Abstract
Bird Mating Optimizer (BMO) is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO), which is established by combining the advantages of Teaching-learning-based optimization (TLBO) and Bird Mating Optimizer (BMO). The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC), Particle Swarm Optimization (PSO), Fast Evolution Programming (FEP), Differential Evolution (DE), Group Search Optimization (GSO). Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.
Highlights
As an open and demanding problem, global numerical optimization is of a great importance in various realword areas
Modern heuristic techniques with simple and powerful search capabilities aroused the attention of scholars, such as ant colony optimization [1], differential evolution [2], particle swarm optimization [3], simulated annealing [4], artificial bee colony [5] and group search optimizer [6] etc
For f5, there is no significant difference between TLBMO and Teachinglearning-based optimization (TLBO), but it is superior to Bird Mating Optimizer (BMO)
Summary
As an open and demanding problem, global numerical optimization is of a great importance in various realword areas. Since a great number of science, engineering and geography problems could be formulated as optimization problems, the efficient and optimization algorithms are always needed to tackle increasingly complex actual problems. Speaking, these algorithms can mainly be classified into two categories: traditional techniques and modern heuristic methods. Using distinct patterns to move though the search space is the main difference between BMO and other intelligence algorithms This feature helps to avoid premature convergence and maintain population diversity. BMO algorithm shows promising performance in solving optimization problems. It is not efficient in identifying the high performance regions of a solution space.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
