Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 16, 2025Engineering Computations

Multi-strategy particle swarm optimization algorithm based on multi-information metric

View Full Paper
Ask AI
Bookmark
Share

Authors

SHShiwei HouXLXiangren LvMLMi Li

Discussion

Loading...

Member takes

Overview

The MIM-PSO algorithm improves optimization by balancing learning strategies and evolutionary states, suggesting effective applications in complex problems.

Key Points

  • MIM-PSO demonstrates superior optimization performance, efficiently finding global optimal solutions.
  • The algorithm utilizes a state evaluation method based on two factors for real-time population monitoring.
  • Four different learning strategies are implemented to enhance exploration and convergence during optimization.
  • Testing on CEC2017 and CEC2021 functions shows robust results in the hyperparameter optimization of neural networks.

Cite This Study

Hou et al. (2025) studied this question.

synapsesocial.com/papers/68f0d5eb105731330a2b201ahttps://doi.org/10.1108/ec-02-2025-0117
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Strength Prediction of Smart Cementitious Materials Using a Neural Network Optimized by Particle Swarm Algorithm2024 · 7 citations
  2. 2An Adaptive PSO Approach with Modified Position Equation for Optimizing Critical Node Detection in Large-Scale Networks: Application to Wireless Sensor Networks2025 · 3 citations
  3. 3An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition2021 · 55 citations