Analysis of selection hyper-heuristics for population-based meta-heuristics in real-valued dynamic optimization

Abstract Dynamic optimization problems provide a challenge in that optima have to be tracked as the environment changes. The complexity of a dynamic optimization problem is determined by the severity and frequency of changes, as well as the behavior of the values and trajectory of optima. While many efficient algorithms have been developed to solve these types of problems, the choice of the best algorithm is highly dependent on the type of change present in the environment. This paper analyses the ability of popular selection operators used in a hyper-heuristic framework to continuously select the most appropriate optimization method over time. Empirical studies examine the behavioral differences between various hyper-heuristic selection operators to better understand their mode of operation. The results show that these hyper-heuristic approaches can yield higher performance more consistently across difference types of environments.

[1]  Edmund K. Burke,et al.  A greedy hyper-heuristic in dynamic environments , 2009, GECCO '09.

[2]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Mark Hoogendoorn,et al.  Parameter Control in Evolutionary Algorithms: Trends and Challenges , 2015, IEEE Transactions on Evolutionary Computation.

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

[5]  Tim Hendtlass,et al.  A simple and efficient multi-component algorithm for solving dynamic function optimisation problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  Andries Petrus Engelbrecht,et al.  Towards a more complete classification system for dynamically changing environments , 2012, 2012 IEEE Congress on Evolutionary Computation.

[7]  Carlos Cruz,et al.  Optimization in dynamic environments: a survey on problems, methods and measures , 2011, Soft Comput..

[8]  Raymond Chiong,et al.  Dynamic Function Optimization: The Moving Peaks Benchmark , 2013, Metaheuristics for Dynamic Optimization.

[9]  Shengxiang Yang,et al.  Population-Based Incremental Learning with Immigrants Schemes in Changing Environments , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[10]  Tim M. Blackwell,et al.  Swarms in Dynamic Environments , 2003, GECCO.

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

[12]  Andries P. Engelbrecht,et al.  Analysing the performance of dynamic multi-objective optimisation algorithms , 2013, 2013 IEEE Congress on Evolutionary Computation.

[13]  Andries Petrus Engelbrecht,et al.  Alternative hyper-heuristic strategies for multi-method global optimization , 2010, IEEE Congress on Evolutionary Computation.

[14]  Qingfu Zhang,et al.  Distributed evolutionary algorithms and their models: A survey of the state-of-the-art , 2015, Appl. Soft Comput..

[15]  Riccardo Poli,et al.  There Is a Free Lunch for Hyper-Heuristics, Genetic Programming and Computer Scientists , 2009, EuroGP.

[16]  Andries Petrus Engelbrecht,et al.  Heuristic space diversity control for improved meta-hyper-heuristic performance , 2015, Inf. Sci..

[17]  Fei Peng,et al.  Population-Based Algorithm Portfolios for Numerical Optimization , 2010, IEEE Transactions on Evolutionary Computation.

[18]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[19]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[20]  Andries Petrus Engelbrecht,et al.  On the optimality of particle swarm parameters in dynamic environments , 2013, 2013 IEEE Congress on Evolutionary Computation.

[21]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

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

[23]  Haluk Topcuoglu,et al.  A hyper-heuristic based framework for dynamic optimization problems , 2014, Appl. Soft Comput..

[24]  Jim Smith,et al.  A Memetic Algorithm With Self-Adaptive Local Search: TSP as a case study , 2000, GECCO.

[25]  Andries Petrus Engelbrecht,et al.  Analysis of hyper-heuristic performance in different dynamic environments , 2014, 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE).

[26]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[27]  T. Blackwell,et al.  Particle swarms and population diversity , 2005, Soft Comput..

[28]  Peter J. Bentley,et al.  Dynamic Search With Charged Swarms , 2002, GECCO.

