Improvement Of Grey Wolf Optimizer And Its Application In Operational Amplifier Design

Grey wolf optimizer(GWO)is a meta-heuristic swarm intelligence algorithm proposed in recent years,which inspired by the social behavior and life habits of grey wolf groups in nature.In a group,wolves are divided into four social classes according to their individual ability and role.The wolves in the lower classes must obey the leadership of the higher classes.The wolves in the first three classes were elected leaders of the pack and lead the hunting behavior of wolves.The mathematical model of GWO simulates the strict social dominant hierarchy and orderly predation behavior of wolves.The optimal solution is assumed to be prey.The three best individuals were assumed to be leaders of the grey wolf pack,which lead the pack to encircle prey,hunt prey and attack prey in the process of optimization.GWO has few parameters,simple structure and is easy to implemented.It has been proved that GWO has strong local search ability and high accuracy in solving optimization problems.The algorithm has received extensive attention and research from many researchers.But the research of the algorithm is still in the initial stage.GWO also has its own shortcomings similar to other swarm intelligence algorithm,such as the evolutionary information of population has not been fully used,optimization process is easy to fall into local optimum and lead to premature phenomena.The improvement of GWO and research on its application are of great significance to promote the development of GWO and the progress of computational science.This paper mainly completes three aspects of research work and the contents are as follows:It suppose that the alpha,beta and delta have better knowledge about the potential prey in GWO.The best three individuals guide the pack to approach the prey.In order to improve convergence speed and accuracy of GWO in solving complex problems,a guidance mechanism is introduced and an alpha guided grey wolf optimizer(AgGWO)is proposed in this paper.In each population movement of AgGWO,the evolution of pack is guided by the potential location information of prey contained in the Alpha's moving directiont and the Alpha's leadership is strengthened in the population.Compared with other algorithms on 16 benchmark functions,the experimental results verify the effectiveness of the proposed algorithm.If the leader falls into the local optimum for a long time and can not jump out,the population will be concentrated near the leader,and the diversity of the populationwill be reduced seriously in GWO.In order to improve population diversity and reduce the impact of guidance mechanism on population diversity,this paper proposes an improved alpha-guided grey wolf optimizer(IAgGWO).The Alpha's moving direction is used to guide the evolution of wolf leadership group which improved the alpha guidance mechanism in AgGWO.Meanwhile,a mutation operator is introduced to enhance the ability of the algorithm to jump out of local optimum.The effectiveness of the proposed strategy is verified by comparing simulation experiments.In order to simplify the parameter design of analog integrated circuits,this paper take two kinds of operational amplifiers as examples and mathematically model their optimization with aiming at maximizing the open-loop low-frequency gain.The IAgGWO,AgGWO and three other swarm intelligence algorithms are applied to the practical engineering optimization problem.The simulation results show the effectiveness of the proposed algorithm.Semi-empirical model simulation experiments in Spectre further illustrate the practical application significance of the proposed algorithm in analog integrated circuit design.