Solving multimodal problems via multiobjective techniques with Application to phase equilibrium detection

作者: Mike Preuss , Guenter Rudolph , Feelly Tumakaka

DOI: 10.1109/CEC.2007.4424812

关键词: Evolutionary computationSimple (abstract algebra)MathematicsEvolutionary algorithmPhase detectorLocal search (optimization)Quality (business)Mathematical optimizationCoupling (computer programming)Function (mathematics)

摘要: For solving multimodal problems by means of evolutionary algorithms, one often resorts to multistarts or niching methods. The latter approach the question: 'What is elsewhere?' an implicit second criterion in order keep populations distributed over search space. Induced a practical problem that appears be simple but not easily solved, multiobjective algorithm proposed for problems. It employs explicit diversity as objective. Experimental comparison with standard methods suggests fast and reliable coupling it local technique straightforward leads enormous quality gain. combined still may especially valuable costly target function evaluations.

参考文章(22)
Franz Oppacher, Mark Wineberg, The Shifting Balance Genetic Algorithm: improving the GA in a dynamic environment genetic and evolutionary computation conference. pp. 504- 510 ,(1999)
T Bartz-Beielstein, M Preuss, Considerations of Budget Allocation for Sequential Parameter Optimization (SPO) PPSN 2006 (EMAA Workshop). ,(2006)
Felix Streichert, Gunnar Stein, Holger Ulmer, Andreas Zell, A Clustering Based Niching Method for Evolutionary Algorithms Genetic and Evolutionary Computation — GECCO 2003. pp. 644- 645 ,(2003) , 10.1007/3-540-45105-6_79
Márk Jelasity, UEGO, an abstract niching technique for global optimization Lecture Notes in Computer Science. pp. 378- 387 ,(1998) , 10.1007/BFB0056880
Agoston E. Eiben, J. E. Smith, Introduction to evolutionary computing ,(2003)
Edmundo Gomes de Azevedo, J. M. Prausnitz, Ruediger N. Lichtenthaler, Molecular Thermodynamics of Fluid-Phase Equilibria ,(1969)
Michael Emmerich, Nicola Beume, Boris Naujoks, None, An EMO algorithm using the hypervolume measure as selection criterion international conference on evolutionary multi criterion optimization. pp. 62- 76 ,(2005) , 10.1007/978-3-540-31880-4_5