Given a multiplicative uncertainty model of the plant, learning operators are designed to minimize the ultimate tracking error and convergence rate, while guaranteeing robust convergence.
To ascertain operator learning effect, and to quantify the interoperator reproducibility, using the newly introduced GDx VCC (variable corneal compensator) scanning laser polarimeter.
The Baldwinian learning operator obtains the evolving information based on the learned distribution model of a current population.
ASHLO algorithm, involves random learning operator, individual learning operator, social learning operator and adaptive strategies.
The random learning operator, individual learning operator, social learning operator and adaptive strategies are involved in the ASHLO method.
Three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to generate new solutions and search for the optima by mimicking the learning behaviors of humans.
To simulate the human learning process, the random learning operator, individual learning operator, social learning operator, and adaptive strategies are involved in the proposed ASHLO method.
Section 3 gives a brief description of the ASHLO method, involving random learning operator, individual learning operator, social learning operator and adaptive strategies.
Adaptive simplified human learning optimization(ASHLO) algorithm involves the random learning operator, individual learning operator, social learning operator [25, 26, 27], and adaptive strategies [18], which are mimicking human learning process, to find the optimal solution of the optimization problem.