[29]  Andries Petrus Engelbrecht,et al.  Evaluating landscape characteristics of dynamic benchmark functions , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[30]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

[31]  Andries Petrus Engelbrecht,et al.  A self-adaptive heterogeneous pso for real-parameter optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[32]  Haifeng Li,et al.  Ensemble of differential evolution variants , 2018, Inf. Sci..

[33]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[34]  William A. Barrett,et al.  Spiders: a new user interface for rotation and visualization of n-dimensional point sets , 1994, Proceedings Visualization '94.

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

[36]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[37]  Guohua Wu,et al.  Differential evolution with multi-population based ensemble of mutation strategies , 2016, Inf. Sci..

[38]  Tad Hogg,et al.  An Economics Approach to Hard Computational Problems , 1997, Science.

[39]  Jürgen Branke,et al.  Multiswarms, exclusion, and anti-convergence in dynamic environments , 2006, IEEE Transactions on Evolutionary Computation.

[40]  Andries Petrus Engelbrecht,et al.  Issues with performance measures for dynamic multi-objective optimisation , 2013, 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE).

[41]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[42]  Yu Wang,et al.  Self-adaptive learning based particle swarm optimization , 2011, Inf. Sci..

[43]  Andries Petrus Engelbrecht,et al.  A Self-adaptive Heterogeneous PSO Inspired by Ants , 2012, ANTS.

[44]  L. G. van Willigenburg,et al.  Efficient Differential Evolution algorithms for multimodal optimal control problems , 2003, Appl. Soft Comput..

[45]  Du Plessis,et al.  Adaptive multi-population differential evolution for dynamic environments , 2012 .

[46]  Alexander Nareyek,et al.  Choosing search heuristics by non-stationary reinforcement learning , 2004 .

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

[48]  Carlos A. Coello Coello,et al.  A comparative study of differential evolution variants for global optimization , 2006, GECCO.

[49]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[50]  Arvind S. Mohais,et al.  DynDE: a differential evolution for dynamic optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[51]  Ming Yang,et al.  An Adaptive Multipopulation Framework for Locating and Tracking Multiple Optima , 2016, IEEE Transactions on Evolutionary Computation.

[52]  Kenneth Sörensen,et al.  Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..

[53]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[54]  A. Sima Etaner-Uyar,et al.  An Ant-Based Selection Hyper-heuristic for Dynamic Environments , 2013, EvoApplications.

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

[56]  Olivier Teytaud,et al.  Continuous lunches are free! , 2007, GECCO '07.

[57]  Frédéric Saubion,et al.  Autonomous operator management for evolutionary algorithms , 2010, J. Heuristics.

[58]  Peter I. Cowling,et al.  Hyperheuristics: Recent Developments , 2008, Adaptive and Multilevel Metaheuristics.

[59]  Andries Petrus Engelbrecht,et al.  Analysis of global information sharing in hyper-heuristics for different dynamic environments , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[60]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[61]  Changhe Li,et al.  A Self-Learning Particle Swarm Optimizer for Global Optimization Problems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[62]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[63]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

[64]  A. Sima Etaner-Uyar,et al.  A hybrid multi-population framework for dynamic environments combining online and offline learning , 2013, Soft Comput..

[65]  Andries Petrus Engelbrecht,et al.  Multi-method algorithms: Investigating the entity-to-algorithm allocation problem , 2013, 2013 IEEE Congress on Evolutionary Computation.

[66]  Peter J. Angeline,et al.  Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.

[67]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[68]  Alessandro Berti,et al.  No-Free-Lunch theorems in the continuum , 2014, Theor. Comput. Sci..

[69]  A. Sima Etaner-Uyar,et al.  An Investigation of Selection Hyper-heuristics in Dynamic Environments , 2011, EvoApplications.

[70]  Janez Brest,et al.  Dynamic optimization using Self-Adaptive Differential Evolution , 2009, 2009 IEEE Congress on Evolutionary Computation.

[71]  A. E. Eiben,et al.  Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..