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Updated: Jul 2, 2025

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Published on: March 19, 2020

A modified shuffled frog leaping algorithm with inertia weight.

Zhuanzhe Zhao^1,2, Mengxian Wang¹, Yongming Liu^3,4

¹School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, Anhui, China.

Scientific Reports

|February 20, 2024

Summary

This summary is machine-generated.

A modified shuffled frog leaping algorithm (MSFLA) with inertia weight improves optimization accuracy for complex engineering problems. This enhanced algorithm overcomes local optima and demonstrates superior global optimization capabilities.

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristic Computing

Background:

The shuffled frog leaping algorithm (SFLA) is a metaheuristic algorithm inspired by frog behavior.
SFLA, combining shuffled complex evolution and particle swarm optimization (PSO), often gets trapped in local optima for complex engineering problems, limiting its accuracy.
Existing SFLA variants struggle with low optimization accuracy in challenging scenarios.

Purpose of the Study:

To introduce a novel modified shuffled frog leaping algorithm (MSFLA) to address the limitations of the original SFLA.
To enhance the global search capability and optimization accuracy of SFLA for complex engineering problems.
To theoretically analyze and validate the convergence properties of the proposed MSFLA.

Main Methods:

A modified shuffled frog leaping algorithm (MSFLA) incorporating an inertia weight (α) was developed.
The inertia weight extends the search scope for the worst-performing frog (vector) in the SFLA.
Theoretical convergence analysis was performed using a novel dynamic equation and Z-transform.

Main Results:

The MSFLA demonstrated improved solution accuracy and convergence properties compared to the original SFLA and other algorithms.
Testing on 7 benchmark functions showed MSFLA's excellent global optimization ability in high-dimensional and complex problem spaces.
The modified algorithm effectively overcomes the local optimum problem inherent in the original SFLA.

Conclusions:

The proposed MSFLA effectively enhances optimization accuracy and convergence for complex engineering problems.
The inertia weight parameter significantly improves the global search capability of the shuffled frog leaping algorithm.
MSFLA presents a robust and accurate alternative for tackling high-dimensional optimization challenges.