Master-Slave Optimization (MSO)

版本 1.0.0 (2.1 KB) 作者: praveen kumar
Rastrigin Function is tested
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更新时间 2024/11/15

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Explanation of the Code:
  1. Initialization: The algorithm initializes a population of slave agents, distributing them randomly within the given bounds.
  2. Parallel Evaluation (parfor): The parfor loop (from MATLAB’s Parallel Computing Toolbox) is used to distribute solution evaluations across multiple slave agents in parallel, speeding up the optimization process.
  3. Solution Perturbation: Each slave agent generates a new solution by adding a small random perturbation to its current position and then evaluates the fitness of this new solution.
  4. Master Node: Collects results from all slave agents, updates the global best solution, and manages the iteration process.
  5. Objective Function: The Rastrigin function is used as a test case, but you can replace it with your own objective function.
Customization:
  • Objective Function: Replace objFunc with your custom function to optimize.
  • Parameters: Adjust the number of slave agents, iterations, and search space bounds as needed.
  • Parallel Computing: Ensure you have the Parallel Computing Toolbox installed and configured to use parfor.
Note:
  • The parfor loop can significantly speed up computations for problems that are expensive to evaluate. If you don’t have the Parallel Computing Toolbox, you can replace parfor with a regular for loop, but it will run sequentially.

引用格式

praveen kumar (2024). Master-Slave Optimization (MSO) (https://www.mathworks.com/matlabcentral/fileexchange/175763-master-slave-optimization-mso), MATLAB Central File Exchange. 检索时间: .

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master-slave optim

版本 已发布 发行说明
1.0.0