A model of the bee hive that clearly separates the self-organizing decision-making behaviour of the bees in the hive and the problem-specific behaviour of the bees outside the hive is presented. This separation allows for the applications of the model for different problem domains. Results of the application to three problem domains are presented - web search, function optimization and hierarchical optimization.

Bee hive at work: A problem solving, optimizing mechanism

Man has always tried to make new systems, which can do his difficult tasks. Manage and control of such new complex systems is new challenge in our life. Today, one solution to deal with this challenge is using advantage of features of autonomy. It means that instead of managing and controlling the entire system, each component or at least part of the system can manage or control itself even in unpredictable situations. In this paper, we proposed a new algorithm, is named “self-configurable particle swarm optimization algorithm (SCPSO)”. This method can control a system without outside observers in decentralize fashion. Each particle can makes decision to find optimum position for itself even when there is not enough information from whole system. By this method, particles can work autonomously even in unpredictable situations. We examine our proposed algorithm in both static and dynamic environments. This algorithm is a good method for using in disaster management or crisis management. Results show this method is a successful, especially in communication less environments.

Using Self-Configurable Particle Swarm Optimization for Allocation Position of Rescue Robots

A model of the bee hive that clearly separates the self-organizing decision-making behaviour  of the beesin the hive andthe problem-speciﬁc behaviour of the bees outsidethe hive is presented. This separation allows for the applications of the model for different problem domains. The Bee Hiveat Work model has been applied to several problem domains. Results of the application to three problem domains are presented - web search, function optimization and hierarchical optimization. In web search, the model has been successful in following a story as it was developing on web sites. Another its application was able to ﬁnd global optima of various mathematical functions. We explored also to some extent a rather novel idea of building a hierarchical hive.

The Bee Hive At Work: Exploring its Searching and Optimizing Potential

One of the great challenges attracting researchers of various disciplines has been the question: how to solve it? Problem solving has been an important topic of research e.g. in artificial intelligence and many remarkable results have been achieved there. However, the challenge is too complex that only one discipline, or one paradigm, or one approach can prevail. Within artificial intelligence, there has been a natural tendency to relate somehow artificial problem solvers to human ones: either in the way they behave, or in the way they actually do it. However, nature seems to offer a much broader wealth of inspiration. Among other sources of inspiration, social insects seem to play a distinct role. Indeed, their behaviour is interesting not only individually, but also in a collective, or a colony, or a swarm. From among the kinds of social insects, ants have gained perhaps the biggest attraction of problem solving researchers. Their ability to solve classes of optimisation tasks with a help of pheromones has become infamous. Only relatively recently, other kinds of social insects began to draw attention of researchers. We shall devote our attention to honey bees (apis mellifera). Honey bees are relative newcomers, although the very idea of taking inspiration from a bee hive model to represent knowledge for a knowledge based system can be traced back to 1986 (Bullock, 1986). We are interested in taking inspiration from honey bees for devising of approaches to problem solving and in particular to automated, or computer based problem solving. Behaviour of honey bees is a subject of study of other disciplines, in particular of biologists. Their studies have proven to be extremely useful (Beekman, 2007), (Biesmeijer, 2001), (Bonabeau, 1996), (Camazine, 1991), (Selley, 1991), (Zhang, 2006). Without them, we would not have the underlying knowledge on how honey bees behave in nature. From the problem solving perspective, much of the research has been concentrated on the optimisation task, perhaps in reference to the success of ant colonies (Karaboga, 2005), (Pham, 2006), (Teodorovic, 2005), (Tovey, 2005), (Wong, 2008).

/pdf/exploring-the-bee-hive-metaphor-as-a-model-for-problem-vjck3ai713.pdf

Exploring the Bee Hive Metaphor as a Model for Problem Solving: Search, Optimisation, and More

A model of the bee hive that clearly separates the self-organizing decision-making behaviour of the bees in the hive and the problem-specific behaviour of the bees outside the hive is presented. This separation allows for the applications of the model for different problem domains. The Bee Hive at Work model has been applied to several problem domains. Results of the application to three problem domains are presented - web search, function optimization and hierarchical optimization. In web search, the model has been successful in following a story as it was developing on web sites. Another its application was able to find global optima of various mathematical functions. We explored also to some extent a rather novel idea of building a hierarchical hive.

Based on the metaphor of specialization and cooperation in hierarchical social organization, a new particle swarm optimization (PSO) algorithm, hierarchical subpopulation PSO (HS-PSO), is proposed. In HS-PSO, the entire population is divided into several subpopulations which are arranged in a hierarchy. The subpopulations at the same level of the hierarchy evolve relative independently and cooperate with each other via their respective best particles. For the particles at different levels, different tasks are assigned. Two versions of HS-PSO which use the same or different kinds of PSO algorithms for the particles at different levels are presented. The efficiency of HS-PSO is verified by the simulation results.

/pdf/a-hierarchical-subpopulation-particle-swarm-optimization-18ryi35210.pdf

A Hierarchical Subpopulation Particle Swarm Optimization Algorithm

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A Hierarchical Subpopulation Particle Swarm Optimization Algorithm