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SWARM INTELLIGENCE ENGINEERING for SPATIAL ORGANIZATION MODELLING Rawan Ghnemat, Cyrille Bertelle, Gérard H.E

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SWARM INTELLIGENCE ENGINEERING for SPATIAL ORGANIZATION MODELLING Rawan Ghnemat, Cyrille Bertelle, Gérard H.E SWARM INTELLIGENCE ENGINEERING FOR SPATIAL ORGANIZATION MODELLING Rawan Ghnemat, Cyrille Bertelle, Gérard H.E. Duchamp To cite this version: Rawan Ghnemat, Cyrille Bertelle, Gérard H.E. Duchamp. SWARM INTELLIGENCE ENGINEER- ING FOR SPATIAL ORGANIZATION MODELLING. ACEA08, Jul 2008, Salt, Jordan. pp.118-125. hal-00431232 HAL Id: hal-00431232 https://hal.archives-ouvertes.fr/hal-00431232 Submitted on 11 Nov 2009 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. SWARM INTELLIGENCE ENGINEERING FOR SPATIAL ORGANIZATION MODELLING Rawan Ghnemat, Cyrille Bertelle Gerard´ H.E. Duchamp LITIS - University of Le Havre LIPN - University of Paris XIII 25 rue Philippe Lebon - BP 540 99 avenue Jean-Baptiste Clement´ 76058 Le Havre cedex, France 93430 Villetaneuse, France ABSTRACT ments are given, using repast agent platform mixed with a geographical information system. In this paper, we propose a swarm intelligence algorithm to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of 2. MODELLING SPATIAL COMPLEXITY spatial centers using multi-criteria. We combine a decen- tralized approach based on emergent clustering mixed with 2.1. Complex System Concepts spatial constraints or attractions. We propose an extension 2.1.1. From Entities to Systems of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to anal- Complex system theory [11] is based on the fact that for yse and simulate urban dynamics like gentrification or the many applicative domains, we can find similar processes dynamics of the cultural equipment in urban area. linking emergent global behavior and interaction network of constituents. The global behavior is generally not ac- Index Terms— swarm intelligence, complex systems, cessible using classical analytical methods. self-organization, ant systems, spatial organization In classical analytical methods, the global behavior of the system is the description of the equations. Simulations from these formulations, consist in obtaining the trajecto- 1. INTRODUCTION ries of the behavior predefined in the equation formulation. Many natural and artificial systems have emergent prop- In complex systems modelling, we have to model the erties based on spatial development. This spatial develop- constituents of the system and the interaction network or ment is both the result of some mecanisms from the system system which link these constiuents, using a decentralized behavior and the actor of the system formation by morpho- approach. So the global behavior of the system cannot be genetic feedback. Natural ecosystems or social organiza- understood by the description of each constituent. In com- tions in urban dynamics are typically such emergent spa- plex system modelling, the global behavior is an emergent tial organizations. The goal of this paper is to study some property from the interaction network or systems between computable mecanisms and algorithms able to model such its constituents and lead to the creation of an dynamical spatial self-organization processes, taking into account the organization of these constituents complexity of the phenomena. This dynamical and emergent organization retro-acts In section 2, complex system concepts are defined and on its own components. Two kinds of feedback allow to their applications to urban dynamics understanding are de- describe these phenomenon. The positive feedback means scribed. Section 3 presents swarm intelligence algorithms that the emergence increases the organization constitution. as methodologies to implement the complex systems con- the negative feedback means that the emergence has regu- cepts, using distributed computations. Section 4 proposes lator properties which finally stop the increasing organiza- some specific swarm intelligence methods based on ant tion constitution and allow the system stabilization. systems in order to model the spatial organizations emer- gence. Ant clustering is presented as the basis of the self- organization process. An algorithm based on the usage 2.1.2. Spatial emergence from system and environment in- of pheromon template is proposed as an extension of this teraction self-organization process, allowing to control some spa- tial constraints during the self-organization phenomenon. Another major aspect of complex systems is that they can Using multi-template, we propose a general methodology be considered as open systems. This means that they are to model multi-criteria spatial self-organization. Experi- crossed by energetical fluxes that make them evolve in a (a) Complexity of geographical space with respect of emergent organizations (b) Modelling the organization detection using swarm intelligence over dynamical environments Figure 1. Spatial organizations Complexity Description and the Conceptual Generic Model Based on Swarm Intelligence continuous way. From these energetical fluxes, complex lustration of systems where spatial emergence, self-organi- systems can evolve through critical states, using bifurca- zation and structural interaction between the system and tion schema and attractors behaviors. One of the major its components occur. In figure 1, we concentrate on the vector or support of these energetical fluxes is the environ- emergence of organizational systems from geographical ment itself where the complex systems and their entities systems. The continuous dynamic development of the or- evolve. In many natural and artificial systems, the envi- ganization feed-back on the geographical system which ronement has some spatial effects which interact on the contains the organization components and their environ- whole complexity of the phenomenon. This spatial envi- ment. The lower part of this figure explains our analy- ronment can be modified by the system but he can also be sis methodology. It consists to describe many applica- the catalyst of its own evolution. Understanding and mod- tives problems by dynamical graphs or environments in elling the deep structural effect of the interaction between order to detect organizations over these dynamical envi- the systems and its spatial environment is the goal of the ronment. For the organization detection, we use swarm study presented in the sequel. intelligence processes. We model the feed-back process of this emergent organization on the system constituents and 2.2. Application to urban dynamics its environment. To analyse or simulate urban dynamics, nowadays, we can use the great amount of geographical Social and human developments are typical complex sys- databases directly available for computational treatment tems. Urban development and dynamics are the perfect il- within Geographical Information Systems. On the organi- zational level description, the new development of multia- deposites over the environment (here a graph), the virtual gent systems (MAS) allows nowadays to develop suitable ants react in elementary way by a probabilistic choice of models and efficient simulations. path weighted with two coefficients, one comes from the problem heuristic and the other represent the pheromon The applications we focuss on in the models that we rate deposit by all the ants until now. The feed-back pro- will propose in the following concerns specifically the multi- cess of the whole system over the entities is modelled by center (or multi-organizational) phenomona inside urban the pheromon action on the ants themselves. development. As an artificial ecosystems, the city devel- opment has to deal with many challenges, specifically for Particule Swarm Optimization (PSO) is a metaheuris- sustainable development, mixing economical, social and tic method initially proposed by J. Kennedy and R. Eben- environmental aspects. The decentralized methodology hart [10].This method is initialized with a virtual parti- proposed in the following allows to deal with multi-criteria cle set which can move over the space of solutions corre- problems, leading to propose a decision making assistance, sponding to a specific optimization problem. The method based on simulation analysis. can be considered as an extension of a bird flocking model, like the BOIDS simulation from C.W. Reynolds [13]. In Gentrification phenomena can be modelled using such PSO algorithm, each virtual particle moves according to methodology. It is typically a multi-criteria self-organization its current velocity, its best previous position and the best processes where appears emergent coming of new popula- position obtained from the particles of its neighborhood. tion inside urban or territorial areas. This new population The feed-back process of the whole system over the enti- firstly attracted by some criteria, brings some other chara- ties is modelled by the storage of this two best positions as teristics which are able to modify and feedback over the the result of communications between the system entities. environment. Other swarm optimization methods have been devel- Cultural dynamics processes in urban areas are also opped like Artificial Immune Systems [6]
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