Swarm Intelligence for Electric and Electronic Engineering (original) (raw)
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Sensors, 2011
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. Even though cheap robot hardware has become widely accessible on the market, application of multi-robot systems in our everyday lives is limited. Nevertheless, due to the potential that this field has, great efforts have been made by various research groups to investigate the algorithms for coordination and control of multi-robot systems consisting of large number of units. In order to unify the research under a single framework, some researchers have proposed different multi-robot system taxonomies. Dudek et al.
Distributed Bees Algorithm for Task Allocation in Swarm of Robots
IEEE Systems Journal, 2012
In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.
Building a swarm of robotic bees
Swarm Robotics refers to the application of Swarm Intelligence techniques where a desired collective behavior emerges from the local interactions of robots with one another and with their environment. In this paper, a modified Bees Algorithm is proposed for multi-target search and coverage by an autonomous swarm of robotic “bees”. The objective is to find targets in an unknown area, send their estimated locations and fitness values to other robots in swarm which then provide the coverage of the found targets in a self-organized, decentralized way. The robots are equipped with ultrasonic sensors for obstacle avoidance, thermal sensors for target detection, and ZigBee modules for local communication. For the experiments, a small swarm of robots was built to test the performance of the modified Bees Algorithm. The experimental results show that the swarm is self-organized, decentralized and adaptive, and it can be successfully applied to the unknown area search and coverage.
Distributed Task Allocation in Swarms of Robots
Systems Journal, IEEE, 2011
This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm's performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA's control parameters by means of a genetic algorithm. rescue, communication networks, monitoring, surveillance, cleaning, maintenance, and so forth. In order to efficiently perform their tasks, robots require high level of autonomy and cooperation. They use their sensing abilities to explore an unknown environment and deploy on the sites of interest, i.e. targets. However, the coordination of a robot swarm is not an easy problem, especially when the resources for the deployment task are limited. Such a large group of robots, if organized in a centralized manner, could experience information overflow that can lead to the overall system failure . For this reason, the communication between the robots can be realized through local interactions, either directly with one another or indirectly via environment .
A Comprehensive Survey on Artificial Bee Colony Algorithm as a Frontier in Swarm Intelligence
The nature is an intrinsic basis of idea for researchers continuously working in the area of optimization. The Artificial Bee Colony (ABC) algorithm imitates the foraging behavior of real honeybees and it is effectively used to solve multi-model and complex problems. Various strategies is developed on the behavior of honeybees but ABC is the most popular among all. The ABC algorithm is used to get rid of difficult real-world optimization problems that are not solvable by conventional methods. This paper presents a state-of-the-art study of ABC and its latest modifications with in-depth evaluation and analysis of recent popular variants of ABC.
A survey: algorithms simulating bee swarm intelligence
Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.
Overview of Algorithms for Swarm Intelligence
Computational Collective Intelligence. Technologies and Applications, 2011
Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016
In this paper a honeybee inspired collective-decision-making algorithm called BEECLUST is studied in a swarm of autonomous robots and the performance of the swarm is investigated in different conditions. The algorithm has low requirements thus it is promising for implementation in robots with low resources. Here the algorithm is applied in swarms of improved e-puck robots in three different conditions in order to study the strengths and limitations of the algorithm. The collective system demonstrated a high performance in adapting to a dynamic environment as well as a very low sensitivity to additional robots with malfunctioning sensors. On the other hand the system shows an strong response to robots that act as social seeds influencing the decisionmaking of the swarm.
Swarm Intelligence: State of Art
2019
The paper explores Swarm Optimization and its application on different field. Here, an attempt has been made to achieve the inline relationship between the Swarm Optimization and Genetic Algorithm. We have worked to solve the problems related to optimization using those various algorithms. In this paper, we have provided solution to minimize the population of different colonies especially using cost functions.
Solving multi-objective optimization problem with the desired boundary has been a great deal since few decades. This has paved way for many search algorithms which provides reasonable optimal value convincing running period. Of all the search algorithms, the swarm based algorithms were found promising in obtaining the optimal solution with minimal convergence time. This article presents few of such search algorithms that has been developed from the inspiration honeybee's lifestyle. This could be regarded as intelligent optimization tools. Some searches even uses greedy criterion for attaining the solution if and only if it satisfies the objective function.