Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11

Swarm Intelligence Related Abstracts

11 Intelligent Swarm-Finding in Formation Control of Multi-Robots to Track a Moving Target

Authors: Anh Duc Dang, Joachim Horn

Abstract:

This paper presents a new approach to control robots, which can quickly find their swarm while tracking a moving target through the obstacles of the environment. In this approach, an artificial potential field is generated between each free-robot and the virtual attractive point of the swarm. This artificial potential field will lead free-robots to their swarm. The swarm-finding of these free-robots dose not influence the general motion of their swarm and nor other robots. When one singular robot approaches the swarm then its swarm-search will finish, and it will further participate with its swarm to reach the position of the target. The connections between member-robots with their neighbours are controlled by the artificial attractive/repulsive force field between them to avoid collisions and keep the constant distances between them in ordered formation. The effectiveness of the proposed approach has been verified in simulations.

Keywords: Swarm Intelligence, Multi-Agent Systems, formation control, potential field method, obstacle avoidance

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10 Evolved Bat Algorithm Based Adaptive Fuzzy Sliding Mode Control with LMI Criterion

Authors: P.-W. Tsai, C.-Y. Chen, C.-W. Chen

Abstract:

In this paper, the stability analysis of a GA-Based adaptive fuzzy sliding model controller for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving FLC rules. Then, FLC rules and the consequent parameter are decided on via an Evolved Bat Algorithm (EBA). After this, we guarantee a new tracking performance inequality for the control system. The tracking problem is characterized to solve an eigenvalue problem (EVP). Next, an adaptive fuzzy sliding model controller (AFSMC) is proposed to stabilize the system so as to achieve good control performance. Lyapunov’s direct method can be used to ensure the stability of the nonlinear system. It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality (LMI) problem. Finally, a numerical simulation is provided to demonstrate the control methodology.

Keywords: Swarm Intelligence, adaptive fuzzy sliding mode control, Lyapunov direct method, evolved bat algorithm

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9 Half-Circle Fuzzy Number Threshold Determination via Swarm Intelligence Method

Authors: C. Y. Chen, P. W. Tsai, C. W. Chen, J. W. Chen

Abstract:

In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well.

Keywords: Swarm Intelligence, half-circle fuzzy numbers, predictions, Lyapunov method

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8 A Parallel Implementation of Artificial Bee Colony Algorithm within CUDA Architecture

Authors: Celal Öztürk, Selcuk Aslan, Dervis Karaboga

Abstract:

Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Swarm Intelligence, parallelization, GPU Computing, Artificial Bee Colony Algorithm

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7 An Algorithm to Depreciate the Energy Utilization Using a Bio-Inspired Method in Wireless Sensor Network

Authors: Navdeep Singh Randhawa, Shally Sharma

Abstract:

Wireless Sensor Network is an autonomous technology emanating in the current scenario at a fast pace. This technology faces a number of defiance’s and energy management is one of them, which has a huge impact on the network lifetime. To sustain energy the different types of routing protocols have been flourished. The classical routing protocols are no more compatible to perform in complicated environments. Hence, in the field of routing the intelligent algorithms based on nature systems is a turning point in Wireless Sensor Network. These nature-based algorithms are quite efficient to handle the challenges of the WSN as they are capable of achieving local and global best optimization solutions for the complex environments. So, the main attention of this paper is to develop a routing algorithm based on some swarm intelligent technique to enhance the performance of Wireless Sensor Network.

Keywords: Swarm Intelligence, Routing, Wireless Sensor Network, MPRSO

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6 A Survey on Intelligent Traffic Management with Cooperative Driving in Urban Roads

Authors: B. Karabuluter, O. Karaduman

Abstract:

