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Related Concept Videos

The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
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Related Experiment Videos

Fog Task Scheduling Using Quality-Source-Driven Multi-Anchor Synchronized Search Algorithm.

Haitao Xie1,2, Zhuo Luo1, Zhiwei Ye1,2

  • 1School of Computer Science and Artificial Intelligence, Hubei University of Technology, Wuhan 430068, China.

Biomimetics (Basel, Switzerland)
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces ASQS, a novel algorithm for optimizing task scheduling in Internet of Things (IoT)-Fog environments. ASQS enhances efficiency by mimicking natural collective search behaviors for better resource utilization and performance.

Keywords:
IoT–Fog task schedulinglarge-scale schedulingmetaheuristic algorithmmulti-anchor synchronizationquality-source anchors

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Efficient task scheduling in heterogeneous IoT-Fog environments presents significant challenges.
  • Limited fog resources, diverse task demands, and conflicting Quality of Service (QoS) objectives complicate scheduling.
  • Existing methods struggle to balance exploration and exploitation effectively.

Purpose of the Study:

  • To propose ASQS, a Quality-Source-driven Multi-Anchor Synchronized Search algorithm for IoT-Fog task scheduling.
  • To enhance the utilization of high-quality historical search information in scheduling.
  • To improve overall scheduling performance considering multiple QoS objectives.

Main Methods:

  • ASQS is biomimetically motivated by collective search behaviors in natural systems.
  • It constructs quality layers, extracts quality-source anchors, and uses an ACO-inspired synchronization mechanism.
  • Incorporates Fourier Neural Operator (FNO)-based search and Lévy-flight perturbation for enhanced guidance and exploration.

Main Results:

  • ASQS demonstrates competitive optimization accuracy, stable convergence, and superior scheduling performance.
  • Evaluated across benchmark functions and fog scheduling scenarios, it excels in fitness, makespan, latency, load balance, and constraint handling.
  • Large-scale experiments confirm ASQS scalability under heavy workloads.

Conclusions:

  • ASQS is an effective, scalable, and biomimetically motivated optimizer for IoT-Fog task scheduling.
  • The algorithm successfully addresses the complexities of heterogeneous IoT-Fog environments.
  • ASQS offers a promising approach for improving resource management and performance in distributed systems.