First International Conference Modelling and Development of Intelligent Systems Sibiu - Romania, 22-25 October, 2009 Wasp based algorithms and applications Dana Simian Abstract The aim of this paper is to present the wasp based computational model and many applications of wasp based algorithms. A general frame for designing a wasp based algorithm, starting from the classical problem of task allocation in a factory, is realized. The most important characteristics of the wasp computational model are underlined and the way of particularization of these characteristics for each problem is presented. Original applications of wasp based algorithms in modeling multi agent systems, in solving optimization problems and in building a reinforcement scheme for a stochastic learning system are presented. 1 Introduction In the last ten years methods and models inspired from the behavior of social insects like ants and wasps have gained increasing attention. Computational analogies to adaptive natural multi- agent systems have served as inspiration for multi-agent optimization and control algorithms in a variety of domains and contexts. Self-organization, direct and indirect interactions between individuals are important characteristics of these natural multi-agent systems. Metaheuristics inspired from nature represent an important approach to solve NP-difficult problems. It is important to identify when a problem can be solved using these kind of methods. It is the goal of this article to identify some type of problems which can be solved using wasp computational based algorithms and to give a general frame for design these algorithms. The remainder of this paper is organized as follows: in section 2 we present the wasp computational model and the classical problem of task allocation in a factory. Starting from this problem we realize a frame for design models based on wasp behavior and present many models for multi agents systems, from different fields. In section 3 we present a reinforcement scheme for stochastic learning automata, based on wasp behavior. In section 4 we present a wasp based algorithm for improving the performances of a co-mutation operator. The co-mutation operator is used in a hybrid approach for building multiple SVM kernels. Conclusions are presented in section 5. 2 Wasp behavior based algorithms in modeling multiagent systems The self organization model that takes place within a colony of wasps was used for solving large complex problems, most of them with a dynamic character. In [18], Theraulaz et al. present 1 Service Oriented Architecture Considerations for Distributed Intelligent Control of Modern Manufacturing Systems Gabriela Varvara, Carlos Pascal, Doru Pănescu Abstract The paper presents some architectural issues on the implementation of software systems designed to support the control of modern manufacturing processes. In the introduction some challenging aspects of modern manufacturing and market problems are pointed out. Then the concepts of an architectural manufacturing paradigm, based on dynamic recursive organization of holons for reconfigurable systems, are synthetically presented. In order to implement control systems that support the holonic recursive decomposition, the authors add to classical multi-agent distributed implementation the Service Oriented Architecture (SOA) characteristics. This development is justified by the necessity to sustain the dynamic cooperation among holons identified during the modeling decomposition phase, as exemplified in the paper, and, also, must be adapted to the characteristics of manufacturing systems as illustrated by the proposed architecture. 1 Introduction 1.1 The Modern Manufacturing Systems – problems, challenges, scenarios The 21 st century market problems required significant changes within the manufacturing systems and the development of associated emerging technologies. Some critical business aspects that need to be addressed are worthwhile to be mentioned [3]: the regular reactivity to “rush orders” and new arrived specification from the customer , an active balance between volume and variety of the production within a single shop floor, the demand for short delivery times for customer-specific products and the need to tightly integrate all the supply chains to hold minimal reserve stocks. All these problems result from everyday plant scenarios and imply a specific response from the manufacturing system as detailed in the next paragraph. The “rush order” reactivity scenario has some characteristics: a rush order can be introduced at any time, has electronic financial credit to buy manufacturing services, is able to negotiate with the existing orders to gain access to services as soon as possible and behaves in order to maximize its profit limits. A mass customization scenario involve flexible choice to meet together different orders that reflect changing of customer requirements with minimizing the stocks (warehouse and out). Another usual scenario refers to the existence of tightly integrated supply chains; the manufacturing system behaves in a proactive manner to find the optimal solution to outsourcing some production by maximal utilization of available resources through cooperation for real-time reaction to changing demands. The above mentioned scenario is tightly coupled with another one 299