Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization

This chapter presents a review of modern parallel platforms and the way in which they can be exploited to implement parallel multi-objective evolutionary algorithms. Regarding parallel platforms, a special emphasis is given to global metacomputing which is an emerging form of parallel computing with promising applications in evolutionary (both multi- and singleobjective) optimization. In addition, we present the well-known models to parallelize evolutionary algorithms (i.e., master-slave, island, diffusion and hybrid models) describing some possible strategies to incorporate these models in the context of multi-objective optimization. Since an important concern in parallel computing is performance assessment, the chapter also presents how to apply parallel performance measures in multi-objective evolutionary algorithms taking into consideration their stochastic nature. Finally, we present a selection of current parallel multi-objective evolutionary algorithms that integrate novel strategies to address multi-objective issues.

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

[2]  John L. Gustafson,et al.  Fixed Time, Tiered Memory, and Superlinear Speedup , 1990, Proceedings of the Fifth Distributed Memory Computing Conference, 1990..

[3]  Selim G. Akl,et al.  Paradigms Admitting Superunitary Behaviour in Parallel Computation , 1997, Parallel Algorithms Appl..

[4]  Griffin Caprio,et al.  Parallel Metaheuristics , 2008, IEEE Distributed Systems Online.

[5]  Enrique Alba,et al.  Analyzing synchronous and asynchronous parallel distributed genetic algorithms , 2001, Future Gener. Comput. Syst..

[6]  Jasper A Vrugt,et al.  Improved evolutionary optimization from genetically adaptive multimethod search , 2007, Proceedings of the National Academy of Sciences.

[7]  Luis F. G. Sarmenta,et al.  Volunteer Computing , 1996 .

[8]  N. D. Durie,et al.  Digest of papers , 1976 .

[9]  D. K. Arvind,et al.  Detection of Concurrency-Related Errors in Joyce , 1992, CONPAR.

[10]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[11]  Andrzej Osyczka,et al.  Evolutionary Algorithms for Single and Multicriteria Design Optimization , 2001 .

[12]  Christopher C. Hsiung,et al.  Cray X-MP: the birth of a supercomputer , 1989, Computer.

[13]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[14]  Gordon Bell.  Bell's Law for the Birth and Death of Computer Classes: A theory of the Computer's Evolution , 2008, IEEE Solid-State Circuits Newsletter.

[15]  Kwong-Sak Leung,et al.  An Enhanced Annealing Genetic Algorithm For Multi-objective Optimization Problems , 2002, GECCO.

[16]  Jürgen Teich,et al.  3D exploration of software schedules for DSP algorithms , 1999, CODES '99.

[17]  W. Daniel Hillis,et al.  The connection machine , 1985 .

[18]  Ben Paechter,et al.  PSFGA : Parallel processing and evolutionary computation for multiobjective optimisation , 2004 .

[19]  V. A. Vyssotsky,et al.  Structure of the multics supervisor , 1965, AFIPS '65 (Fall, part I).

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

[21]  Gary B. Lamont,et al.  Considerations in engineering parallel multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[22]  Andreas Zell,et al.  Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms , 2005, EMO.

[23]  Frank Neumann,et al.  Do additional objectives make a problem harder? , 2007, GECCO '07.

[24]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[25]  Daisuke Sasaki,et al.  MULTIOBJECTIVE AERODYNAMIC DESIGN AND VISUALIZATION OF SUPERSONIC WINGS BY USING ADAPTIVE RANGE MULTIOBJECTIVE GENETIC ALGORITHMS , 2004 .

[26]  Enrique Alba,et al.  Parallel evolutionary algorithms can achieve super-linear performance , 2002, Inf. Process. Lett..

[27]  Tom Blank,et al.  The MasPar MP-1 architecture , 1990, Digest of Papers Compcon Spring '90. Thirty-Fifth IEEE Computer Society International Conference on Intellectual Leverage.

[28]  Hans Werner Meuer.  The TOP500 Project: Looking Back Over 15 Years of Supercomputing Experience , 2008, Informatik-Spektrum.

[29]  Wolfram Stadler,et al.  Fundamentals of Multicriteria Optimization , 1988 .

[30]  Carlos A. Coello Coello,et al.  Objective reduction using a feature selection technique , 2008, GECCO '08.

