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Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms
Andrea Mambrini1, Dirk Sudholt1
1Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK.
Evolutionary Computation
|June 13, 2015
Summary
This study introduces adaptive migration intervals for island models, optimizing performance by adjusting communication frequency. These dynamic schemes improve computational efficiency and reduce communication overhead compared to fixed intervals.
Area of Science:
- Computer Science
- Artificial Intelligence
- Parallel Computing
Background:
- Island models are fundamental in parallel computing, with migration intervals significantly impacting dynamic behavior.
- Optimal setting of migration intervals remains a challenge, affecting model performance and efficiency.
Purpose of the Study:
- To develop and evaluate adaptive schemes for dynamically adjusting the migration interval in island models.
- To minimize communication effort and running time by adapting to fitness improvements.
Main Methods:
- Proposed adaptive schemes that increase the migration interval when no fitness improvement is found, minimizing communication.
- Decreased the migration interval upon detecting fitness improvements to rapidly disseminate new solutions.
- Developed a method to calculate upper bounds for expected running time and communication effort.
Main Results:
- Adaptive schemes demonstrated competitive or superior performance compared to optimal fixed migration intervals.
- Evaluated performance based on running time and communication effort (expected number of migrants sent).
- Example applications showed the effectiveness of adaptive strategies on common benchmark functions.
Conclusions:
- Adaptive migration intervals offer a flexible and efficient approach to managing island models.
- The proposed schemes provide a robust method for optimizing performance in terms of speed and communication costs.
- This research contributes to a better understanding and practical application of parameter tuning in distributed evolutionary algorithms.
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