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An adaptive dimension differential evolution algorithm based on ranking scheme for global optimization.

Tien-Wen Sung1, Baohua Zhao1, Xin Zhang1

  • 1Fujian Provincial Key Laboratory of Big Data Mining and Applications, College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China.

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Summary
This summary is machine-generated.

This study introduces an adaptive dimension differential evolution (ADDE) algorithm to improve optimization performance. The enhanced DE algorithm balances search and development, outperforming other methods in tests.

Keywords:
Adaptive dimensionDifferential evolutionGlobal optimizationRanking schemeSwarm intelligence

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Evolutionary algorithms, particularly swarm intelligence, are crucial for big data processing in modern IoT systems.
  • The Differential Evolution (DE) algorithm is a powerful optimization tool but suffers from premature convergence to local optima.
  • Addressing DE's limitations is essential for efficient and accurate problem-solving.

Purpose of the Study:

  • To propose an improved Differential Evolution algorithm, termed Adaptive Dimension Differential Evolution (ADDE).
  • To enhance the balance between exploration and exploitation in optimization processes.
  • To improve the efficiency and accuracy of search strategies in evolutionary computation.

Main Methods:

  • Developed an Adaptive Dimension Differential Evolution (ADDE) algorithm with adaptive dimension updating.
  • Incorporated an elitism-based strategy to refine the location update mechanism.
  • Validated ADDE performance against established algorithms using the CEC2014 benchmark test suite.

Main Results:

  • The proposed ADDE algorithm demonstrated superior performance compared to other tested optimization algorithms.
  • ADDE effectively balances the search and development phases, mitigating premature convergence.
  • Experimental results confirm the enhanced efficiency and accuracy of the ADDE algorithm.

Conclusions:

  • The Adaptive Dimension Differential Evolution (ADDE) algorithm offers a competitive advancement in optimization techniques.
  • ADDE provides a robust solution for complex optimization problems, particularly in big data scenarios.
  • This research contributes a novel approach to enhancing swarm intelligence-based evolutionary algorithms.