Gpubased parallel implementation of swarm intelligence. In this chapter, we provide an overview of some of the most widely used bioinspired algorithms, especially. A swarm intelligence based task allocation algorithm sita. Antnet 29, a routing algorithm for wired networks based on aco. Swarm intelligence algorithm an overview sciencedirect topics. Design and development of a software system for swarm. The classical particle swarm optimization pso uses the swarming behaviour. It focuses on the research of collective behaviours of a swarm in nature andor social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. The results of the numerical experiment proved that the proposed technique was characterized by higher convergence. Principles and applications of swarm intelligence for adaptive.
Recent algorithms and applications in swarm intelligence research highlights the current research on swarm intelligence algorithms and its applications. All sibased algorithms use multiagents, inspired by the collective behaviour of social insects, like ants, termites, bees, and wasps, as well as from other animal societies like flocks of birds or fish. Swarmalgorithms, as we explore, provide the layer of logic on top for devices to exhibit. Design of randomly deployed heterogeneous wireless sensor. An discussion of swarm intelligence and its attractive features appears in section iii and, in section iv, we present some specific swarmbased algorithms and discuss their applicability and performance. Swarm intelligence is the study of computational systems inspired by the collective intelligence. Swarm intelligence algorithms for feature selection. Kose design and development of a software system for swarm intelligence based research studies 2. Researcharticle design of randomly deployed heterogeneous wireless sensor networks by algorithms based on swarm intelligence joonwoolee1 andwonkim2. The inspiration often comes from nature, especially biological systems.
The essential idea of swarm intelligence algorithms is to employ many simple agents applying almost no rule which in turn leads to an emergent global behavior. Swarm intelligence for routing in communication networks. Bioinspired swarm intelligence algorithms a systematic analysis. Swarm intelligencebased techniques can be used in a number of applications.
In the communities of optimization, computational intelligence and computer science, bioinspired algorithms, especially those swarmintelligencebased algorithms, have become very. Multi swarm optimization is a variant of particle swarm optimization pso based on the use of multiple subswarms instead of one standard swarm. Swarm intelligence mathematical and computer sciences. In this chapter, eight different swarm intelligencebased algorithms are summarized and their working steps are listed. Here, a new version of kh algorithm with elitism strategy, called khe, is proposed. Unfortunately, it is not easy to achieve a global optimum solution. Evolutionary algorithms are search methods that take their inspiration from natural selection and survival of the fittest in the biological world. Many optimization algorithms are based on swarm intelligence, and use populationbased a pproaches. Swarm intelligence based optimization techniques are far. Most will use some sort of three key evolutionary op erators. Most will use some sort of three key evolutionary op. An discussion of swarm intelligence and its attractive features appears in section iii and, in section iv, we present some specific swarm based algorithms and discuss their applicability and performance. Collective intelligence emerges through the cooperation of large numbers of homogeneous agents in the environment. In this paper, we tailor the artificial bee colony abc algorithm to solve the graph coloring problem gcp.
Typical swarm intelligence schemes include particle swarm optimization. Swarm intelligence algorithm an overview sciencedirect. Keywords swarm intelligence, ant colony optimization, association rule mining. Even so, swarmintelligencebased algorithms have been developed and applied in a vast number of applications in optimization, engi neering. It is potentially more powerful than particle swarm optimization and genetic algorithms. A list of swarm intelligence algorithms is presented in table i. Swarm intelligence introduction two principles in swarm intelligence summary selforganization is based on. The basic principle shared is the use of a multitude of agents interacting using stigmergy. Thereby this paper seeks to enhance the readers understanding of these algorithms in particular and swarm intelligence algorithms in general. Swarm based algorithms have recently emerged as a family of natureinspired, population based algorithms that are capable of producing low cost, fast, and robust solutions to several complex problems. The general approach in multiswarm optimization is that. Swarm intelligence based algorithms for data clustering.
Swarm intelligence an overview sciencedirect topics. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence. The popularity of swarm intelligence has also instigated the development of numerous data mining algorithms, which will be discussed in this overview. All of these algorithms have demonstrated their potential to solve many optimization problems. Bioinspired swarm intelligence algorithms a systematic. We also analyze the ways of achieving exploration and exploitation in algorithms. Overview of algorithms for swarm intelligence springerlink. The application of swarm principles to robots is called swarm robotics, while swarm intelligence refers to the more general set of algorithms. Clustering nodes and discretizing movement to increase the.
Request pdf swarm intelligence based algorithms for data clustering this paper provides an introduction of the swarm intelligence with a special emphasis on. Fireworks algorithm fwa is a novel swarm intelligence algorithm under active research 184, 181, 180. Many optimization algorithms are based on swarm intelligence, and use population based a pproaches. A study of chaotic maps producing symmetric distributions. Swarm intelligence based techniques can be used in a number of applications. Ying tan, in gpubased parallel implementation of swarm intelligence algorithms, 2016. In this chapter, a very efficient fwa variant based on. These sibased algorithms can have some advantages over traditional algorithms. A key component of swarm intelligence algorithms sias is the random number generator rng which provide random numbers to drive the stochastic search process. The shortcomings of existing intelligent optimization algorithms are easy to produce premature convergence, easy to fall into local optimal equilibrium states, and poor efficiency at evolutionary late stage. Swarm intelligence algorithms for data clustering 283 as mentioned in grosan et al. In this sense, particle swarm optimization pso, ant colony optimization. This collective intelligence seems to emerge from what are often large groups. Swarm intelligence is an innovative computational way inspired by the collective behavior of social systems.
