A hybridization of cuckoo search and particle swarm optimization for solving optimization problems

A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.

[1]  Xin-She Yang,et al.  Discrete cuckoo search algorithm for the travelling salesman problem , 2014, Neural Computing and Applications.

[2]  W. Tinney,et al.  Optimal Power Flow By Newton Approach , 1984, IEEE Transactions on Power Apparatus and Systems.

[3]  Ou Tang,et al.  Simulated annealing in lot sizing problems , 2004 .

[4]  M. Dorigo,et al.  The Ant Colony Optimization MetaHeuristic 1 , 1999 .

[5]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[6]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[7]  P. K. Dash,et al.  An improved cuckoo search based extreme learning machine for medical data classification , 2015, Swarm Evol. Comput..

[8]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010, Int. J. Math. Model. Numer. Optimisation.

[10]  Rui Chi,et al.  Multi-objective particle swarm-differential evolution algorithm , 2017, Neural Computing and Applications.

[11]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[12]  Carlos A. Coello Coello,et al.  Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer , 2008, Informatica.

[13]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[14]  Miaomiao Wang,et al.  Improved particle swarm optimization based approach for bilevel programming problem-an application on supply chain model , 2014, Int. J. Mach. Learn. Cybern..

[15]  Clifford T. Brown,et al.  Lévy Flights in Dobe Ju/’hoansi Foraging Patterns , 2007 .

[16]  Minghao Yin,et al.  A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem , 2013 .

[17]  Masao Fukushima,et al.  Tabu Search directed by direct search methods for nonlinear global optimization , 2006, Eur. J. Oper. Res..

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

[19]  Liang Gao,et al.  An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes , 2015, Appl. Soft Comput..

[20]  L. Wehenkel,et al.  Experiments with the interior-point method for solving large scale optimal power flow problems , 2013 .

[21]  Ali R. Yildiz,et al.  A novel hybrid immune algorithm for global optimization in design and manufacturing , 2009 .

[22]  Ilya Pavlyukevich.  Lévy flights, non-local search and simulated annealing , 2007, J. Comput. Phys..

[23]  S. G. Ponnambalam,et al.  Hybridizing Cuckoo Search with Bio-inspired Algorithms for Constrained Optimization Problems , 2015, SEMCCO.

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

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

[26]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[27]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[28]  Pudi Sekhar,et al.  An enhanced cuckoo search algorithm based contingency constrained economic load dispatch for security enhancement , 2016 .

[29]  Mazdak Shokrian,et al.  Application of a multi objective multi-leader particle swarm optimization algorithm on NLP and MINLP problems , 2014, Comput. Chem. Eng..

[30]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

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

[32]  Marcelo Seido Nagano,et al.  A high quality solution constructive heuristic for flow shop sequencing , 2002, J. Oper. Res. Soc..

[33]  Lino A. Costa,et al.  A hybrid genetic pattern search augmented Lagrangian method for constrained global optimization , 2012, Appl. Math. Comput..

[34]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[35]  Vinicius Veloso de Melo,et al.  Investigating Multi-View Differential Evolution for solving constrained engineering design problems , 2013, Expert Syst. Appl..

[36]  Andreas C. Nearchou,et al.  A novel metaheuristic approach for the flow shop scheduling problem , 2004, Eng. Appl. Artif. Intell..

[37]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[39]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[40]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[41]  Mile Savković,et al.  Improved Cuckoo Search (ICS) algorthm for constrained optimization problems , 2014 .

[42]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[43]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[44]  G. Zaslavsky,et al.  Lévy Flights and Related Topics in Physics , 2013 .

[45]  Saeed Tavakoli,et al.  Improved Cuckoo Search Algorithm for Feed forward Neural Network Training , 2011 .

[46]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[47]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[48]  Reza Tavakkoli-Moghaddam,et al.  A novel hybrid approach combining electromagnetism-like method with Solis and Wets local search for continuous optimization problems , 2009, J. Glob. Optim..

[49]  Daniele Peri,et al.  Robust optimization for ship conceptual design , 2010 .

[50]  Saeed Tavakoli,et al.  Improved cuckoo search for reliability optimization problems , 2013, Comput. Ind. Eng..

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

[52]  Springer-Verlag London,et al.  A hybridization of teaching-learning-based optimization and differential evolution for chaotic time series prediction , 2014 .

[53]  Azlan Mohd Zain,et al.  Cuckoo Search Algorithm for Optimization Problems—A Literature Review and its Applications , 2014, Appl. Artif. Intell..

[54]  Feng Zou,et al.  A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction , 2014, Neural Computing and Applications.