Holonic multiagent systems (hmas) offer a promising software engineering approach for developing complex open software systems. However the process of building Multi-Agent Systems (mas) and hmas is mostly different from the process of building more traditional software systems as it introduces new design and development challenges. This paper introduces an agent-oriented software process for engineering complex systems called aspecs. aspecs is based on a holonic organisational metamodel and provides a step-by-step guide from requirements to code allowing the modelling of a system at different levels of details using a set of refinement methods. This paper details the entire aspecs development process and provides a set of methodological guidelines for each process activity. A complete case study is also used to illustrate the design process and the associated notations. aspecs uses uml as a modelling language. Because of the specific needs of agents and holonic organisational design, the uml semantics and notation are used as reference points, but they have been extended by introducing new specific profiles.

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Abstract. This paper presents a multi agent-oriented prototyping approach. It is a generic approach, applicable to a wide range of multi-agent systems. This approach relies on a few assumptions, the most important is that MAS must be described by an organizational model which semantics is given in term of a formal framework. This model allows for a simple description of both individual and collective multi-agent system aspects. The framework we use to give a formal description of this model is based on a multi-formalism approach. We illustrate this approach through a case study.

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We present a self-adaptive and distributed metaheuristic called CoalitionBased Metaheuristic (CBM). This method is based on the Agent Metaheuristic Framework (AMF) and hyper-heuristic approach. In CBM, several agents, grouped in a coalition, concurrently explore the search space of a given problem instance. Each agent modifies a solution with a set of operators. The selection of these operators is determined by heuristic rules dynamically adapted by individual and collective learning mechanisms. The intention of this study is to exploit AMF and hyper-heuristic approaches to conceive an efficient, flexible and modular metaheuristic. AMF provides a generic model of metaheuristic that encourages modularity, and hyper-heuristic approach gives some guidelines to design flexible search methods. The performance of CBM is assessed by computational experiments on the vehicle routing problem.