Métaheuristiques pour l'optimisation et auto-organisation dans les systèmes biologiques

This article proposes to link the concepts of self-organization and adaptive memory programming (AMP) in the field of optimization metaheuristics. The self-organization is described within the framework of biology and the AMP is defined. The main categories of the metaheuristics using these two concepts are described and their use of the self-organization and the AMP is highlighted.

[1]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.

[2]  A. Fraser Simulation of Genetic Systems by Automatic Digital Computers VI. Epistasis , 1960 .

[3]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[4]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.

[5]  Guy Theraulaz,et al.  Dynamic Scheduling and Division of Labor in Social Insects , 2000, Adapt. Behav..

[6]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[7]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[8]  G Theraulaz,et al.  Coordination in Distributed Building , 1995, Science.

[9]  E. Bonabeau,et al.  Fixed response thresholds and the regulation of division of labor in insect societies , 1998 .

[10]  G. Theraulaz,et al.  Response threshold reinforcements and division of labour in insect societies , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[11]  Shervin Nouyan,et al.  Agent-Based Approach to Dynamic Task Allocation , 2002, Ant Algorithms.

[12]  M. Resende,et al.  GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES (GRASP) , 1999 .

[13]  P.-P. Grasse La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs , 1959, Insectes Sociaux.

[14]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[15]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[16]  J. Dréo,et al.  Continuous interacting ant colony algorithm based on dense heterarchy , 2004, Future Gener. Comput. Syst..

[17]  Qin Ling A Method for Solving Optimization Problem in Continuous Space by Using Ant Colony Algorithm , 2002 .

[18]  Corso Elvezia Ant colonies for the traveling salesman problem , 1997 .

[19]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 : Advanced Algorithms and Operators , 2000 .

[20]  Nicolas Monmarché,et al.  Clustering and Dynamic Data Visualization with Artificial Flying Insect , 2003, GECCO.

[21]  David B. Fogel,et al.  Evolution-ary Computation 1: Basic Algorithms and Operators , 2000 .

[22]  Nicolas Monmarché,et al.  On how Pachycondyla apicalis ants suggest a new search algorithm , 2000, Future Gener. Comput. Syst..

[23]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[24]  Nicolas Monmarché,et al.  On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic , 1999 .

[25]  Luca Maria Gambardella,et al.  Adaptive memory programming: A unified view of metaheuristics , 1998, Eur. J. Oper. Res..

[26]  D. E. Goldberg,et al.  Optimization and Machine Learning , 2022 .

[27]  O. H. Lowry Academic press. , 1972, Analytical chemistry.

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[29]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[30]  P. Glansdorff,et al.  Thermodynamic theory of structure, stability and fluctuations , 1971 .

[31]  Jie Sheng,et al.  A Method for Solving Optimization Problems in Continuous Space Using Ant Colony Algorithm , 2002, Ant Algorithms.

[32]  Luca Maria Gambardella,et al.  Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem , 1995, ICML.

[33]  R. Matthews,et al.  Ants. , 1898, Science.

[34]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[35]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.

[36]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[37]  J. Dréo,et al.  Métaheuristiques pour l'optimisation difficile , 2003 .

[38]  J. Deneubourg,et al.  Trails and U-turns in the Selection of a Path by the Ant Lasius niger , 1992 .

[39]  Gnter Rudolph,et al.  Parallel Approaches to Stochastic Global Optimization , 1992 .

[40]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[41]  Panos M. Pardalos,et al.  Parallel algorithms for global optimization problems , 1996, Solving Combinatorial Optimization Problems in Parallel.

[42]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[43]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

[44]  E. Bonabeau,et al.  Quantitative study of the fixed threshold model for the regulation of division of labour in insect societies , 1996, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[45]  V. K. Jayaraman,et al.  Ant Colony Approach to Continuous Function Optimization , 2000 .

[46]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[47]  Éric D. Taillard La programmation a memoire adaptative et les algorithmes pseudo-gloutons: nouvelles perspectives pour les meta-heuristiques , 1998 .

[48]  T. Seeley The honey bee colony as a superorganism. , 1989 .

[49]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[50]  Stephen F. Smith,et al.  Wasp-like Agents for Distributed Factory Coordination , 2004, Autonomous Agents and Multi-Agent Systems.

[51]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[52]  W. Lefèvre Natural or Artificial Systems , 2001 .

[53]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[54]  Philippe Collard,et al.  From GAs to artificial immune systems: improving adaptation in time dependent optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[55]  H. Meinhardt Models of biological pattern formation , 1982 .

[56]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[57]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[58]  E. Wilson The relation between caste ratios and division of labor in the ant genus Pheidole (Hymenoptera: Formicidae) , 1984, Behavioral Ecology and Sociobiology.