An Effective Constraint-Handling Improved Cuckoo Search Algorithm and Its Application in Aerodynamic Shape Optimization

This research develops an effective constraint-handling method to improve meta-heuristic algorithms’ performance when solving constrained optimization problems. With the cuckoo search (CS) as the basic optimization algorithm, a constraint-handling improved cuckoo search algorithm (CICS) is presented and applied to an airfoil aerodynamic shape optimization. First, the newly developed constraint-handling (CH) method is compared to five types of traditional techniques by incorporating the cuckoo search algorithm, particle swarm optimization (PSO), and genetic algorithm (GA), respectively, on ten benchmark analytical test problems and four engineering design optimization problems. Results indicate that the present method is effective and robust, and outperforms the other constraint-handling methods, not only for cuckoo search but also for particle swarm optimization and genetic algorithm. Next, the presented constraint-handling improved cuckoo search algorithm is compared to nine types of state-of-the-art meta-heuristic algorithms. It shows better performance when solving the aforementioned constrained problems. Finally, the CICS algorithm is successfully applied to a strongly constrained aerodynamic drag minimization design problem. It also shows its feasibility to aerodynamic design optimization and superiority to the original cuckoo search algorithm.

[1]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[2]  Dervis Karaboga,et al.  A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems , 2011, Appl. Soft Comput..

[3]  Yongquan Zhou,et al.  An improved Cuckoo Search Algorithm for Solving Planar Graph Coloring Problem , 2013 .

[4]  Thang Trung Nguyen,et al.  Economic Load Dispatch with Multiple Fuel Options and Valve Point Effect Using Cuckoo Search Algorithm with Different Distributions , 2015 .

[5]  Xiaoyong Liu,et al.  PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses , 2014, TheScientificWorldJournal.

[6]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[7]  Huiling Chen,et al.  Slime mould algorithm: A new method for stochastic optimization , 2020, Future Gener. Comput. Syst..

[8]  Yongquan Zhou,et al.  A Novel Discrete Cuckoo Search Algorithm for Spherical Traveling Salesman Problem , 2013 .

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

[10]  Dong-Ho Lee,et al.  Two-point design optimization of transonic airfoil using response surface methodology , 1999 .

[11]  Anil Kumar,et al.  A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve , 2016, Appl. Soft Comput..

[12]  Sriparna Saha,et al.  On Some Improved Versions of Whale Optimization Algorithm , 2019, Arabian Journal for Science and Engineering.

[13]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[14]  Rohit Salgotra,et al.  The naked mole-rat algorithm , 2019, Neural Computing and Applications.

[15]  Enrique Herrera-Viedma,et al.  Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion , 2014, Inf. Sci..

[16]  O. Hassan,et al.  A novel implementation of computational aerodynamic shape optimisation using Modified Cuckoo Search , 2016 .

[17]  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..

[18]  Hossam Faris,et al.  Harris hawks optimization: Algorithm and applications , 2019, Future Gener. Comput. Syst..

[19]  Zhonghua Han,et al.  Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models , 2016, Structural and Multidisciplinary Optimization.

[20]  Seyedali Mirjalili,et al.  Equilibrium optimizer: A novel optimization algorithm , 2020, Knowl. Based Syst..

[21]  Sriparna Saha,et al.  Improved Flower Pollination Algorithm for Linear Antenna Design Problems , 2019, SocProS.

[22]  Salim Chikhi,et al.  Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm , 2012, Int. J. Bio Inspired Comput..

[23]  Andrew Lewis,et al.  Autonomous Particles Groups for Particle Swarm Optimization , 2014 .

[24]  N. Jawahar,et al.  An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization , 2014 .

[25]  Kenneth Morgan,et al.  Modified cuckoo search: A new gradient free optimisation algorithm , 2011 .

[26]  Zhun Fan,et al.  Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique , 2009 .

[27]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[28]  S. Puzzi,et al.  A double-multiplicative dynamic penalty approach for constrained evolutionary optimization , 2008 .

[29]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[30]  Thomas Stützle,et al.  Ant Colony Optimization: Overview and Recent Advances , 2018, Handbook of Metaheuristics.

[31]  Thang Trung Nguyen,et al.  Cuckoo search algorithm for short-term hydrothermal scheduling , 2014 .

[32]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[33]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[34]  R. Haftka,et al.  Constrained particle swarm optimization using a bi-objective formulation , 2009 .

[35]  Mohammed Azmi Al-Betar,et al.  A survey on applications and variants of the cuckoo search algorithm , 2017, Appl. Soft Comput..

[36]  M. Tuba,et al.  Modified cuckoo search algorithm for unconstrained optimization problems , 2011 .

[37]  T. Bakhshpoori,et al.  AN EFFICIENT OPTIMIZATION PROCEDURE BASED ON CUCKOO SEARCH ALGORITHM FOR PRACTICAL DESIGN OF STEEL STRUCTURES , 2012 .

[38]  E. S. Ali,et al.  Optimal Power System Stabilizers design via Cuckoo Search algorithm , 2016 .

[39]  Malcolm Irving,et al.  A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling , 1998 .

[40]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[41]  Seyedali Mirjalili,et al.  Henry gas solubility optimization: A novel physics-based algorithm , 2019, Future Gener. Comput. Syst..

[42]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[43]  Tetsuyuki Takahama,et al.  Constrained optimization by applying the /spl alpha/ constrained method to the nonlinear simplex method with mutations , 2005, IEEE Transactions on Evolutionary Computation.

[44]  Zhong-Hua Han,et al.  Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids , 2020, Chinese Journal of Aeronautics.

[45]  Sriparna Saha,et al.  New cuckoo search algorithms with enhanced exploration and exploitation properties , 2018, Expert Syst. Appl..

[46]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[47]  Iztok Fister,et al.  Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm , 2017, Appl. Soft Comput..

[48]  Pinar Civicioglu,et al.  Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms , 2018, Neural Computing and Applications.

[49]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[50]  Zhong-Hua Han,et al.  Efficient Kriging-Based Aerodynamic Design of Transonic Airfoils: Some Key Issues , 2012 .

[51]  Yong Wang,et al.  A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization , 2006, IEEE Transactions on Evolutionary Computation.

[52]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[53]  Xin-She Yang,et al.  Multiobjective cuckoo search for design optimization , 2013, Comput. Oper. Res..

[54]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[55]  Mustafa Servet Kiran,et al.  A modification of tree-seed algorithm using Deb's rules for constrained optimization , 2018, Appl. Soft Comput..

[56]  Weerakorn Ongsakul,et al.  Cuckoo search algorithm for non-convex economic dispatch , 2013 .

[57]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[58]  Ling Wang,et al.  An effective differential evolution with level comparison for constrained engineering design , 2010 .

[59]  Sriparna Saha,et al.  Improved Cuckoo Search with Better Search Capabilities for Solving CEC2017 Benchmark Problems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

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

[61]  Shang He,et al.  An improved particle swarm optimizer for mechanical design optimization problems , 2004 .