A Comparative Study Among Some Natural-Inspired Optimization Algorithms
Jamal Salahaldeen Majeed Alneamy, Rahma Abdulwahid Hameed Alnaish · 2022
Biologically inspired algorithms such as Ant colony optimization (ACO), Firefly algorithm (FA), Artificial Bee Colony (ABC), biogeography-based optimization algorithm (BBO) and teaching learning-based optimization (TLBO) are among the most effective algorithms used for optimization. These algorithms are able to solve real-world optimization problems and discover optimal or nearly optimal solutions in a realistic time. Actually, these algorithms have different performance quality in solving various problems based on convergence speed and finding the optimal solution. So, this paper intends to make a comparative study among ABC, FA and TLBO algorithms in terms of minimum, maximum, mean and standard deviation based on some benchmark functions which are Sphere, Rosenbrock, Ackley, Schwefel and Rastrigin functions. The experimental results show that the performance of the TLBO algorithm is superior to other optimization algorithms.