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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing.

Mengxing Huang1,2, Qianhao Zhai3, Yinjie Chen1

  • 1School of Information and Communication Engineering, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China.

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|April 30, 2021
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Summary
This summary is machine-generated.

This study introduces a novel Multi-Objective Whale Optimization Algorithm (MOWOA) to optimize computation offloading in mobile edge computing. The improved MOWOA2 balances time and energy, outperforming existing methods for better Quality of Service (QoS).

Keywords:
computation offloadingedge computingmulti-objectivewhale optimization algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Computation offloading in edge computing is crucial for task execution on servers.
  • Network limitations necessitate efficient decision-making for local vs. server-side task execution.
  • Existing methods often focus on single objectives or suffer from high computational complexity, lacking universal applicability.

Purpose of the Study:

  • To propose a novel Multi-Objective Whale Optimization Algorithm (MOWOA) for optimal computation offloading in mobile edge computing.
  • To address the limitations of existing methods by balancing time and energy consumption.
  • To enhance solution quality and diversity in the offloading mechanism.

Main Methods:

  • Development of a Multi-Objective Whale Optimization Algorithm (MOWOA) tailored for computation offloading.
  • Introduction of crowding degrees for sorting and improving solution set quality.
  • Implementation of an improved MOWOA (MOWOA2) using the gravity reference point method for enhanced solution diversity.

Main Results:

  • MOWOA is applied for the first time to optimize computation offloading.
  • The MOWOA2 algorithm demonstrates superior performance compared to established methods like GrEA, CGbAIS, and NSGA-III.
  • MOWOA2 achieves better quality in the final solution set for computation offloading.

Conclusions:

  • The proposed MOWOA2 offers an effective and balanced approach to computation offloading in mobile edge computing.
  • This method improves Quality of Service (QoS) by optimizing task distribution.
  • MOWOA2 provides a computationally efficient and universally applicable solution for complex offloading challenges.