Multi dimensional taxonomy of bio inspired systems based on model driven architecture

الملخص EN

The biological metaphor is an analogy between the biological world and the artificial world that enables us to benefit from artificial approaches by imitating some biological aspects while ignoring others.

The biological metaphors, also called bio-inspired approaches, depend not only, on the biological field considered, but also on our understanding of that field and the paradigms and means we use to extract practical and useful elements to model some aspects of that field.

Today, there is a huge number of metaphors which are different by their very nature and this number is expected to increase according to our inspiration capabilities.

In front of this increasing numbers of metaphors it becomes necessary to define the main features of each one in order to evaluate their practical impact, to compare them, to ease their learning and use, to combine them, etc.

Finding the main or common features of bio-inspired approaches is not an easy task.

Although significant achievement can be noticed in some fields like artificial neural networks or genetic algorithms, a common agreement on definitions and concepts of a huge number of bio-inspired approaches is still lacking.

In this article, we propose a multidimensional approach based on the Model Driven Architecture (MDA) to describe conceptually a wide range of bio-inspired approaches.

Our starting point is to consider that each bio-inspired approach has two aspects: Structural aspect and Behavioural aspect.

While the structural aspect is concerned with the involved elements and their relationships, the behavioural aspect deals with the process by which a computing is achieved in an artificial system based on the considered bio-inspired approach.

Our choice of the MDA paradigm is justified by its ability to describe uniformly various intricate processes and artefacts involved in the development of software systems.

As a preliminary result, our description approach proved to be effective in characterizing a wide range of bio-inspired systems.