基于群集智能算法的移动机器人路径规划研究

本文提出一种新的群集智能算法,在用Dijkstra算法基于链接图建模的地图中得到一个最优解的可行空间后,再用粒子群算法或蚂蚁算法优化得到全局的最优路径。因为群集智能算法是一种概率搜索算法,没有集中控制约束条件,不会因为个别个体的故障影响整个问题的求解,具有较强的鲁棒性,所以在机器人全局路径规划应用中具有较显著的优点。仿真结果表明了算法的有效性,是机器人路径规划的一个较好的方法。     

An effective clinical decision support system using swarm intelligence

The Journal of Supercomputing - As healthcare organizations collect a large volume of data on a daily basis, there is an absolute necessity to extract valuable information from them, owing to the...     

Collision avoidance between UAV clusters using swarm intelligence techniques

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.     

Designing adaptive learning itineraries using features modelling and swarm intelligence

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user??s success on the course. Therefore, the path can change in order to adapt itself to learners?? preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.     

Animal–robots collective intelligence

In this paper we try to define – as ethologists – the easiest ways for creating such a synergy around a common project: mixed groups of interacting animals and robots. The following aspects are explored. (1) During this century, ethology has accumulated numerous results showing that animals' interactions could be rather simple signals and it is possible to interact with animals not only by mimicking their behaviors but also by making specially designed and often simple artifacts. (2) The theory of self-organization in animal societies shows that very simple, but numerous, interactions taking place between individuals may ensure complex performances and produce Collective Intelligence (CI) at the level of the group. This context is the most interesting to develop mixed animal–robots interactions. (3) An experiment using an artifact interacting within a CI system in the wild (gull flocks) is developed. (4) Cases of robots making CI on their own have been developed. (5) Considering (4) and (5), what are the expected difficulties to mix robots and animals in CI systems. (6) Why develop such mixed societies? The control of interactions between artificial systems and living organisms is a key aspect in the design of artificial systems, as well as in many agricultural, medical, scientific and technical fields. Such developments refer generally to human–robots interactions, leading to further complexity of the behavior and algorithms of robots. However, complex performances do not always require complex individual behavior and interesting developments may also refer to simpler interactions. As far as we know, experiments studying animal–robots interactions are rather anecdotal, with a naíve point of view on animal behavior and are often published in non-scientific journals. However, we are very convinced that robotics has much to learn from ethology while robotics in turn may surely help ethology to explore animal behavior. In this paper we try to define – as ethologists – the easiest ways for creating such a synergy around a common project: mixed groups of interacting animals and robots.     

Generating collective wall-jumping behavior for a robotic swarm with self-teaching automatic curriculum learning

Artificial Life and Robotics - Swarm robotics (SR) is a research field about how to design a large number of robots so that they can generate meaningful collective behaviors. One of the promising...     

Herd Clustering: A synergistic data clustering approach using collective intelligence

Traditional data mining methods emphasize on analytical abilities to decipher data, assuming that data are static during a mining process. We challenge this assumption, arguing that we can improve the analysis by vitalizing data. In this paper, this principle is used to develop a new clustering algorithm.Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances are represented by moving particles. Particles attract each other locally and form clusters by themselves as shown in the case studies reported. To demonstrate its effectiveness, the performance of HC is compared to other state-of-the art clustering methods on more than thirty datasets using four performance metrics. An application for DNA motif discovery is also conducted. The results support the effectiveness of HC and thus the underlying philosophy.     

Optimal design of a scaled-up PRO system using swarm intelligence approach

In this study,the pressure-retarded osmosis (PRO) process is optimized using Harris hawks optimization (HHO)-based maximum power point tracking (MPPT) technolog...     

Analyzing customer behavior from shopping path data using operation edit distance

Radio frequency identification (RFID) technology has been successfully applied to gather customers’ shopping habits from their motion paths and other behavioral data. The customers’ behavioral data can be used for marketing purposes, such as improving the store layout or optimizing targeted promotions to specific customers. Some data mining techniques, such as clustering algorithms can be used to discover customers’ hidden behaviors from their shopping paths. However, shopping path data has peculiar challenges, including variable length, sequential data, and the need for a special distance measure. Due to these challenges, traditional clustering algorithms cannot be applied to shopping path data. In this paper, we analyze customer behavior from their shopping path data by using a clustering algorithm. We propose a new distance measure for shopping path data, called the Operation edit distance, to solve the aforementioned problems. The proposed distance method enables the RFID customer shopping path data to be processed effectively using clustering algorithms. We have collected a real-world shopping path data from a retail store and applied our method to the dataset. The proposed method effectively determined customers’ shopping patterns from the data.     

