Open AccessBest-Worst-Play (BWP): A metaphor-less Optimization Algorithm
Author(s) -
Ramanpreet Singh,
G. Pradeep Kumar,
Vimal Kumar Pathak,
Prem Singh,
Himanshu Chaudhary
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1455/1/012007
Subject(s) - algorithm , benchmark (surveying) , computer science , ramer–douglas–peucker algorithm , optimization algorithm , population based incremental learning , mathematical optimization , mathematics , machine learning , genetic algorithm , computation , geodesy , geography
A novel algorithm which is an ensemble of two metaphor-less algorithms is presented in this paper. The algorithm is inspired by Rao-1 and Jaya algorithms. Since the algorithm always plays around with the best and worst solutions; the algorithm is named as Best-Worst-Play (BWP) algorithm. The algorithm does not require any algorithm specific parameters, however, algorithm control parameters are required. To test the effectiveness and performance of the proposed algorithm, a number of unconstrained and constrained benchmark functions are considered. It is found that proposed algorithm has outperformed several well-established metaphor based algorithms. The proposed BWP algorithm may be used by researchers to solve the unconstrained and constrained optimization problems