GDE3: the third evolution step of generalized differential evolution

A developed version of generalized differential evolution, GDE3, is proposed. GDE3 is an extension of differential evolution (DE) for global optimization with an arbitrary number of objectives and constraints. In the case of a problem with a single objective and without constraints GDE3 falls back to the original DE. GDE3 improves earlier GDE versions in the case of multi-objective problems by giving a better distributed solution. Performance of GDE3 is demonstrated with a set of test problems and the results are compared with other methods

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

[2]  B. Babu,et al.  Differential evolution for multi-objective optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[3]  J. Lampinen A constraint handling approach for the differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[4]  N. Madavan Multiobjective optimization using a Pareto differential evolution approach , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  C. S. Chang,et al.  Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system , 2000 .

[6]  Kalyanmoy Deb,et al.  Omni-optimizer: A Procedure for Single and Multi-objective Optimization , 2005, EMO.

[7]  Dimitris K. Tasoulis,et al.  Vector evaluated differential evolution for multiobjective optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Hong-Chan Chang,et al.  Application of differential evolution to passive shunt harmonic filter planning , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[9]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[11]  Carlos A. Coello Coello,et al.  Simple Feasibility Rules and Differential Evolution for Constrained Optimization , 2004, MICAI.

[12]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[13]  Feng-Sheng Wang,et al.  Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast , 2000 .

[14]  H. B. Quek,et al.  Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system , 1999 .

[15]  Kalyanmoy Deb,et al.  Constrained Test Problems for Multi-objective Evolutionary Optimization , 2001, EMO.

[16]  Stefan Janaqi,et al.  New Strategies in Differential Evolution , 2004 .

[17]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[18]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[19]  K. Multiobjective Optimization Using a Pareto Differential Evolution Approach , 2022 .

[20]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[21]  Mikkel T. Jensen,et al.  Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms , 2003, IEEE Trans. Evol. Comput..

[22]  Xiaodong Li,et al.  Solving Rotated Multi-objective Optimization Problems Using Differential Evolution , 2004, Australian Conference on Artificial Intelligence.

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  Jouni Lampinen,et al.  An Extension of Generalized Differential Evolution for Multi-objective Optimization with Constraints , 2004, PPSN.

[25]  Marco Laumanns,et al.  A unified model for multi-objective evolutionary algorithms with elitism , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[26]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[27]  Jouni Lampinen,et al.  DE’s Selection Rule for Multiobjective Optimization , 2001 .

[28]  Jonathan E. Fieldsend,et al.  Using unconstrained elite archives for multiobjective optimization , 2003, IEEE Trans. Evol. Comput..

[29]  Feng-Sheng Wang,et al.  Hybrid differential evolution with multiplier updating method for nonlinear constrained optimization problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[30]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[31]  Ji-Pyng Chiou,et al.  Differential evolution for dynamic optimization of differential-algebraic systems , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[32]  H. Abbass The self-adaptive Pareto differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[33]  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.

[34]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.