The Modified Differential Evolution Algorithm (MDEA)

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms. DE has drawn the attention of many researchers resulting in a lot of variants of the classical algorithm with improved performance. This paper presents a new modified differential evolution algorithm for minimizing continuous space. New differential evolution operators for realizing the approach are described, and its performance is compared with several variants of differential evolution algorithms. The proposed algorithm is basedon the idea of performing biased initial population. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed differential evolution algorithms. The results indicate that the proposed algorithm is able to arrive at high quality solutions in a relatively short time limit: for the largest publicly known problem instance, a new best solution could be found.

[1]  Xin Yao,et al.  Making a Difference to Differential Evolution , 2008, Advances in Metaheuristics for Hard Optimization.

[2]  Ajith Abraham,et al.  A Modified Differential Evolution Algorithm and Its Application to Engineering Problems , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[3]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.

[4]  Andries Petrus Engelbrecht,et al.  Empirical analysis of self-adaptive differential evolution , 2007, Eur. J. Oper. Res..

[5]  B. V. Babu,et al.  Modified differential evolution (MDE) for optimization of non-linear chemical processes , 2006, Comput. Chem. Eng..

[6]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

[11]  M. M. Ali,et al.  A numerical study of some modified differential evolution algorithms , 2006, Eur. J. Oper. Res..

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

[13]  Antonio LaTorre,et al.  A Memetic Differential Evolution Algorithm for Continuous Optimization , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[14]  Ajith Abraham,et al.  A simple adaptive Differential Evolution algorithm , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

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

[16]  Danilo Vasconcellos Vargas,et al.  Phylogenetic differential evolution , 2011 .

[17]  Witold Pedrycz,et al.  Foundations of Fuzzy Logic and Soft Computing, 12th International Fuzzy Systems Association World Congress, IFSA 2007, Cancun, Mexico, June 18-21, 2007, Proceedings , 2007, IFSA.

[18]  Cliff T. Ragsdale,et al.  Modified differential evolution: a greedy random strategy for genetic recombination , 2005 .

[19]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[20]  Adam P. Piotrowski,et al.  The grouping differential evolution algorithm for multi-dimensional optimization problems , 2010 .

[21]  Millie Pant,et al.  Differential Evolution with Parent Centric Crossover , 2008, 2008 Second UKSIM European Symposium on Computer Modeling and Simulation.

[22]  M. M. Ali,et al.  Differential evolution with preferential crossover , 2007, Eur. J. Oper. Res..

[23]  Zbigniew Michalewicz,et al.  Advances in Metaheuristics for Hard Optimization , 2008, Advances in Metaheuristics for Hard Optimization.

[24]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[25]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[26]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.