Learning automata induced artificial bee colony for noisy optimization

We propose two extensions of the traditional artificial bee colony algorithm to proficiently optimize noisy fitness. The first strategy is referred to as stochastic learning automata induced adaptive sampling. It is employed with an aim to judiciously select the sample size for the periodic fitness evaluation of a trial solution, based on the fitness variance in its local neighborhood. The local neighborhood fitness variance is here used to capture the noise distribution in the local surrounding of a candidate solution of the noisy optimization problem. The second strategy is concerned with determining the effective fitness estimate of a trial solution using the distribution of its noisy fitness samples, instead of direct averaging of the samples. Computer simulations undertaken on the noisy versions of a set of 28 benchmark functions reveal that the proposed algorithm outperforms its contenders with respect to function error value in a statistically significant manner.

[1]  MengChu Zhou,et al.  A learning automata-based particle swarm optimization algorithm for noisy environment , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[2]  Pratyusha Rakshit,et al.  Uncertainty Management in Differential Evolution Induced Multiobjective Optimization in Presence of Measurement Noise , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Pratyusha Rakshit,et al.  Realization of an Adaptive Memetic Algorithm Using Differential Evolution and Q-Learning: A Case Study in Multirobot Path Planning , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  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..

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

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

[7]  Kumpati S. Narendra,et al.  Learning Automata - A Survey , 1974, IEEE Trans. Syst. Man Cybern..

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

[9]  Junichi Suzuki,et al.  A Confidence-Based Dominance Operator in Evolutionary Algorithms for Noisy Multiobjective Optimization Problems , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[10]  Hussein A. Abbass,et al.  Fitness inheritance for noisy evolutionary multi-objective optimization , 2005, GECCO '05.

[11]  Pratyusha Rakshit,et al.  Non-dominated Sorting Bee Colony optimization in the presence of noise , 2016, Soft Comput..

[12]  Shu Tezuka.  Linear Congruential Generators , 1995 .

[13]  Pratyusha Rakshit,et al.  Extending multi-objective differential evolution for optimization in presence of noise , 2015, Inf. Sci..

[14]  R. Lyndon While,et al.  Applying evolutionary algorithms to problems with noisy, time-consuming fitness functions , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[15]  Kalyanmoy Deb,et al.  Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization , 2015, EMO.

[16]  M. E. Muller,et al.  A Note on the Generation of Random Normal Deviates , 1958 .

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

[18]  Pratyusha Rakshit,et al.  Artificial Bee Colony induced multi-objective optimization in presence of noise , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[19]  Zhuhong Zhang,et al.  Immune Algorithm with Adaptive Sampling in Noisy Environments and Its Application to Stochastic Optimization Problems , 2007, IEEE Computational Intelligence Magazine.

[20]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[21]  Kwang Ryel Ryu,et al.  Deriving a robust policy for container stacking using a noise-tolerant genetic algorithm , 2012, RACS.

[22]  Hajime Kita,et al.  Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation , 2000, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Wolfgang Hörmann,et al.  General Principles in Random Variate Generation , 2004 .

[24]  Peter Stagge,et al.  Averaging Efficiently in the Presence of Noise , 1998, PPSN.

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

[26]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..