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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.
Peerj. Computer Science
|July 25, 2022
Summary
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.
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.
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