Particle Swarm Optimizer with Aging Operator for Multimodal Function Optimization

References

• Clerc , M. and Kennedy , J. 2002 . The particle swarm - explosion, stability, and convergence in a multidimensional complex space . IEEE Trans. Evol. Comput. , 6 ( 1 ) : 58 – 73 .
   Web of Science ®Google Scholar
• Yaping Du , N. W. and Zhang , J. 2004 . An optimum design method based on pso algorithm for neuron controllers . in Proc. World Congr. on Intell. Control and Autom. , 3 : 2617 – 2621 .
   Google Scholar
• P. S. Andrews , An investigation into mutation operators for particle swarm optimization , in Proc. IEEE Congr. Evol. Comput ., 2006 , pp. 1044 – 1051 .
   Google Scholar
• van den Bergh , F. and Engelbrecht , A. P. 2004 . A cooperative approach to particle swarm optimization . IEEE Trans. Evol. Comput. , 8 ( 3 ) : 225 – 239 .
   Web of Science ®Google Scholar
• Liang , J. J. , Qin , A. K. , Suganthan , P. N. and Baskar , S. 2006 . Comprehensive learning particle swarm optimizer for global optimization of multimodal functions . IEEE Trans. Evol. Comput. , 10 ( 3 ) : 281 – 295 .
   Web of Science ®Google Scholar
• de Oca , M. A. M. , Stutzle , T. , Van den Enden , K. and Dorigo , M. 2011 . Incremental social learning in particle swarms . IEEE Trans. Syst. Man Cybern. Part B Cybern. , 41 ( 2 ) : 368 – 384 .
   PubMed Web of Science ®Google Scholar
• Zhan , Z.-H. , Zhang , J. , Li , Y. and Shi , Y.-H. 2011 . Orthogonal learning particle swarm optimization . IEEE Trans. Evol. Comput. , 15 ( 6 ) : 832 – 847 .
   Web of Science ®Google Scholar
• Ratnaweera , A. , Halgamuge , S. and Watson , H. 2004 . Selforganizing hierarchical particle swarm optimizer with time-varying acceleration coefficients . IEEE Trans. Evol. Comput. , 8 ( 3 ) : 240 – 255 .
   Web of Science ®Google Scholar
• Y. Shi and R. Eberhart , A modified particle swarm optimizer , in Proc. IEEE World Congr. Comput. Intell ., 1998 , pp. 69 – 73 .
   Google Scholar
• Zhan , Z.-H. , Zhang , J. , Li , Y. and Chung , H. S.-H. 2009 . Adaptive particle swarm optimization . IEEE Trans. Syst. Man Cybern. Part B Cybern. , 39 ( 6 ) : 1362 – 1381 .
   PubMed Web of Science ®Google Scholar
• J. Sun , W. Fang , V. Palade , X. Wu , and W. Xu , “ Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point ,” Appl. Math. Comput ., vol. 218 , no. 7 , pp. 3763 – 3775 , Dec . 2011
   Web of Science ®Google Scholar
• B. Jiang , N. Wang , and X. He , “ Asynchronous particle swarm optimizer with relearning strategy ,” in Proc. Annu. Conf. IEEE Ind. Electron. Soci. IEEE , 2011 , pp. 2341 – 2346 .
   Google Scholar
• J. J. Liang and P. N. Suganthan , “ Dynamic multiswarm particle swarm optimizer ,” in Proc. IEEE Swarm Intell. Symp ., 2005 , pp. 124 – 129 .
   Google Scholar
• M. Clerc . Standard particle swarm optimisation from 2006 to 2011 . [Online]. Available: www.particleswarm.info
   Google Scholar
• Y. Jiang , T. Hu , C. Huang , and X. Wu , An improved particle swarm optimization algorithm , Appl. Math. Comput ., vol. 193 1 , Oct . 2007 . pp. 231 – 239 ,
   Web of Science ®Google Scholar
• Li , X. 2010 . Niching without niching parameters: Particle swarm optimization using a ring topology . IEEE Trans. Evol. Comput. , 14 ( 1 ) : 150 – 169 .
