Improved Gbest artificial bee colony algorithm for the constraints optimization problems

Living beings in nature are most intelligent creation of nature as they evolve with time and try to find optimum solution for each problem individually or collectively. Artificial bee colony algorithm is nature inspired algorithm that mimic the swarming behaviour of honey bee and successfully solved various optimization problems. Solution quality in artificial bee colony depends on the step size during position update. Randomly decided step size always has high possibility of miss out the exact solution. Its popular variant, namely Gbest-guided artificial bee colony algorithm tried to balance it and accomplished effectively for unconstrained optimization problems but, not satisfactory for the constrained optimization problems. Further, in the Gbest-guided artificial bee colony, individuals, which are going to update their positions, attract towards the current best solution in the swarm, which sometimes leads to premature convergence. To avoid such situation as well as to enhance the efficiency of Gbest-guided artificial bee colony to solve the unconstrained continuous optimization problems, an improved variant is proposed here. The improved Gbest-guided artificial bee colony proposed modifications in the position update during both the phase i.e. employed and onlooker bee phase to introduce diversification in search space additionally intensification of the identified region. The performance of new algorithm is evaluated for 21 benchmark optimization problems. Based on statistical analyses, it is shown that the new variant is a viable alternate of Gbest-guided artificial bee colony for the constraint optimization problems.

[1]  D. Williamson,et al.  The box plot: a simple visual method to interpret data. , 1989, Annals of internal medicine.

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

[3]  Harish Sharma,et al.  Lbest Gbest Artificial Bee Colony algorithm , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[4]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[5]  Sandeep Kumar,et al.  Modified Gbest Artificial Bee Colony Algorithm , 2018 .

[6]  Harish Sharma,et al.  Fibonacci Series-Inspired Local Search in Artificial Bee Colony Algorithm , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[7]  Harish Sharma,et al.  Artificial bee colony algorithm: a survey , 2013, Int. J. Adv. Intell. Paradigms.

[8]  K. V. Arya,et al.  Opposition based lévy flight artificial bee colony , 2012, Memetic Computing.

[9]  Priyanka Tiwari,et al.  Weight driven position update artificial bee colony algorithm , 2016, 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall).

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

[11]  Harish Sharma,et al.  Memetic search in artificial bee colony algorithm , 2013, Soft Computing.

[12]  Harish Sharma,et al.  Self-adaptive artificial bee colony , 2014 .

[13]  Yu Xue,et al.  Discrete gbest-guided artificial bee colony algorithm for cloud service composition , 2014, Applied Intelligence.

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

[15]  Zelda B. Zabinsky,et al.  A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems , 2005, J. Glob. Optim..

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

[17]  Martin Middendorf,et al.  Performance evaluation of artificial bee colony optimization and new selection schemes , 2011, Memetic Comput..

[18]  Sandeep Kumar,et al.  Fitness based Position Update in Artificial Bee Colony Algorithm , 2014 .

[19]  Weifeng Gao,et al.  A modified artificial bee colony algorithm , 2012, Comput. Oper. Res..

[20]  Harish Sharma,et al.  Lévy flight artificial bee colony algorithm , 2016, Int. J. Syst. Sci..

[21]  Vivek Kumar Sharma,et al.  Memetic search in Artificial Bee Colony algorithm with fitness based position update , 2014, International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014).

[22]  Ranjit Roy,et al.  Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power , 2013, Expert Syst. Appl..

[23]  Tiranee Achalakul,et al.  The best-so-far selection in Artificial Bee Colony algorithm , 2011, Appl. Soft Comput..

[24]  Harish Sharma,et al.  Grasshopper inspired artificial bee colony algorithm for numerical optimisation , 2018, J. Exp. Theor. Artif. Intell..

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

[26]  Sandeep Kumar,et al.  Improved Onlooker Bee Phase in Artificial Bee Colony Algorithm , 2014, ArXiv.

[27]  . V.AmalaRani.  CAN PROTOCOL DRIVERLESS TRAIN CONTROL SYSTEM , 2014 .

[28]  Sandeep Kumar,et al.  Artificial Bee Colony, Firefly Swarm Optimization, and Bat Algorithms , 2018, Advances in Swarm Intelligence for Optimizing Problems in Computer Science.

[29]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[30]  Harish Sharma,et al.  Beer froth artificial bee colony algorithm for job-shop scheduling problem , 2018, Appl. Soft Comput..

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