A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems

In this paper, a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known, the performance of a DE algorithm depends on the mutation strategy and its control parameters, namely, crossover and mutation rates. For this reason, the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore, a new mutation operator, namely DE/Best to current/l, is proposed in the DE-QL algorithm. In this paper, 57 EDPs provided in “Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results” are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition.

[1]  P. Olivi,et al.  Preface , 2019, Journal of Solid State Electrochemistry.

[2]  J. Lampinen.  A constraint handling approach for the differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Hans-Georg Beyer,et al.  A Matrix Adaptation Evolution Strategy for Constrained Real-Parameter Optimization , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

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

[5]  Radka Polakova,et al.  L-SHADE with competing strategies applied to constrained optimization , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[6]  Lixin Tang,et al.  Differential Evolution With an Individual-Dependent Mechanism , 2015, IEEE Transactions on Evolutionary Computation.

[7]  Alex S. Fukunaga,et al.  Evaluating the performance of SHADE on CEC 2013 benchmark problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

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

[9]  Rainer Storn,et al.  Differential Evolution-A simple evolution strategy for fast optimization , 1997 .

[10]  Tetsuyuki Takahama,et al.  Efficient Constrained Optimization by the ε Constrained Rank-Based Differential Evolution , 2012, 2012 IEEE Congress on Evolutionary Computation.

[11]  Ruhul A. Sarker,et al.  Constraint Consensus Mutation-Based Differential Evolution for Constrained Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[12]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution for Optimization of Noisy Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[13]  Nimagna Biswas,et al.  An Improved Unified Differential Evolution Algorithm for Constrained Optimization Problems , .

[14]  Zhijian Wu,et al.  A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[15]  Josef Tvrdík,et al.  A simple framework for constrained problems with application of L-SHADE44 and IDE , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[16]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[17]  Ales Zamuda,et al.  Adaptive constraint handling and Success History Differential Evolution for CEC 2017 Constrained Real-Parameter Optimization , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[18]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

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

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

[21]  Dipti Srinivasan,et al.  A unified differential evolution algorithm for constrained optimization problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[22]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[23]  Jason Sheng-Hong Tsai,et al.  A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[24]  Zhijian Wu,et al.  Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems , 2011, Soft Comput..

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

[26]  Zhun Fan,et al.  LSHADE44 with an Improved $\epsilon$ Constraint-Handling Method for Solving Constrained Single-Objective Optimization Problems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

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

[28]  Witold Pedrycz,et al.  A variable reduction strategy for evolutionary algorithms handling equality constraints , 2015, Appl. Soft Comput..

[29]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[30]  Guohua Wu,et al.  A test-suite of non-convex constrained optimization problems from the real-world and some baseline results , 2020, Swarm Evol. Comput..

[31]  Ales Zamuda,et al.  Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling , 2016, Appl. Soft Comput..

[32]  Pierre Hansen,et al.  Variable neighborhood search and local branching , 2004, Comput. Oper. Res..