   Web of Science ®Google Scholar
• G. S. Hornby , “ A steady-state version of the agelayered population structure ea ,” in Genetic Programming Theory and Practice VII , 2010 , pp. 87 – 102 .
   Google Scholar
• Garca-Nieto , J. and Alba , E. 2011 . Restart particle swarm optimization with velocity modulation: a scalability test . Soft Comput. , 15 : 2221 – 2232 .
   Web of Science ®Google Scholar
• C. Horoba , T. Jansen , and C. Zarges , “ Maximal age in randomized search heuristics with aging ,” in Proc. Genet. Evol. Comput. Conf ., 2009 , pp. 803 – 810 .
   Google Scholar
• T. Jansen and C. Zarges , “ Aging beyond restarts ,” in Proc. Genet. Evol. Comput. Conf ., 2010 , pp. 705 – 712 .
   Google Scholar
• Cutello , V. , Nicosia , G. and Pavone , M. 2007 . An immune algorithm with stochastic aging and kullback entropy for the chromatic number problem . J. Comb. Optim. , 14 : 9 – 33 .
   Web of Science ®Google Scholar
• Cutello , V. , Nicosia , G. , Pavone , M. and Timmis , J. 2007 . An immune algorithm for protein structure prediction on lattice models . IEEE Trans. Evol. Comput. , 11 ( 1 ) : 101 – 117 .
   Web of Science ®Google Scholar
• H.-P. Schwefel and G. Rudolph , “ Contemporary evolution strategies ,” in Proc. Eur. Conf. Artif. Life . Springer-Verlag , 1995 , pp. 893 – 907 .
   Google Scholar
• Kubota , N. and Fukuda , T. 1997 . Genetic algorithms with age structure . Soft Comput. , 1 : 155 – 161 .
   Google Scholar
• Jansen , T. and Zarges , C. 2009 . Comparing different aging operators . in Artif. Immun. Syst., ser. LNCS , 5666 : 95 – 108 .
   Google Scholar
• Kennedy , J. and Eberhart , R. 1995 . Particle swarm optimization . in Proc. IEEE Int. Conf. Neural Netw , 4 : 1942 – 1948 .
   Google Scholar
• Poli , R. , Kennedy , J. and Blackwell , T. 2007 . Particle swarm optimization an overview . Swarm. Intell. , 1 : 33 – 57 .
   Google Scholar
• X. Yang , J. Yuan , J. Yuan , and H. Mao , A modified particle swarm optimizer with dynamic adaptation , Appl. Math. Comput ., 189 2 , pp. 1205 – 1213 , Jun . 2007 .
   Web of Science ®Google Scholar
• Hsieh , S.-T. , Sun , T.-Y. , Liu , C.-C. and Tsai , S.-J. 2009 . Efficient population utilization strategy for particle swarm optimizer . IEEE Trans. Syst. Man Cybern. Part B Cybern. , 39 ( 2 ) : 444 – 456 .
   PubMed Web of Science ®Google Scholar
• Kennedy , J. and Mendes , R. 2002 . Population structure and particle swarm performance . in Proc. IEEE Congr. Evol. Comput , 2 : 1671 – 1676 .
   Google Scholar
• Mendes , R. , Kennedy , J. and Neves , J. 2004 . The fully informed particle swarm: simpler, maybe better . IEEE Trans. Evol. Comput. , 8 ( 3 ) : 204 – 210 .
   Web of Science ®Google Scholar
• R. Eberhart and J. Kennedy , “ A new optimizer using particle swarm theory ,” in Proc. 6th Int. Symp. Micro Mach. and Hum. Sci ., 1995 , pp. 39 – 43 .
