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A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global
Wenfen Zhang1,2, Yulin Lan1,2
1School of Computer and Artificial Intelligence, Xiangnan University, Chenzhou, China.
Computational Intelligence and Neuroscience
|April 4, 2022
Summary
This study introduces a novel memetic algorithm (MPCE & SSALS) for Large-Scale Global Optimization (LSGO) problems. The new algorithm demonstrates superior effectiveness compared to existing state-of-the-art methods.
Area of Science:
- Computer Science
- Operations Research
- Artificial Intelligence
Background:
- Large-Scale Global Optimization (LSGO) is crucial in management, computer science, and communication.
- LSGO presents significant computational challenges across diverse applications.
- Existing optimization algorithms may not fully address the complexities of LSGO.
Purpose of the Study:
- To propose a novel memetic algorithm, MPCE & SSALS, for tackling LSGO problems.
- To enhance global exploration and local exploitation in optimization.
- To evaluate the efficacy of the proposed algorithm against established methods.
Main Methods:
- Development of a memetic algorithm (MPCE & SSALS) integrating multiparent evolution and adaptive local search.
- Utilizing multiparent crossover for global search and step-size adaptive local search for exploitation.
- Implementing a dynamic population size strategy, decreasing to one individual, with alternating global and local search phases.
Main Results:
- Experimental validation on 15 benchmark functions from the CEC'2013 LSGO suite.
- Comparative analysis against four state-of-the-art optimization algorithms.
- Demonstrated superior performance of the MPCE & SSALS algorithm.
Conclusions:
- The proposed MPCE & SSALS algorithm is highly effective for Large-Scale Global Optimization.
- The combination of multiparent evolution and adaptive local search offers significant advantages.
- MPCE & SSALS represents a promising advancement in solving challenging LSGO problems.
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