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Related Experiment Video

Updated: Aug 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network.

Jeng-Shyang Pan1,2, Li-Gang Zhang1, Shu-Chuan Chu1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces SAGD, an efficient surrogate-assisted hybrid meta-heuristic algorithm combining the gannet optimization algorithm (GOA) and differential evolution (DE). SAGD effectively tackles complex, time-consuming optimization problems by intelligently selecting candidates for fitness evaluation.

Keywords:
add-point strategydifferential evolutionary algorithmgannet optimization algorithmsurrogate model

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Machine Learning

Background:

  • Meta-heuristic algorithms excel at complex optimization but face challenges with time-intensive fitness function evaluations.
  • Surrogate-assisted meta-heuristic algorithms offer a solution for problems with high computational cost per function evaluation.

Purpose of the Study:

  • To propose an efficient surrogate-assisted hybrid meta-heuristic algorithm (SAGD) for computationally expensive optimization problems.
  • To introduce a novel add-point strategy leveraging historical surrogate model data for improved candidate selection.
  • To integrate local Radial Basis Function (RBF) surrogates and a generation-based optimal restart strategy for enhanced performance.

Main Methods:

  • Combines the Gannet Optimization Algorithm (GOA) with Differential Evolution (DE) within a surrogate-assisted framework.
  • Employs a new add-point strategy using historical surrogate model information to guide true fitness evaluations.
  • Utilizes local RBF surrogates for objective function landscape modeling and a control strategy for sample prediction and updates.
  • Incorporates a generation-based optimal restart strategy to dynamically select samples for algorithm restarts.

Main Results:

  • The proposed SAGD algorithm demonstrated strong performance on seven benchmark functions.
  • SAGD proved effective in solving the wireless sensor network (WSN) coverage problem, a representative expensive optimization task.
  • The results indicate significant improvements in handling optimization problems with high fitness evaluation costs.

Conclusions:

  • SAGD offers an efficient and effective approach for solving computationally expensive optimization problems.
  • The novel add-point strategy and integrated components contribute to the algorithm's success in managing high-cost fitness evaluations.
  • The study validates the utility of surrogate-assisted meta-heuristics for complex real-world applications like WSN optimization.