Local models-an approach to distributed multi-objective optimization

Computational Optimization and Applications

Volumn 42, Issue 1, 2009, Pages 105-139

  Bui, Lam T ^a   Abbass, Hussein A ^a   Essam, Daryl ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Evolutionary algorithms; Local models; Multi objective optimization; Parallelization; Particle swarm optimization

Indexed keywords

FUNCTION EVALUATION; GLOBAL OPTIMIZATION; HEALTH; MATHEMATICAL MODELS; MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION; PARALLEL ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO); SPHERES;

FITNESS EVALUATIONS; GENERAL FRAMEWORKS; LOCAL MODELS; OPTIMIZATION PROBLEMS; PARALLELIZATION; PARETO FRONTS; PARTICLE SWARM OPTIMIZATION; SEARCH SPACES; SWARM OPTIMIZATIONS;

EVOLUTIONARY ALGORITHMS;

EID: 58149488003     PISSN: 09266003     EISSN: 15732894     Source Type: Journal    
DOI: 10.1007/s10589-007-9119-8     Document Type: Article

References (39)

1. Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto frontier differential evolution approach for multiobjective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 971-978. IEEE Service Center, Piscataway (2001)
2. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic, Boston (2002)
3. Branke, J., Schmeck, H., Deb, K., Maheshwar, R.S.: Parallelizing multiobjective evolutionary algorithms: cone separation. In: Proceedings of the Congress on Evolutionary Computation, pp. 1952-1957. IEEE Press, New York (2004)
4. D. Buche N.N. Schraudolph P. Koumoutsakos 2005 Accelerating evolutionary algorithms with gaussian process fitness function models IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 35 2 183 194
5. Cantu-Paz, E.: A survey of parallel genetic algorithms. Technical Report IlliGal, TR-97003, University of Illinois, Urbana-Champaign, 1997
6. Cantu-Paz, E.: Migration policies, selection pressure and parallel evolutionary algorithms. Technical Report IlliGal TR-99015, University of Illinois, Urbana-Champaign, 1999
7. Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic, Boston (2000)
8. C.A.C. Coello 2006 Evolutionary multi-objective optimization: a historical view of the field IEEE Comput. Intell. Mag. 1 1 28 36
9. Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic, New York (2002)
10. Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)
11. K. Deb A. Pratap S. Agarwal T. Meyarivan 2002 A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Trans. Evol. Comput. 6 2 182 197
12. Deb, K., Zope, P., Jain, A.: Distributed computing of Pareto optimal solutions with evolutionary algorithms. In: Evolutionary Multi-Criterion Optimization (2003)
13. Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the Congress on Evolutionary Computation, pp. 81-86. IEEE Press, New York (2001)
14. Elmihoub, T., Hopgood, A.A., Nolle, L., Battersby, A.: Performance of hybrid genetic algorithms incorporating local search. In: Proceedings of 18th European Simulation Multiconference, pp. 154-160. Magdeburg, Germany (2004)
15. Streichert, F., Ulmer, H., Zell, A.: Parallelization of multiobjective evolutionary algorithms using clustering algorithms. In: Evolutionary Multi-Criterion Optimization, pp. 92-107 (2005)
16. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic, Boston (2002)
17. Hart, W.E.: Adaptive global optimization with local search. Ph.D. thesis, University of California, San Diego, CA, USA (1994)
18. Hiroyasu, T., Miki, M., Wantanabe, S.: The new model of parallel genetic algorithm in multiobjective optimization problems- divided range multi-objective genetic algorithm. In: Proceedings of the Congress on Evolutionary Computation, pp. 333-340. IEEE Press, New York (2000)
19. H. Ishibuchi T. Yoshida T. Murata 2003 Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling IEEE Trans. Evol. Comput. 7 2 204 223
20. Jaimes, A.L., Coello, C.A.C.: MRMOGA: parallel evolutionary multiobjective optimization using multiple resolutions. In: Corne, D. (ed.) Proceedings of the Congress on Evolutionary Computation, pp. 2294-2301. IEEE Press, New York (2005)
21. A. Jaszkiewicz 2002 Genetic local search for multi-objective combinatorial optimization Eur. J. Oper. Res. 137 1 50 71
22. Y. Jin J. Branke 2005 Evolutionary optimization in uncertain environments-a survey IEEE Trans. Evol. Comput. 9 3 303 317
23. KanGal. Kangal laboratory website. http://www.iitk.ac.in/kangal/codes. shtml.
24. Tasoulis, D.K., Parsopoulos, K.E., Vrahatis, M.N.: Multiobjective optimization using parallel vector evaluated particle swarm optimization. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, vol. 2, pp. 823-828. ACTA Press, Innsbruck (2004)
25. Knowles, J.D., Corne, D.: M-PAES: A memetic algorithm for multi-objective optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 325-332. IEEE Press, New York (2000)
26. Lobo, E.G., Goldberg, D.E.: Decision making in a hybrid genetic algorithm. Technical report, Department of General Engineering, University of Illinois at Urbana-Champaign, 1996
27. K.E. Parsopoulos M.N. Vrahatis 2002 Recent approaches to global optimization problems through particle swarm optimization Nat. Comput. 1 2-3 235 306
28. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms, Hillsdale, New Jersey, 1985, pp. 93-100
29. Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Master's thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (1995)
30. K.C. Tan Y.J. Yang C.K. Goh 2006 A distributed cooperative coevolutionary algorithm for multi-objective optimization IEEE Trans. Evol. Comput. 10 5 527 549
31. Tan, K.C., Yang, Y.J., Lee, T.H.: Designing a distributed cooperative coevolutionary algorithm for multiobjective optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 2513-2520. IEEE Press, New York (2003)
32. K.C. Tan T.H. Lee E.F. Khor 2001 Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization IEEE Trans. Evol. Comput. 5 6 565 588
33. Van Veldhuizen, D.A.: Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Ph.D. thesis, Air Force Institute of Technology, Dayton OH, USA (1999)
34. Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary computation and convergence to a Pareto front. In: Koza, J.R., et al. (eds.) Genetic Programming Conference 1998, pp. 221-228 (1998)
35. D.A. Van Veldhuizen J.B. Zydallis G.B. Lamont 2003 Considerations in engineering parallel multiobjective evolutionary algorithms IEEE Trans. Evol. Comput. 7 2 144 173
36. Zhu, Z.-Y., Leung, K.-S.: Asynchronous self-adjustable island genetic algorithm for multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, pp. 837-842. IEEE Press, New York (2002)
37. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Technical report, Swiss Federal Institute of Technology (2001)
38. Zitzler, E., Thiele, L.: Multi-objective optimization using evolutionary algorithms-a comparative case study. In: Parallel Problem Solving from Nature, pp. 292-301. Springer, Berllin (1998)
39. E. Zitzler L. Thiele K. Deb 2000 Comparison of multiobjective evolutionary algorithms: empirical results Evol. Comput. 8 1 173 195