No abstract

Nowadays bio-inspired approaches are widely used. Some of them became paradigms in many domains, such as Ant Colony Optimization (ACO) and Genetic Algorithms (GA). Despite the inherent challenges of surviving, in the natural world, biological organisms evolve, self-organize and self-repair with only local knowledge and without any centralized control. The analogy between biological systems and Multi-Agent Systems (MAS) is more than evident. In fact, every entity in real and natural systems is easily identified as an agent. Therefore, it will be more efficient to model them with agents. In a simulation context, MAS has been used to mimic behavioural, functional or structural features of biological systems. In a general context, bio-inspired systems are carried out with ad hoc design models or with a one target feature MAS model. Consequently, these works suffer from two weaknesses. The first is the use of dedicated models for restrictive purposes (such as academic projects). The second one is the lack of a design model. In this paper, our contribution aims to propose a generic multi-paradigms model for bio-inspired systems. This model is agent-based and will integrate different bio-inspired paradigms with respect of their concepts. We investigate to which extent is it possible to preserve the main characteristics of both natural and artificial systems. Therefore, we introduce the influence/reaction principle to deal with these bio-inspired multi-agent systems. Povzetek: Avtorji prispevka analizirajo podobnosti med biološkimi in multiagentnimi sistemi in predlagajo Bio-IR-M, integrirano shemo, ki zajema tako genetske algoritme kot npr. modele, temelječe na mravljah.

Rat Swarm Optimization (RSO) is one of the newest swarm intelligence optimization algorithms that is inspired from the behaviors of chasing and fighting of rats in nature. In this paper we will apply the RSO to one of the most challenging problems, which is data clustering. The search capability of RSO is used here to find the best clusters centers. The proposed algorithm RSO for clustering (RSOC) is tested on several benchmarks and compared to some other optimization algorithms for data clustering including some wellknown and powerful algorithms such as Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and other recent such as the Hybridization of Krill Herd Algorithm and harmony search (H-KHA), hybrid Harris Hawks Optimization with differential evolution (H-HHO), and Multi-Verse Optimizer (MVO). Results are validated through a bunch of measures: homogeneity, completeness, v-measure, purity, and error rate. The computational results are encouraging, Where they demonstrate the effectiveness of RSOC over other techniques.

International audienceCurrently there is a multitude of bio-inspired approaches, from which, result complex systems qualified as biomorphic. Today, the advantage of using such approaches is well established: genetic algorithms, ant algorithms, artificial immune systems, etc. However, there is no research supporting the integration of these different approaches. In particular, hybrid approaches integrating several bio-inspired analogies at the same time are rare. In this paper, we investigate to which extent it is possible to propose a design approach which can integrate different bio-inspired paradigms. From this perspective, we show the interest of considering the multi-agent paradigm as the common denominator of bio-inspired approaches and we propose a generic agent-based model for bio-inspired systems