Individual Intelligence Based Optimization and Its Application to ITAE Standard Forms

By learning from the behaviors of social creatures, swarm intelligence based optimization (SIO) has become an attractive research area which leads to the emergence of numerous intelligent optimization algorithms, such as ant colony algorithm, particle swarm optimization (PSO). In this paper, another creature intelligence instead of swarm intelligence, individual intelligence, is studied by learning from the behaviors of solitary creatures, and individual intelligence based optimization (IIO) is proposed. The flowchart of IIO is given according to the foraging procedure of solitary creatures, and an IIO algorithm containing three modules is presented. The validity of the proposed IIO algorithm is verified on function test. Experimental results show that the global optimization ability of IIO is more powerful and converges more quickly than SIO. Its application to standard forms subjected to the criterion of the integral of time multiplied by the absolute value of error (ITAE) indicates the effectiveness of IIO in practice.