Adaptive Non-Uniform Particle Swarm Application to Plasmonic Design

The metaheuristic approach has become an important tool for the optimization of design in engineering. In that way, its application to the development of the plasmonic based biosensor is apparent. Plasmonics represents a rapidly expanding interdisciplinary field with numerous transducers for physical, biological and medicine applications. Specific problems are related to this domain. The plasmonic structures design depends on a large number of parameters. Second, the way of their fabrication is complex and industrial aspects are in their infancy. In this study, the authors propose a non-uniform adapted Particle Swarm Optimization (PSO) for rapid resolution of plasmonic problem. The method is tested and compared to the standard PSO, the meta-PSO (Veenhuis, 2006) and the ANUHEM (Barchiesi, 2009).These approaches are applied to the specific problem of the optimization of Surface Plasmon Resonance (SPR) Biosensors design. Results show great efficiency of the introduced method.

[1]  Christian Veenhuis.  Advanced Meta-PSO , 2006, 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06).

[2]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[3]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (2nd, extended ed.) , 1994 .

[4]  M. Clerc.  A method to improve Standard PSO , 2009 .

[5]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  Ali Derbala,et al.  A Modified Ant Colony Algorithm to the P| Prec| Cmax Scheduling Problem: A Comparative Study , 2013, Int. J. Appl. Metaheuristic Comput..

[8]  Xinchao Zhao.  Convergent analysis on evolutionary algorithm with non-uniform mutation , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[9]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[10]  J. Rolland,et al.  Metallic film optimization in a surface plasmon resonance biosensor by the extended Rouard method. , 2007, Applied optics.

[11]  Lifeng Li,et al.  Multilayer-coated diffraction gratings: differential method of Chandezon et al. revisited , 1994 .

[12]  Yang Wang,et al.  Instance-Specific Parameter Tuning for Meta-Heuristics , 2012 .

[13]  Xiao-Feng Xie,et al.  Round-Table Group Optimization for Sequencing Problems , 2012, Int. J. Appl. Metaheuristic Comput..

[14]  Pandian Vasant,et al.  Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance , 2012 .

[15]  D Barchiesi,et al.  Use of a scanning near-field optical microscope architecture to study fluorescence and energy transfer near a metal. , 1997, Optics letters.

[16]  Safa Khalouli,et al.  A hybrid meta-heuristic to solve a multi-criteria HFS problem , 2013 .

[17]  T. Grosges,et al.  A Poincaré's approach for plasmonics: the plasmon localization , 2008, Journal of microscopy.

[18]  Wolfgang Knoll,et al.  Influence of the Metal Film Thickness on the Sensitivity of Surface Plasmon Resonance Biosensors , 2005, Applied spectroscopy.

[19]  Angel A. Juan,et al.  Hybrid Algorithms for Service, Computing and Manufacturing Systems: Routing and Scheduling Solutions , 2011 .

[20]  Antonio Marcus Nogueira Lima,et al.  Optical properties and instrumental performance of thin gold films near the surface plasmon resonance , 2006 .

[21]  Dennis Weyland,et al.  A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a "Novel" Methodology , 2010, Int. J. Appl. Metaheuristic Comput..

[22]  X. D. Hoa,et al.  Towards integrated and sensitive surface plasmon resonance biosensors: a review of recent progress. , 2007, Biosensors & bioelectronics.

[23]  Dominique Barchiesi,et al.  Adaptive non-uniform, hyper-elitist evolutionary method for the optimization of plasmonic biosensors , 2009, 2009 International Conference on Computers & Industrial Engineering.

[24]  Leo Van Biesen,et al.  A Hybrid Meta-Heuristic Algorithm for Dynamic Spectrum Management in Multiuser Systems: Combining Simulated Annealing and Non-Linear Simplex Nelder-Mead , 2011, Int. J. Appl. Metaheuristic Comput..

[25]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Monica Chis,et al.  Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques , 2010 .

