A differential evolution algorithm with intersect mutation operator

This paper proposes a novel differential evolution (DE) algorithm with intersect mutation operation called intersect mutation differential evolution (IMDE) algorithm. Instead of focusing on setting proper parameters, in IMDE algorithm, all individuals are divided into the better part and the worse part according to their fitness. And then, the novel mutation and crossover operations have been developed to generate the new individuals. Finally, a set of famous benchmark functions have been used to test and evaluate the performance of the proposed IMDE. The experimental results show that the proposed algorithm is better than, or at least comparable to the self-adaptive DE (JDE), which is proven to be better than the standard DE algorithm. In further study, the IMDE algorithm has also been compared with several improved Particle Swarm Optimization (PSO) algorithms, Artificial Bee Colony (ABC) algorithm and Bee Swarm Optimization (BSO) algorithm. And the IMDE algorithm outperforms these algorithms.

[1]  Liang Gao,et al.  Cellular particle swarm optimization , 2011, Inf. Sci..

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

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

[4]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[5]  Liang Gao,et al.  An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers , 2011, Inf. Sci..

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

[7]  Daniela Zaharie,et al.  Influence of crossover on the behavior of Differential Evolution Algorithms , 2009, Appl. Soft Comput..

[8]  T. Warren Liao,et al.  Two hybrid differential evolution algorithms for engineering design optimization , 2010, Appl. Soft Comput..

[9]  Godfrey C. Onwubolu,et al.  Scheduling flow shops using differential evolution algorithm , 2006, Eur. J. Oper. Res..

[10]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[11]  Liang Gao,et al.  A differential evolution algorithm with self-adapting strategy and control parameters , 2011, Comput. Oper. Res..

[12]  Dimitris K. Tasoulis,et al.  Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators , 2011, IEEE Transactions on Evolutionary Computation.

[13]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[14]  Reza Akbari,et al.  A novel bee swarm optimization algorithm for numerical function optimization , 2010 .

[15]  M. M. Ali.  Differential evolution with generalized differentials , 2011, J. Comput. Appl. Math..

[16]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

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

[18]  Donald C. Wunsch,et al.  Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization , 2007, Neural Networks.

[19]  Ivanoe De Falco,et al.  Differential Evolution as a viable tool for satellite image registration , 2008, Appl. Soft Comput..

[20]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

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

[22]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[23]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[24]  Patrick Siarry,et al.  Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO) , 2011, Comput. Oper. Res..

[25]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[26]  Masao Arakawa,et al.  Differential evolution as the global optimization technique and its application to structural optimization , 2011, Appl. Soft Comput..

[27]  Ling Wang,et al.  Parameter analysis based on stochastic model for differential evolution algorithm , 2010, Appl. Math. Comput..

[28]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

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

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