A novel animal migration algorithm for global numerical optimization

Abstract

Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).

Performance analyses of recurrent neural network models exploited for online time-varying nonlinear optimization

“…Notice that η i (s) =η i /s as η i (t) =η i amounts to a step signal for constant vectorη. Using the final value theorem to (7), we have Therefore, it can be concluded that lim t→∞ e(t) 2 = η 2 /γ. Furthermore, the steadystate residual error lim t→∞ e(t) 2 decreases to zero as γ tends to positive infinity.…”

“…Viewed as an essential step of many solutions, the online optimization often arises in mathematics and control theory, and finds its applications in numerical analysis [1], traffic control [2] machine learning [3], robotics [4][5][6] and animal migration analysis [7]. Because of its fundamental roles, much effort has been devoted to the fast and high accuracy solution of nonlinear optimization problem, and subsequently a great deal of models have been proposed and investigated for solving it [8].…”