Open AccessHybrid Bacterial Foraging Sine Cosine Algorithm for Solving Global Optimization Problems
Author(s) -
Shuhairie Mohammad,
Ahmad Nor Kasruddin Nasir,
Nor Maniha Abdul Ghani,
Raja Mohd Taufika Raja Ismail,
Abdul Razak,
Mohd Falfazli Mat Jusof,
Nurul Amira Mhd Rizal
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/917/1/012081
Subject(s) - benchmark (surveying) , foraging , local optimum , trigonometric functions , sine , convergence (economics) , algorithm , mathematical optimization , discrete cosine transform , hybrid algorithm (constraint satisfaction) , mathematics , minification , optimization algorithm , global optimization , computer science , artificial intelligence , constraint logic programming , constraint programming , geometry , geodesy , economic growth , stochastic programming , economics , image (mathematics) , biology , geography , ecology
This paper proposes a new hybrid algorithm between Bacterial Foraging Algorithm (BFA) and Sine Cosine Algorithm (SCA) called Hybrid Bacterial Foraging Sine Cosine Algorithm (HBFSCA) to solve global optimization problems. The proposed HBFSCA algorithm synergizes the strength of BFA to avoid local optima with the adaptive step-size and highly randomized movement in SCA to achieve higher accuracy compared to its original counterparts. The performances of the proposed algorithm have been investigated on a set of single-objective minimization problems consist of 30 benchmark functions, which include unimodal, multimodal, hybrid, and composite functions. The results obtained from the test functions prove that the proposed algorithm outperforms its original counterparts significantly in terms of accuracy, convergence speed, and local optima avoidance.