MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm

This paper presents MO-TRIBES, an adaptive multiobjective Particle Swarm Optimization (PSO) algorithm. Metaheuristics have the drawback of being very dependent on their parameter values. Then, performances are strongly related to the fitting of parameters. Usually, such tuning is a lengthy, time consuming and delicate process. The aim of this paper is to present and to evaluate MO-TRIBES, which is an adaptive algorithm, designed for multiobjective optimization, allowing to avoid the parameter fitting step. A global description of TRIBES and a comparison with other algorithms are provided. Using an adaptive algorithm means that adaptation rules must be defined. Swarm’s structure and strategies of displacement of the particles are modified during the process according to the tribes behaviors. The choice of the final solutions is made using the Pareto dominance criterion. Rules based on crowding distance have been incorporated in order to maintain diversity along the Pareto Front. Preliminary simulations are provided and compared with the best known algorithms. These results show that MO-TRIBES is a promising alternative to tackle multiobjective problems without the constraint of parameter fitting.

[1]  K Forbes,et al.  In theory and practice. , 1993, Clinical nurse specialist CNS.

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

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

[4]  Andries Petrus Engelbrecht,et al.  Using neighbourhoods with the guaranteed convergence PSO , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[5]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[6]  C. Coello,et al.  Multi-Objective Particle Swarm Optimizers : A Survey of the State-ofthe-Art , 2006 .

[7]  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).

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

[9]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[10]  K. Yasumoto,et al.  Agent Oriented Self Adaptive Genetic Algorithm , 2003 .

[11]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[12]  Godfrey C. Onwubolu.  TRIBES application to the flow shop scheduling problem , 2004 .

[13]  Kalyanmoy Deb,et al.  Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  Johann Dréo,et al.  Metaheuristics for Hard Optimization: Methods and Case Studies , 2005 .

[15]  H. Ishibuchi,et al.  MOGA: multi-objective genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[16]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[17]  Hamid Aghvami,et al.  Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice , 2006 .

[18]  David W. Corne,et al.  Properties of an adaptive archiving algorithm for storing nondominated vectors , 2003, IEEE Trans. Evol. Comput..

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

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

[21]  Russell C. Eberhart,et al.  Adaptive particle swarm optimization: detection and response to dynamic systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[22]  Yutian Liu,et al.  An adaptive PSO algorithm for reactive power optimization , 2003 .

[23]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[24]  Oliver Vornberger,et al.  An Adaptive Parallel Genetic Algorithm for VLSI-Layout Optimization , 1996, PPSN.

[25]  Hidefumi Sawai,et al.  Genetic algorithm inspired by gene duplication , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[26]  V. Deved,et al.  Proceedings of the 24th IASTED international conference on Artificial intelligence and applications , 2006 .

[27]  DebKalyanmoy.  Multi-objective genetic algorithms , 1999 .

[28]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[29]  Xiaodong Li,et al.  Adaptively choosing niching parameters in a PSO , 2006, GECCO.

[30]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[31]  Ian C. Parmee.  Evolutionary and Adaptive Computing in Engineering Design: The Integration of Adaptive Search Exploration and Optimization with Engineering Design Pro , 2000 .

[32]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[33]  Peter J. Fleming,et al.  On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers , 1996, PPSN.

[34]  Gabriella Kókai,et al.  Self-adaptive ant colony optimisation applied to function allocation in vehicle networks , 2007, GECCO '07.

[35]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[36]  Patrick M. Reed,et al.  Efficient and Reliable Evolutionary Multiobjective Optimization Using epsilon-Dominance Archiving and Adaptive Population Sizing , 2004, GECCO.

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

[38]  Maurice Clerc.  Binary Particle Swarm Optimisers: toolbox, derivations, and mathematical insights , 2005 .

[39]  Xiao-Feng Xie,et al.  Adaptive particle swarm optimization on individual level , 2002, 6th International Conference on Signal Processing, 2002..

[40]  Q. Henry Wu,et al.  MCPSO: A multi-swarm cooperative particle swarm optimizer , 2007, Appl. Math. Comput..

[41]  P. Siarry,et al.  Multiobjective Optimization: Principles and Case Studies , 2004 .

[42]  Keiichiro Yasuda,et al.  Adaptive particle swarm optimization using velocity information of swarm , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[43]  Suganthan.  [IEEE 1999. Congress on Evolutionary Computation-CEC99 - Washington, DC, USA (6-9 July 1999)] Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) - Particle swarm optimiser with neighbourhood operator , 1999 .

[44]  Bernhard Sendhoff,et al.  A critical survey of performance indices for multi-objective optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[45]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[46]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[47]  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).

[48]  Victor J. Rayward-Smith,et al.  Modern Heuristic Search Methods , 1996 .

[49]  Ian C. Parmee.  Evolutionary and adaptive computing in engineering design , 2001 .

[50]  Ling Chen,et al.  An adaptive ant colony clustering algorithm , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[51]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[52]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[53]  Lester Ingber,et al.  Adaptive simulated annealing (ASA): Lessons learned , 2000, ArXiv.

[54]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[55]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[56]  Yongling Zheng,et al.  On the convergence analysis and parameter selection in particle swarm optimization , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[57]  Yaochu Jin,et al.  A Critical Survey of Performance Indices for Multi-Objective Optimisation , 2003 .

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

[59]  Amir Nakib,et al.  Magnetic Resonance Image Segmentation Based on Two-Dimensional Exponential Entropy and a Parameter Free PSO , 2007, Artificial Evolution.

[60]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.