deep-neural-network-guided-pso-tracking-global

name: deep-neural-network-guided-pso-tracking-global description: "Deep Neural Network-guided Particle Swarm Optimization for tracking global optima in complex dynamic environments. Uses DNN as surrogate model to predict promising search regions, accelerating PSO convergence while maintaining tracking of moving optima. Activation: PSO, particle swarm optimization, DNN-guided search, global optimization, surrogate model"

DNN-Guided Particle Swarm Optimization

Overview

A hybrid optimization framework combining Deep Neural Networks with Particle Swarm Optimization (PSO) for tracking global optimal positions in complex dynamic environments. The DNN serves as a surrogate model predicting fitness landscapes, guiding particle placement toward promising regions and significantly accelerating convergence while maintaining the ability to track moving optima.

Source Paper

• Title: Deep Neural Network-guided PSO for Tracking a Global Optimal Position in Complex Dynamic Environment
• Authors: Stephen Raharja, Toshiharu Sugawara
• arXiv: 2604.14064v1
• Published: 2026-04-15
• Categories: N/A
• PDF: https://arxiv.org/pdf/2604.14064v1

Core Concepts

Key Innovation

Traditional PSO limitations addressed:

1. Premature convergence to local optima in complex landscapes
2. Poor tracking of moving optima in dynamic environments
3. Inefficient sampling in high-dimensional spaces

DNN-guided PSO solutions:

1. Train DNN surrogate on evaluated points to predict fitness landscape
2. Use DNN predictions plus uncertainty to guide particle placement
3. Periodically re-evaluate to update surrogate model
4. Balance exploration (DNN uncertainty) and exploitation (DNN prediction)

Implementation

import numpy as np
import torch
import torch.nn as nn

class DNNSurrogate(nn.Module):
    """DNN surrogate for fitness landscape prediction."""
    
    def __init__(self, input_dim, hidden_dims=[64, 64]):
        super().__init__()
        layers = []
        prev = input_dim
        for h in hidden_dims:
            layers.extend([nn.Linear(prev, h), nn.ReLU(), nn.Dropout(0.1)])
            prev = h
        layers.append(nn.Linear(prev, 2))  # mean + variance
        self.network = nn.Sequential(*layers)
    
    def forward(self, x):
        out = self.network(x)
        return out[:, 0], out[:, 1].exp()


class DNNGuidedPSO:
    def __init__(self, n_particles, dim, bounds, w=0.7, c1=1.5, c2=1.5):
        self.n = n_particles
        self.dim = dim
        self.bounds = bounds
        self.w, self.c1, self.c2 = w, c1, c2
        self.positions = np.random.uniform(*bounds, (n_particles, dim))
        self.velocities = np.zeros_like(self.positions)
        self.pbest_pos = self.positions.copy()
        self.pbest_fit = np.full(n_particles, np.inf)
        self.gbest_pos = None
        self.gbest_fit = np.inf
        self.surrogate = DNNSurrogate(dim)
        self.history = {'x': [], 'y': []}
    
    def step(self, fitness_fn):
        fitness = np.array([fitness_fn(p) for p in self.positions])
        self.history['x'].extend(self.positions.tolist())
        self.history['y'].extend(fitness.tolist())
        
        # Update bests
        improved = fitness < self.pbest_fit
        self.pbest_fit[improved] = fitness[improved]
        self.pbest_pos[improved] = self.positions[improved]
        
        best_idx = np.argmin(self.pbest_fit)
        if self.pbest_fit[best_idx] < self.gbest_fit:
            self.gbest_fit = self.pbest_fit[best_idx]
            self.gbest_pos = self.pbest_pos[best_idx].copy()
        
        # DNN-guided exploration
        if len(self.history['x']) > 20:
            self._train_surrogate()
        
        # PSO velocity update
        r1, r2 = np.random.random((2, self.n, self.dim))
        self.velocities = (self.w * self.velocities 
                         + self.c1 * r1 * (self.pbest_pos - self.positions)
                         + self.c2 * r2 * (self.gbest_pos - self.positions))
        
        self.positions = np.clip(self.positions + self.velocities, *self.bounds)
        return self.gbest_fit

Applications

1. Robot path planning in dynamic environments
2. Hyperparameter optimization for ML models
3. Real-time control with changing objectives
4. Sensor network optimization

Activation Keywords

• DNN-guided PSO
• particle swarm optimization
• surrogate model optimization
• neural network optimization
• dynamic optimization

Tools Used

• Read - Read existing files and documentation
• Write - Create new files and documentation
• Bash - Execute commands when needed

Instructions for Agents

1. Identify user's intent and specific requirements
2. Gather necessary context from files or user input
3. Execute appropriate actions using available tools
4. Provide clear results and suggest next steps

Examples

Basic Deep Neural Network Guided Pso Tracking Global usage

User: "Help me with deep neural network guided pso tracking global"
→ Understand requirements → Execute actions → Provide results

Advanced usage

User: "I need detailed deep neural network guided pso tracking global assistance"
→ Clarify scope → Provide comprehensive solution → Follow up