Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

Abstract

To address the problem of the sparrow search algorithm (SSA) has poor global search ability, weak local development ability, and easily falls into the local optimal solution, a multi-strategy improved evolutionary sparrow search algorithm (MSSA) is proposed. The introduction of the tent chaotic map improves the diversity of the initialization population, accelerates algorithm convergence, and improves convergence accuracy. Endow sparrow finders with a random search ability to coordinate the balance between global search and local development. To discover dangerous sparrow individuals, the mutation evolution operation is completed, and a greedy strategy is combined to improve the processing ability of the algorithm for local optimal solutions and make full use of each sparrow individual. Six benchmark functions were used to comprehensively verify the feasibility of the proposed algorithm based on four aspects: optimization ability, robustness, convergence ability, and optimization trajectory. These results indicate that the proposed algorithm is superior. Finally, through the comparison and analysis of the parameter identification and control strategies of the two servo systems in practical application, on the one hand, the advantages of the proposed algorithm in practical engineering applications are illustrated. In addition, a fuzzy PID control strategy based on MSSA is proposed. By adding step, sinusoidal, triangular wave and disturbance signals, simulation experiments show that the control strategy can significantly improve the dynamic and steady performance of the servo system.

A Review of Key Technologies for Friction Nonlinearity in an Electro-Hydraulic Servo System

“…Through parameter estimation and feedforward compensation of the model, the main effect of current variation in the mechanical system was successfully reflected. Gao et al [58] improved the sparrow search algorithm using a multi-strategy method, optimized the local search ability of the algorithm, and applied it to the parameter identification of the friction model of the servo system. Chen et al [59] analyzed the motion trajectory of the flower pollination algorithm for the first time, improved the equations of the algorithm exploration and utilization stages through the chaotic mapping method and optimal information-guided search, and proposed an enhanced global flower pollination algorithm (GFPA).…”

“…Sparrow Search Algorithm (SSA) [58] The adjustment parameters are small, convergence accuracy is high, and robustness is good.…”

A Robustness Temperature Inversion Method for Cable Straight Joints Based on Improved Sparrow Search Algorithm Optimized BPNN

“…By comparison, the mapping generated by tent chaos mapping in the interval of [0,1] had better ergodic and uniformity. In terms of processing large-scale data, the iteration speed was also more faster [37], [38]. In addition, to overcome the shortcomings of traditional tent chaotic mapping and avoid chaotic particles falling into small period and unstable period points during iteration [37], a random variable was added to the original mapping formula, and the modified Tent chaotic mapping was expressed as follows.…”

“…Tuerxun et al [32] mixed SSA and SVM to improve the accuracy of wind turbines fault diagnosis. There are more other improved models [33], [36]- [38]. Although earlier research has increased the algorithm's accuracy and speed of convergence, the global search and local development capabilities of SSA, a recently developed swarm intelligence algorithm, remain uneven and the system is still prone to falling into the local optimum.…”

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

“…Although the meta-heuristic algorithm overcomes the shortcomings of the traditional identification algorithm, there is still room for improvement in terms of the algorithm structure and optimization efficiency. The sparrow search algorithm (SSA) [32,33] is a meta-heuristic algorithm based on sparrow foraging simulation, which has the characteristics of good self-organization ability and easy implementation. Therefore, this research introduces a sparrow search algorithm for system identification in practical engineering.…”