A noise resilient Differential Evolution with improved parameter and strategy control

A switched-parameter Differential Evolution (DE) enforced with equiprobable switching between two alternative mutation strategies, an optional blending crossover, and a threshold-based selection mechanism is proposed for optimization of complex functions corrupted with additive noise. In order to handle the noisy optimization problems, the DE framework is coupled with three new algorithmic components. Each individual is subjected to one of the two well known mutation strategies namely DE/best/1 and DE/rand/1 with equal chances. In the recombination stage, binomial and blending crossovers are opted in the same switchable strategy as done for mutation. A novel threshold-based selection mechanism is used to allow less fit offspring to survive occasionally, thus countering the noisy function behavior. Additive Gaussian noise is used to simulate the noisy behavior of functions defined over continuous search spaces. A benchmark suite comprising of 21 well-known numerical functions is considered to compare and contrast the proposed method with other state-of-the-art evolutionary algorithms specifically tailored for noisy optimization scenario. The proposed method shows very competitive performance indicating highly robust behavior against the noisy functional landscapes.

[1]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[2]  Amit Konar,et al.  An Improved Differential Evolution Scheme for Noisy Optimization Problems , 2005, PReMI.

[3]  Renato A. Krohling,et al.  Swarm algorithms with chaotic jumps applied to noisy optimization problems , 2011, Inf. Sci..

[4]  Pratyusha Rakshit,et al.  Noisy evolutionary optimization algorithms - A comprehensive survey , 2017, Swarm Evol. Comput..

[5]  Ponnuthurai N. Suganthan,et al.  Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[6]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[7]  Bernhard Sendhoff,et al.  Functions with noise-induced multimodality: a test for evolutionary robust Optimization-properties and performance analysis , 2006, IEEE Transactions on Evolutionary Computation.

[8]  P. N. Suganthan,et al.  Ensemble of Constraint Handling Techniques , 2010, IEEE Transactions on Evolutionary Computation.

[9]  Long Li,et al.  Differential evolution based on covariance matrix learning and bimodal distribution parameter setting , 2014, Appl. Soft Comput..

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

[11]  Gary B. Fogel,et al.  Noisy optimization problems - a particular challenge for differential evolution? , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[13]  Ferrante Neri,et al.  A memetic Differential Evolution approach in noisy optimization , 2010, Memetic Comput..

[14]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[15]  Olivier Teytaud,et al.  Differential evolution for strongly noisy optimization: Use 1.01n resamplings at iteration n and reach the − 1/2 slope , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

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

[17]  Ponnuthurai N. Suganthan,et al.  An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[19]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[20]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[21]  Amit Konar,et al.  Improved differential evolution algorithms for handling noisy optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[22]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[23]  Sankha Subhra Mullick,et al.  A Switched Parameter Differential Evolution for Large Scale Global Optimization - Simpler May Be Better , 2015, MENDEL.

[24]  Ferrante Neri,et al.  Differential Evolution with Noise Analyzer , 2009, EvoWorkshops.

[25]  Bo Liu,et al.  Hybrid differential evolution for noisy optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[26]  Giovanni Iacca,et al.  Noise analysis compact differential evolution , 2012, Int. J. Syst. Sci..