Improved Whale Optimization Algorithm And Its Application In Parameter Optimization Of Nonlinear Energy Traps

The whale optimization algorithm is a new intelligent algorithm proposed by Mirjalili in2016.Its optimization mechanism is inspired by the predation behavior of humpback whales.Due to its special local search method,the whale optimization algorithm has the characteristics of faster convergence speed and fewer setting parameters.It is gradually applied in engineering optimization,neural networks,artificial intelligence,and other fields.However,like other intelligent algorithms,the whale optimization algorithm cannot balance the global search and the local search well,and is easy to trap into local optima.This paper mainly analyzes the defects of the whale optimization algorithm.It also proposes an improved whale optimization algorithm and applies it to the parameter optimization problem of nonlinear energy sink.In the algorithm research stage: First,for the defects of the whale optimization algorithm that cannot balance the global search and local search,this paper selects the better ones from the convergence factors proposed in recent years.Second,inspired by the artificial bee colony algorithm,this paper proposes a motivation strategy that allows the algorithm to better jump out of the local optimal.Then,the search for prey is replaced by the adaptive mutation strategy with stronger optimization ability to solve the problem of low global search efficiency of the whale optimization algorithm.Finally,the optimal solution of the current iteration is searched dimension by dimension to improve the convergence speed of the algorithm.After confirming the optimal parameters in the improved algorithm,a variety of benchmark functions are used numerically simulation to analyze the improved algorithm.The results show the effectiveness and superiority of the algorithm.In the engineering application stage: This paper applies the improved algorithm to the parameter optimization problem of two types of a nonlinear energy sink,namely “negative stiffness and sliding friction” and “magnetic bi-stable”.Aiming at the parameter optimization problem of nonlinear energy sink with negative stiffness and sliding friction,the results show that optimizing under the action of impulse,the improved algorithm has faster convergence speed,higher convergence accuracy,and analysis of the sensitivity of parameters to be optimized;for optimization under the action of the earthquake,the improved algorithm can still get better solutions in complex situations,and the optimal individual obtained by improved algorithm shows better damping ability in numerical simulation.Aiming at the parameter optimization problem of the magnetic Bi-stable nonlinear energy sink,this paper optimizes 9parameters,and substitutes the obtained results into 21 different cases for numerical simulation.The results show that the optimal individual obtained by the improved algorithm can perform better in the vibration reduction performance of the device.The results also indicate that the improved algorithm can solve the multi-objective optimization problem well.Therefore,the improved algorithm has practical engineering value.