   Google Scholar
• Kennedy , J. 2000 . Stereotyping: improving particle swarm performance with cluster analysis . in Proc. IEEE Congr. Evol. Comput. , 2 : 1507 – 1512 .
   Google Scholar
• Yang , S. and Li , C. 2010 . A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments . IEEE Trans. Evol. Comput. , 14 ( 6 ) : 959 – 974 .
   Web of Science ®Google Scholar
• S. Bird and X. Li , “ Adaptively choosing niching parameters in a pso ,” in Proc. Genet. Evo. Comput. Conf ., 2006 , pp. 3 – 10 .
   Google Scholar
• X. Li and X. Yao , “ Cooperatively coevolving particle swarms for large scale optimization ,” IEEE Trans. Evol. Comput ., no. 99 , 2011 , early Access .
   Google Scholar
• A. Ghosh , S. Tsutsui , and H. Tanaka , Individual aging in genetic algorithms , in Aust. and New Zealand Conf. Intell. Inf. Syst ., 1996 , pp. 276 – 279 .
   Google Scholar
• Choi , D.-H. 2002 . Cooperative mutation based evolutionary programming for continuous function optimization . Oper. Res. Lett. , 30 ( 3 ) : 195 – 201 .
   Web of Science ®Google Scholar
• V. Cutello , G. Nicosia , and M. Pavon e , “ A hybrid immune algorithm with information gain for the graph coloring problem ,” in Proc. Genet. Evo. Comput. Conf., ser. LNCS , 2003 , vol. 2723 , pp. 171 – 182 .
   Google Scholar
• G. Stracquadanio , C. Drago , V. Romano , and G. Nicosia , “ An immunological algorithm for doping profile optimization in semiconductors design ,” in Artif. Immun. Syst., ser. LNCS , 2010 , vol. 6209 , pp. 213 – 222
   Google Scholar
• G. Hornby , “ Alps: the age-layered population structure for reducing the problem of premature convergence .” in Proc. Genet. Evol. Comput. Conf ., 2006 , pp. 815 – 822 .
   Google Scholar
• van den Bergh , F. and Engelbrecht , A. 2006 . A study of particle swarm optimization particle trajectories . Inf. Sci. , 176 ( 8 ) : 937 – 971 .
   Web of Science ®Google Scholar
• de Castro , L. N. and Von Zuben , F. J. 2002 . Learning and optimization using the clonal selection principle . IEEE Trans. Evol. Comput. , 6 ( 3 ) : 239 – 251 .
   Web of Science ®Google Scholar
• A. Auger , N. Hansen , J. Perez Zerpa , R. Ros , and M. Schoenauer , “ Experimental comparisons of derivative free optimization algorithms ” in Proc. 8th Int. Symp. Exp. Algorithms, ser. LNCS , vol. 5526 . Springer , 2009 , pp. 3 – 15 .
   Google Scholar
• Suganthan , P. N. , Hansen , N. , Liang , J. J. , Deb , K. , Chen , Y. P. , Auger , A. and Tiwari , S. 2005 . Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization , Singapore : Nanyang Technological University . Tech. Rep
   Google Scholar
• Shang , Y.-W. and Qiu , Y.-H. 2006 . A note on the extended rosenbrock function . Evol. Comput. , 14 ( 1 ) : 119 – 126 .
   PubMed Web of Science ®Google Scholar
• Salomon , R. 1995 . Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions - a survey of some theoretical and practical aspects of genetic algorithms . BioSystems , 39 : 263 – 278 .
   Web of Science ®Google Scholar
• N. Higashi and H. Iba , “ Particle swarm optimization with gaussian mutation ,” in Proc. IEEE Swarm Intell. Symp ., 2003 , pp. 72 – 79 .
   Google Scholar
• R. W. Morrison and K. A. D. Jong , “ Measurement of population diversity ,” in Proc. Artif. Evolution , 2002 , pp. 31 – 41 .
   Google Scholar