Swarm intelligencebased algorithms within iot based systems. Request pdf swarm intelligence based algorithms for data clustering this paper provides an introduction of the swarm intelligence with a special emphasis on particle swarm optimization and ant. Introduction multiagent research draws inspiration from the behavior of insects as they provide a good example of cooperation and self organization. In the communities of optimization, computational intelligence and computer science, bioinspired algorithms, especially those swarm intelligence based algorithms, have become very. This paper provides an indepth survey of wellknown optimization algorithms. Much effort is devoted to develop efficient rngs with good statistical.
Swarm intelligence optimization algorithm based on orthogonal optimization abstract. Learn particle swarm optimization pso in 20 minutes. This book synthesizes material that has previously only been available in primary literature. In the proposed abcgcp, a sequence of nodes of the given graph is generated. Xinshe yang, mehmet karamanoglu, in swarm intelligence and bioinspired computation, 20. Recent algorithms and applications in swarm intelligence. The general approach in multi swarm optimization is that. Gpubased parallel implementation of swarm intelligence algorithms. Recent work has involved merging the global search properties of sds with other swarm intelligence algorithms. To determine the solutions performance, the study involved three scenarios. Ying tan, in gpu based parallel implementation of swarm intelligence algorithms, 2016. Mar 30, 2018 particle swarm optimization pso is one of the most wellregarded stochastic, population based algorithms in the literature of heuristics and metaheuristics. The main differences between the kh algorithm and other swarm intelligence algorithms is that the parameters used in the kh algorithm is fully originated from the real krill herd in nature. Pdf swarm intelligencebased algorithms within iotbased.
Swarm intelligence wikimili, the best wikipedia reader. 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. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness. Principles, advances, and applications delivers indepth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. Many swarm optimization algorithms have been introduced since the early 60s, evolutionary programming to the most recent, grey wolf optimization. Multiswarm optimization is a variant of particle swarm optimization pso based on the use of multiple subswarms instead of one standard swarm.
Swarm intelligence optimization algorithm based on. Rng plays a key role in driving the search process. Find out the local interactions between nearby robots to produce largescale group behaviors from the entire swarm often inspired from biological foundations, such as ants, bees, termites, etc. Swarm intelligence yichen hu abstract swarm intelligence is an important concept in arti cial intelligence and computer science with emergent properties. Artificial intelligence, swarm intelligence, data mining. Selected algorithms are briefly explained and compared with each other. Hence, many researchers use a combination of two or more optimization algorithms, which based on swarm intelligence methods, to overcome the drawbacks of the single algorithm. Swarmbased algorithms have recently emerged as a family of natureinspired, populationbased algorithms that are capable of producing low cost, fast, and robust solutions to several complex problems. Design of randomly deployed heterogeneous wireless. In this paper, we carry out a critical analysis of these sibased. Nasa is investigating the use of swarm technology for planetary mapping. These techniques are ant colony optimizer, particle swarm optimizer, artificial bee colony algorithm, glowworm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, and hunting search algorithm. A further comparison was made on the improvisation of above.
Practical applications of swarm intelligence and evolutionary computation. This book constitutes the thoroughly refereed postconference proceedings of the 1st international conference on swarm intelligence based optimization, icsibo 2014, held in mulhouse, france, in may 2014. This algorithm shares many key features with antnet, but has important differences. Performance enhancement of swarm intelligence techniques in. The european space agency is thinking about an orbital swarm for selfassembly and interferometry. Although many differences exist among the proposed techniques, fig. Pdf algorithms for the graph coloring problem based on. The common denominator constituent elements can be used to suggest subtypes for. Also, the study seeks to determine the extent to which swarm intelligence sibased centralized clustering solutions optimal natureinspired algorithms, compared to other approaches, might optimize the wsn features of localization and network coverage. Swarm intelligence in converging digital and physical worlds. Algorithms more tuned to the swarmbot more exhaustively tested parameters explored more kinds of behaviors etc. Antbased control abc6 is another successful swarm intelligence based algorithm designed for telephone networks. Performance enhancement of swarm intelligence techniques. Routing algorithms routing algorithms can be classified as static or dynamic, and centralized or distributed.
Elitism tragedy can prevent the krill population from degrading. A swarm intelligence based task allocation algorithm sita for the computational grid abstract this paper proposes the use of a swarm intelligence based approach sita for task allocation and scheduling in a dynamically reconfigurable environment such as the computational grid. The primary reason is that ba uses a good combination of major advantages of these algorithms in some way. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly. In this paper, we carry out a critical analysis of these sibased algorithms by analyzing their ways to mimic evolutionary operators. A novel hybrid algorithm based on chaotic search and the arti. A new swarm intelligence approach for clustering based on.