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Related Experiment Video

Updated: Jun 6, 2025

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Multiple strategies improved spider wasp optimization for engineering optimization problem solving.

Jinxue Sui¹, Zifan Tian², Zuoxun Wang²

¹Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264005, China. suijx@sdtbu.edu.cn.

Scientific Reports

|November 23, 2024

Summary

This summary is machine-generated.

The Multi-strategy Improved Spider Wasp Optimizer (MISWO) enhances swarm intelligence for complex problems. This improved algorithm (MISWO) overcomes local optima and improves convergence speed for better optimization results.

Keywords:

Adaptive step size operator Dynamic lens imaging reverse learning Dynamic selection Engineering design Spider wasp optimizer

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Area of Science:

Computational intelligence
Swarm intelligence algorithms
Metaheuristic optimization

Background:

The Spider Wasp Optimization (SWO) algorithm, inspired by social animal behavior, offers rapid search and high accuracy.
However, SWO struggles with local optima, slow initial convergence, and requires manual "Trade-off Rate" (TR) tuning for complex problems.

Purpose of the Study:

To enhance the Spider Wasp Optimization (SWO) algorithm's performance and versatility.
To address limitations such as local optima, slow convergence, and parameter sensitivity.

Main Methods:

Integration of the Grey Wolf Algorithm for improved initial population fitness and global optimization.
Introduction of adaptive step size and Gaussian mutation for enhanced search accuracy and local optima avoidance.
Dynamic selection of the Trade-off Rate (TR) and implementation of dynamic lens imaging reverse learning for superior individual updates.

Main Results:

The Multi-strategy Improved Spider Wasp Optimizer (MISWO) demonstrated superior optimization capability, stability, and adaptability.
MISWO outperformed existing state-of-the-art algorithms on 23 benchmark functions and 7 engineering optimization problems.
Significant improvements in avoiding local optima and accelerating early convergence were observed.

Conclusions:

MISWO effectively addresses the limitations of the original SWO algorithm.
The proposed enhancements lead to a more robust and versatile optimization technique.
MISWO offers a promising alternative for tackling complex optimization challenges in various domains.