Abstract
A hybrid bird mating optimizer algorithm with teaching-learning-based optimization 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.
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