[27]  Ruoyu Wu,et al.  Towards HIPAA-Compliant Healthcare Systems in Cloud Computing , 2012, Int. J. Comput. Model. Algorithms Medicine.

[28]  P. Vasant,et al.  Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems , 2013 .

[29]  A. Kolomenskiǐ,et al.  Sensitivity and detection limit of concentration and adsorption measurements by laser-induced surface-plasmon resonance. , 1997, Applied optics.

[30]  Dominique Barchiesi,et al.  Apertureless scanning near-field optical microscopy: numerical modeling of the lock-in detection , 2004 .

[31]  J. Homola.  On the sensitivity of surface plasmon resonance sensors with spectral interrogation , 1997 .

[32]  Peng-Yeng Yin.  Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends , 2012 .

[33]  Laiq Khan,et al.  Generators Maintenance Scheduling Using Music-Inspired Harmony Search Algorithm , 2013 .

[34]  Xiao-Shan Gao,et al.  Evolutionary programming based on non-uniform mutation , 2007, Appl. Math. Comput..

[35]  S. N. Omkar,et al.  Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures , 2011, Int. J. Appl. Metaheuristic Comput..

[36]  Güllü Kızıltaş,et al.  A multiobjective optimization framework for nano-antennas via normal boundary intersection (NBI) method , 2009 .

[37]  Bhabani Shankar Prasad Mishra,et al.  A State-of-the-Art Review of Artificial Bee Colony in the Optimization of Single and Multiple Criteria , 2013, Int. J. Appl. Metaheuristic Comput..

[38]  Dominique Barchiesi,et al.  Apertureless scanning near-field optical microscopy: the need for probe-vibration modeling. , 2003, Optics letters.

[39]  Jalel Euchi,et al.  New Evolutionary Algorithm Based on 2-Opt Local Search to Solve the Vehicle Routing Problem with Private Fleet and Common Carrier , 2011, Int. J. Appl. Metaheuristic Comput..

[40]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[41]  Pandian Vasant.  Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications , 2013 .

[42]  Jairo R. Montoya-Torres,et al.  Global Bacteria Optimization Meta-Heuristic: Performance Analysis and Application to Shop Scheduling Problems , 2012 .

[43]  Mariacristina Gagliardi,et al.  Relevance of Mesh Dimension Optimization, Geometry Simplification and Discretization Accuracy in the Study of Mechanical Behaviour of Bare Metal Stents , 2010, Int. J. Comput. Model. Algorithms Medicine.

[44]  Félix Mora-Camino,et al.  Management of Bus Driver Duties Using Data Mining , 2011, Int. J. Appl. Metaheuristic Comput..

[45]  Peng-Yeng Yin.  Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches , 2012 .

[46]  Soon Ae Chun,et al.  A Formal Approach to Evaluating Medical Ontology Systems using Naturalness , 2010, Int. J. Comput. Model. Algorithms Medicine.

[47]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[48]  E. Kretschmann,et al.  Notizen: Radiative Decay of Non Radiative Surface Plasmons Excited by Light , 1968 .

[49]  Félix Mora-Camino,et al.  Dynamic Assignment of Crew Reserve in Airlines , 2011, Int. J. Appl. Metaheuristic Comput..

[50]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[51]  T. Grosges,et al.  Plasmonics: influence of the intermediate (or stick) layer on the efficiency of sensors , 2008 .

[52]  Mohamed Haddar,et al.  A Hybrid Genetic Algorithm for Optimization of Two-dimensional Cutting-Stock Problem , 2010, Int. J. Appl. Metaheuristic Comput..

[53]  Dominique Barchiesi,et al.  Spectroscopic study of resonant dielectric structures in near-field , 1999 .

[54]  Zong Woo Geem.  Research Commentary: Survival of the Fittest Algorithm or the Novelest Algorithm? , 2010, Int. J. Appl. Metaheuristic Comput..