Abstract
Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA’s unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.
Highlights
Problems with optimization must find the most optimal solution of the objective function by iteration
This paper proposes a whale optimization algorithm (WOA) with perceptual perturbation [31,32,33] so that the agent can perturb near the local extreme points to obtain better optimal values as much as possible
According to the logarithmic spiral model proposed by the original WOA, seven kinds of spirals are puts forward asSpiral the mathematical
Summary
Problems with optimization must find the most optimal solution of the objective function by iteration. As a new type of bionic algorithm with better global search performance, many scholars are interested in it and apply it to a variety of engineering problems It has been widely used in the optimization of neural network parameters, allocation, and scheduling. The literature [23] uses WOA to optimize the parameters of the multilayer perception model, and it uses five standard data sets to verify the validity of the modified model We compared it with GWO and PSO, and found a high convergence and classification rate of WOA, and it is possible to avoid local minimums. For this problem, this paper proposes a WOA with perceptual perturbation [31,32,33] so that the agent can perturb near the local extreme points to obtain better optimal values as much as possible. The fourth section is a simulation, and a summary in the fifth section
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