Traffic management and traffic planning are important issues, especially in big cities. Due to the increase of personal vehicles and the physical constraints of urban roads, the problem of transportation especially in crowded cities over time is revealed. This situation reduces the living standards, and it can put human life at risk because the vehicles such as ambulance, fire department are prevented from reaching their targets. Even if the city planners take these problems into account, emergency planning and traffic management are needed to avoid cases such as traffic congestion, intersections, traffic jams caused by traffic accidents or roadworks. In this study, in smart traffic management issues, proposed solutions using intelligent vehicles acting in cooperation with urban roads are examined. Traffic management is becoming more difficult due to factors such as fatigue, carelessness, sleeplessness, social behavior patterns, and lack of education. However, autonomous vehicles, which remove the problems caused by human weaknesses by providing driving control, are increasing the success of practicing the algorithms developed in city traffic management. Such intelligent vehicles have become an important solution in urban life by using 'swarm intelligence' algorithms and cooperative driving methods to provide traffic flow, prevent traffic accidents, and increase living standards. In this study, studies conducted in this area have been dealt with in terms of traffic jam, intersections, regulation of traffic flow, signaling, prevention of traffic accidents, cooperation and communication techniques of vehicles, fleet management, transportation of emergency vehicles. From these concepts, some taxonomies were made out of the way. This work helps to develop new solutions and algorithms for cities where intelligent vehicles that can perform cooperative driving can take place, and at the same time emphasize the trend in this area.

Keywords: Swarm Intelligence, Connected Vehicles, Cooperative Driving, intelligent traffic management, smart driving, urban road

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5 Software Architecture Optimization Using Swarm Intelligence Techniques

Authors: Arslan Ellahi, Syed Amjad Hussain, Fawaz Saleem Bokhari

Abstract:

Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them.

Keywords: Swarm Intelligence, Complexity, dimensions, rapid evolution

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4 An Application of Path Planning Algorithms for Autonomous Inspection of Buried Pipes with Swarm Robots

Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

Abstract:

This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: Swarm Intelligence, Vehicle Routing Problem, autonomous inspection, buried pipes, stigmergy

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3 Probabilistic Approach of Dealing with Uncertainties in Distributed Constraint Optimization

Authors: Sagir Muhammad Yusuf, Chris Baber

Abstract:

In this paper, we describe how Bayesian inferential reasoning contributes to obtaining an optimal prediction for distributed constraint optimization problems (DCOP) with uncertainties. The DCOP functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an optimal prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOP, which is the current issue in the DCOP community. We show Bayesian inference formalise distributed situation-awareness (DSA) with uncertainties and missing data for multi-agent mission. Future work focuses on augmenting existing dynamic DCOP algorithms for multi-agent information merging.

Keywords: Swarm Intelligence, Multi-Agent Reasoning, Bayesian Reasoning, DCOP

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2 Inferential Reasoning for Heterogeneous Multi-Agent Mission

Authors: Sagir Muhammad Yusuf, Chris Baber

Abstract:

We describe issues bedeviling the coordination of heterogeneous multi-agent missions. We explain both Bayesian and agents' presumptions inferential reasoning and the possibility of their extension to cope with heterogeneous multi-agent belief variation. Bayesian Belief Network was used in modelling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases were used in training the network using gradient descent and expectation-maximization algorithms. The output network is a well-train Bayesian Belief Network for making optimal inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness. Future work will investigate the possibility of heterogeneous agents' belief fusion.

Keywords: Swarm Intelligence, multi-robot coordination, multi-agent system, autonomous system, distributed constraint optimization problem

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1 Multi-Agent Searching Adaptation Using Levy Flight and Inferential Reasoning

Authors: Sagir Muhammad Yusuf, Chris Baber

Abstract:

In this paper, we describe how agents adapt to different environments using Bayesian inferential reasoning and learning. Bayesian belief network was used to monitor agents’ knowledge about their environment, and cases are recorded for the network training using expectation-maximization or gradient descent algorithm (performance seems to be the same). We model the problem as distributed constraint optimization (DCOP) and use multi-agent searching as a use case. Experiment results prove the use of average or little data to obtain accurate predictions. We change the environment and monitor agents’ adaptability by recording their prediction accuracy rate, logarithm loss, and Brier score. Our learning algorithms can also handle environment dynamism, uncertainties, and missing data, which are the most significant challenge in the DCOP community. Future works focus on determining the minimum number of a sample that can yield proper network training.

Keywords: Swarm Intelligence, multi-robot coordination, multi-agent system, autonomous system, distributed constraint optimization problem, Levy flight

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