-objective optimization model. Meanwhile, how to take the value of some key parameters is discussed while designing the algorithm so as to improve the reliability. The performance is measured by the packet delivery rate, the end-to-end delay, the link utilization and delay jitter. Simulation results show that CAL-LSN performs well in balancing traffic load and increasing the packet delivery rate. Meanwhile, the end-to-end delay and delay jitter performance can meet the requirement of video transmission.

Distribution Network Reactive Power Optimization Based on Ant Colony Optimization and Differential Evolution Algorithm

Due to the inherent complexity, traditional ant colony optimization （ACO） algorithm is inadequate and insufficient to the reactive power optimization for distribution network. Therefore, firstly the ACO algorithm is improved in two aspects： pheromone mutation and re-initialization strategy. Then the thought of differential evolution （DE） algorithm is proposed to be merged into ACO, and by producing new individuals with random deviation disturbance of DE, pheromone quantity left by ants is disturbed appropriately, to search the optimal path, by which the ability of search having been improved. The proposed algorithm is tested on IEEE30-hus system and actual distribution network, and the reactive power optimization results are calculated to verify the feasibility and effectiveness of the improved algorithm.

RFES: a real-time fire evacuation system for Mobile Web3D

There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.

Tropical cyclone spiral band extraction and center locating by binary ant colony optimization

Tropical cyclone (TC) center locating is crucial because it lays the foundation for TC forecasting. Locating TC centers, usually by manual means, continues to present many difficulties. Not least is the problem of inconsistency between TC center locations forecast by different agencies. In this paper, an objective TC center locating scheme is developed, using infrared satellite images. We introduce a pattern-matching concept, which we illustrate using a spiral curve model. A spiral band model, based on a spiral band region, is designed to extract the spiral cloud-rain bands (SCRBs) of TCs. We propose corresponding criteria on which to score the fitting value of a candidate template defined by our models. In the proposed scheme, TC location is an optimization problem, solved by an ant colony optimization algorithm. In numerical experiments, a minimal mean distance error of 17.9 km is obtained when the scheme is tested against best-track data. The scheme is suitable for TCs with distinct SCRBs or symmetrical central dense overcast, and for TCs both with and without clear eyes.

A hybrid multiple damages detection method for plate structures

A hybrid method is proposed to properly identify multiple damages for plate structures in this work. In the stage of damage localization, singular value decomposition （SVD） is applied to reveal singularities in modal shapes, and hence to detect the damage locations. In the stage of damage quantification, based on the detected location information ant colony optimization （ACO） algorithm is introduced to estimate damage severity by searching for damage evaluation database, which reveals the relationship between the natural frequencies and the damage severity. The modal shapes and the natural frequencies required in damage localization and quantification are obtained via the wavelet finite element method. The numerical simulation and experimental investigation are carried out to test the performance of the hybrid method for free aluminum plates with multiple damages. And the results indicate that the proposed method is effective to identify multiple damages of plate structures with reasonable precision.

Gear Fault Recognition and Diagnosis Based on Ant Colony Optimization Algorithm

introduce a new kind of swarm intelligence algorithm, the Ant Colony Optimization （ACO） algorithm. Propose a clustering analysis model based on ACO, apply the model to recognition and diagnosis of operation state for gearbox. Testing four kinds of gears and clustering some characteristic parameters of the gear vibration signal, the conclusion shows that this method can recognize running state with accuracy and all speed. It is a new method for fault recognition and diagnosis.