[31]  Carlos A. Coello Coello,et al.  MRMOGA: a new parallel multi‐objective evolutionary algorithm based on the use of multiple resolutions , 2007, Concurr. Comput. Pract. Exp..

[32]  Alan H. Karp,et al.  Measuring parallel processor performance , 1990, CACM.

[33]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999 .

[34]  Kalyanmoy Deb,et al.  Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions , 2003, EMO.

[35]  Andrew S. Tanenbaum,et al.  Distributed systems: Principles and Paradigms , 2001 .

[36]  Gordon Bell,et al.  Ultracomputers: a teraflop before its time , 1992, CACM.

[37]  Charles Gide,et al.  Cours d'économie politique , 1911 .

[38]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[39]  Wolfgang K. Giloi,et al.  Towards a taxonomy of computer architecture based on the machine data type view , 1983, ISCA '83.

[40]  Alexander Korobov.  Planigon tessellation cellular automata , 1999 .

[41]  Francisco Luna,et al.  Parallel Multiobjective Optimization , 2005 .

[42]  DAVID P. HELMBOLD,et al.  Modeling Speedup (n) Greater than n , 1990, IEEE Trans. Parallel Distributed Syst..

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

[44]  Leon G. Higley,et al.  Forensic Entomology: An Introduction , 2009 .

[45]  George Karypis,et al.  Introduction to Parallel Computing , 1994 .

[46]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[47]  Masoud Yazdani,et al.  Principles and case studies , 1991 .

[48]  Michael J. Flynn,et al.  Some Computer Organizations and Their Effectiveness , 1972, IEEE Transactions on Computers.

[49]  Kalyanmoy Deb,et al.  Multi-Objective Evolutionary Optimization: Past, Present, and Future , 2000 .

[50]  W. Stadler.  Multicriteria Optimization in Engineering and in the Sciences , 1988 .

[51]  Günter Rudolph,et al.  Parallel Approaches for Multiobjective Optimization , 2008, Multiobjective Optimization.

[52]  Hidefumi Sawai,et al.  Parallel distributed processing of a parameter-free GA by using hierarchical migration methods , 1999 .

[53]  Enrique Alba,et al.  Optimal Broadcasting in Metropolitan MANETs Using Multiobjective Scatter Search , 2006, EvoWorkshops.

[54]  Sven E. Eklund,et al.  A massively parallel architecture for distributed genetic algorithms , 2004, Parallel Comput..

[55]  Theodore C. Belding,et al.  The Distributed Genetic Algorithm Revisited , 1995, ICGA.

[56]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[57]  Joseph Boykin,et al.  Guest Editor's Introduction: Recen Developments in Operating Systems , 1990 .

[58]  Jürgen Teich,et al.  Evolutionary algorithm based exploration of software schedules for digital signal processors , 1999 .

[59]  Arturo Hernández-Aguirre,et al.  G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets , 2008, GECCO 2008.

[60]  Gara Miranda,et al.  Parallel hyperheuristic: a self-adaptive island-based model for multi-objective optimization , 2008, GECCO '08.

[61]  Arturo Hernández Aguirre,et al.  G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets , 2008, GECCO '08.

[62]  Lawrence A. Crowl.  How to measure, present, and compare parallel performance , 1994, IEEE Parallel & Distributed Technology: Systems & Applications.

[63]  Selim G. Akl,et al.  Paradigms Admitting Superunitary Behaviour in Parallel Computation , 1994, Parallel Algorithms Appl..

[64]  Tomoyuki Hiroyasu,et al.  The new model of parallel genetic algorithm in multi-objective optimization problems - divided range multi-objective genetic algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[65]  J. Branke,et al.  Guidance in evolutionary multi-objective optimization , 2001 .

[66]  Erich Strohmaier,et al.  High-performance computing: clusters, constellations, MPPs, and future directions , 2003, Comput. Sci. Eng..

[67]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications , 2005, Advanced Information and Knowledge Processing.

[68]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[69]  Robert O. Briggs,et al.  Cuba, communism, and computing , 1992, CACM.

[70]  Eric E. Johnson,et al.  Completing an MIMD multiprocessor taxonomy , 1988, CARN.

[71]  Samuel T. Chanson,et al.  Process groups and group communications: classifications and requirements , 1990, Computer.

[72]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.