On memetic Differential Evolution frameworks: A study of advantages and limitations in hybridization

This paper aims to study the benefits and limitations in the hybridization of the differential evolution with local search algorithms. In order to perform this study, the performance of three memetic algorithms employing a differential evolution as an evolutionary framework and several local search algorithms adaptively coordinated by means of a fitness diversity logic have been analyzed. The performance of a standard differential evolution whose parameter setting has been executed only after fine tuning has also been taken into account in the comparison. The comparative analysis has been performed on a set of various test functions. Numerical results show that the memetic algorithms without any extensive parameter tuning are still competitive with the finely tuned plain differential evolution.

[1]  Raino A. E. Mäkinen,et al.  An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV , 2007, Applied Intelligence.

[2]  D. Neumann,et al.  Hybrid differential evolution method for the mixed H2/H∞robust control problem under pole assignment , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[3]  Raino A. E. Mäkinen,et al.  Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[4]  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).

[5]  Ji-Pyng Chiou,et al.  Ant direction hybrid differential evolution for solving large capacitor placement problems , 2004, IEEE Transactions on Power Systems.

[6]  C. Su,et al.  Network Reconfiguration of Distribution Systems Using Improved Mixed-Integer Hybrid Differential Evolution , 2002, IEEE Power Engineering Review.

[7]  Tim Hendtlass,et al.  A Combined Swarm Differential Evolution Algorithm for Optimization Problems , 2001, IEA/AIE.

[8]  Ivan Zelinka,et al.  ON STAGNATION OF THE DIFFERENTIAL EVOLUTION ALGORITHM , 2000 .

[9]  M. El-Sharkawi,et al.  Introduction to Evolutionary Computation , 2008 .

[10]  Ville Tirronen,et al.  Fitness diversity based adaptation in Multimeme Algorithms:A comparative study , 2007, 2007 IEEE Congress on Evolutionary Computation.

[11]  Feng-Sheng Wang,et al.  A hybrid method of evolutionary algorithms for mixed-integer nonlinear optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[12]  Feng-Sheng Wang,et al.  Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process , 1999 .

[13]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[14]  Giuseppe Acciani,et al.  Prudent-Daring vs Tolerant Survivor Selection Schemes in Control Design of Electric Drives , 2006, EvoWorkshops.

[15]  Ravicharan Mydur.  Application of evolutionary algorithms and neural networks to electromagnetic inverse problems , 2000 .

[16]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[17]  H. Szu.  Fast simulated annealing , 1987 .

[18]  Ji-Pyng Chiou,et al.  A hybrid method of differential evolution with application to optimal control problems of a bioprocess system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

[21]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[22]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[23]  M. M. Ali,et al.  Differential evolution algorithms using hybrid mutation , 2007, Comput. Optim. Appl..

[24]  A. Singh.  Exponential Distribution: Theory, Methods and Applications , 1996 .

[25]  David B. Fogel,et al.  An Introduction to Evolutionary Computation , 2022 .

[26]  Feng-Sheng Wang,et al.  Parameter estimation of a bioreaction model by hybrid differential evolution , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[27]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[28]  Josef Tvrdík,et al.  Differential evolution with competitive setting of control parameters , 2007 .

[29]  M. Montaz Ali,et al.  Population set-based global optimization algorithms: some modifications and numerical studies , 2004, Comput. Oper. Res..

[30]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[31]  L. G. van Willigenburg,et al.  Parameter control strategy in differential evolution algorithm for optimal control , 2001 .

[32]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[33]  T. Rogalsky,et al.  HYBRIDIZATION OF DIFFERENTIAL EVOLUTION FOR AERODYNAMIC DESIGN , 2000 .

[34]  Mark Sumner,et al.  A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[35]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[36]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[37]  Ville Tirronen,et al.  A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production , 2009, EvoWorkshops.

[38]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[39]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[40]  Ferrante Neri,et al.  An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[41]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[42]  Kao-Shing Hwang,et al.  CO-EVOLUTIONARY HYBRID DIFFERENTIAL EVOLUTION FOR MIXED-INTEGER OPTIMIZATION PROBLEMS , 2001 .

[43]  Karl-Dirk Kammeyer,et al.  Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation , 2006, 2006 IEEE International Conference on Evolutionary Computation.