Solving cold-standby reliability redundancy allocation problems using a new swarm intelligence algorithm

A powerful new hybrid algorithm based on Simplified Swarm Optimization (SSO), Elite Selection and Boundary Search (BS), called the SEB, is proposed for solving the cold-standby reliability redundancy allocation problem (RRAP). The RRAP is a famous mixed-integer nonlinear programming problem in system design that requires that the reliability objective be set to satisfy the resource consumption constraint. In order to balance the exploration and exploitation abilities, the proposed SEB implements a new SSO to update the solutions, an elite selection is performed to select the solutions for subsequent iterations, and the new BS improves the best solution. The performance of the proposed SEB is evaluated by comparing the results with those obtained using existing algorithms and three RRAP benchmark problems taken from the literature.     

Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

Cloud computing plays a significant role in Healthcare Service (HCS) applications and rapidly improves it. A significant challenge is the selection of Virtual Machine (VM) in order to process a medical request. The optimal selection of VM increases the performance of HCS by minimizing the running time of the medical request and also substantially utilizes cloud resources. This paper presents a new idea for optimizing VM selection using a swarm intelligence approach called Analogous Particle swarm optimization (APSO) which works a cloud computing environment. To compute the running time of a medical request, three parameters are considered: Turnaround Time (TAT), Waiting time (WT), and CPU utilization. In addition, a selected optimal VM is used for predicting kidney disease. Early detection of kidney disease facilitates successful treatment. Here, the neural network is used as an automated technique to diagnose kidney disease. A set of experiments and comparisons were performed to analyze the proposed system (APSO and neural network). The results showed that the APSO model performed well, with an execution time of running all particle is 1 s (50 to 80%). Also, the proposed model improved the system efficiency by 5.6%. The precision of recognizing kidney disease using the neural network was 95.7% which outperfomed five other well-known classifiers.     

Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm

This paper presents the hybrid harmony search algorithm with swarm intelligence (HHS) to solve the dynamic economic load dispatch problem. Harmony Search (HS) is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to hybridize the HS algorithm with the powerful population based algorithm PSO for a better convergence of the proposed algorithm. The main aim of dynamic economic load dispatch problem is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits by using penalty function method. Simulations were performed over various standard test systems with 5 units, 10 units and 30 units and a comparative study is carried out with other recently reported results. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.     

Traffic information interpolation method based on traffic flow emergence using swarm intelligence

Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results.

     

基于群体智能的离群数据挖掘

离群数据挖掘是数据挖掘的重要任务之一。首先分析了离群数据及其挖掘方法,然后根据LF算法和CSI算法,提出了基于群体智能的离群数据挖掘算法,并进行了仿真实验。实验结果显示了基于群体智能的离群数据挖掘算法的有效性。与其它方法相比,该算法避免了用户在设定参数初始值时给算法带来的影响,并且不需要设定初始聚类中心,因此具有更好的鲁棒性。     

Coordinating metaheuristic agents with swarm intelligence

Coordination of multi agent systems remains as a problem since there is no prominent method suggests any universal solution. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems using metaheuristic algorithms. An idea for coordinating metaheuristic agents borrowed from swarm intelligence is introduced in this paper. This swarm intelligence-based coordination framework has been implemented as swarms of simulated annealing agents collaborated with particle swarm optimization for multidimensional knapsack problem. A comparative performance analysis is also reported highlighting that the implementation has produced much better results than the previous works.     

Robots, insects and swarm intelligence

The aim of this paper is to consider the relationships between robots and insects. To this end, an overview is provided of the two main areas in which insects have been implicated in robotics research. First, robots have been used to provide working models of mechanisms underlying insect behaviour. Second, there are developments in robotics that have been inspired by our understanding of insect behaviour; in particular the approach of swarm robotics. In the final section of the paper, the possibility of achieving “strong swarm intelligence” is discussed. Two possible interpretations of strong swarm intelligence are raised: (1) the emergence of a group mind from a natural, or robot swarm, and (2) that behaviours could emerge from a swarm of artificial robots in the same way as they emerge from a biological swarm. Both interpretations are dismissed as being unachievable in principle. It is concluded that bio-robotic modelling and biological inspiration have made important contributions to both insect and robot research, but insects and robots remain separated by the divide between the living and the purely mechanical.