A multi-objective endocrine PSO algorithm and application

A novel multi-objective endocrine particle swarm optimization algorithm (MOEPSO) based on the regulation of endocrine system is proposed. In the method, the releasing hormone (RH) of endocrine system is encoded as particle swarm and supervised by the corresponding stimulating hormone (SH). For multi-objective problem, the new SH is composed by the Pareto optimal solutions which determined by the feedback of RH and SH of current generation. In each generation, RH is divided into different classes according to SH, the best positions of different classes, the best position of current generation and the best positions that the particles have achieved so far are simultaneously used to generate the new RH. The effectiveness of the method is tested by simulation experiments with some unconstrained and constrained benchmark multi-objective Pareto optimal problems. The results indicate that the designed method is efficient for some multi-objective optimization problems.

[1]  Prospero C. Naval,et al.  An effective use of crowding distance in multiobjective particle swarm optimization , 2005, GECCO '05.

[2]  Jiao Li-cheng,et al.  Intelligent particle swarm optimization in multiobjective optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[3]  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).

[4]  Derek A. Linkens,et al.  Adaptive Weighted Particle Swarm Optimisation for Multi-objective Optimal Design of Alloy Steels , 2004, PPSN.

[5]  D M Keenan,et al.  A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[7]  Joanne H. Walker,et al.  A performance sensitive hormone-inspired system for task distribution amongst evolving robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Orlando Avila-García,et al.  Using Hormonal Feedback to Modulate Action Selection in a Competitive Scenario , 2004 .

[9]  Sanghamitra Bandyopadhyay,et al.  Adaptive mufti-objective particle swarm optimization algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[10]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

[11]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[12]  Patrick McCluskey,et al.  An Interactive Multistage ε-Inequality Constraint Method For Multiple Objectives Decision Making , 1998 .

[13]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[14]  Yi Yang,et al.  Hybrid particle swarm optimization for multiobjective resource allocation , 2008 .

[15]  Jürgen Teich,et al.  Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[16]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[17]  Carlos M. Fonseca,et al.  'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  Corina Rotar.  An Evolutionary Technique for Multicriterial Optimization Based on Endocrine Paradigm , 2004, GECCO.

[19]  Leon S Farhy.  Modeling of oscillations in endocrine networks with feedback. , 2004, Methods in enzymology.

[20]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[21]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[22]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[23]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[24]  Jonathan E. Fieldsend,et al.  A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts , 2005, EMO.

[25]  Dipti Srinivasan,et al.  Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems , 2003, Evolutionary Multiobjective Optimization.

[26]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[27]  Tapabrata Ray,et al.  A Swarm Metaphor for Multiobjective Design Optimization , 2002 .

[28]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[29]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[30]  Enrico Miglierina,et al.  Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization , 2008, Eur. J. Oper. Res..

[31]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[32]  Mohammad Teshnehlab,et al.  MODIFIED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR ELECTROMAGNETIC ABSORBER DESIGN , 2008 .

[33]  Marco Laumanns,et al.  On the convergence and diversity-preservation properties of multi-objective evolutionary algorithms , 2001 .

[34]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[35]  Jonathan E. Rowe,et al.  Particle swarm optimization and fitness sharing to solve multi-objective optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[36]  Beom-Seon Jang,et al.  Managing approximation models in multiobjective optimization , 2003 .

[37]  Peter McCourt,et al.  Hormone evolution: The key to signalling , 2003, Nature.

[38]  M. A. Abido,et al.  Multiobjective particle swarm optimization for environmental/economic dispatch problem , 2009 .

[39]  Kay Chen Tan,et al.  On solving multiobjective bin packing problems using evolutionary particle swarm optimization , 2008, Eur. J. Oper. Res..

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

[41]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[42]  Jonathan E. Fieldsend,et al.  A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and , 2002 .

[43]  Y. Rahmat-Samii,et al.  Parallel particle swarm optimization and finite- difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs , 2005, IEEE Transactions on Antennas and Propagation.

[44]  J. Teich,et al.  The role of /spl epsi/-dominance in multi objective particle swarm optimization methods , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[45]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[46]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[47]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[48]  Malcolm I. Heywood,et al.  One-class learning with multi-objective genetic programming , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[49]  Carlos A. Coello Coello,et al.  Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer , 2004, GECCO.

[50]  M. Teshnehlab,et al.  Modified Multi-objective Particle Swarm Optimization for electromagnetic absorber design , 2007, 2007 Asia-Pacific Conference on Applied Electromagnetics.

[51]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[52]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[53]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.