The Slime Mould Algorithm for Demand Side Management proceeds in the following steps: (1) Initialization: Generate an initial population of slime moulds with random positions within the permissible DSM strategy space. (2) Fitness Evaluation: Evaluate the fitness of each slime mould based on the objective function (Dpeak). (3) Loop Until Convergence: For each iteration, update positions based on fitness, simulate rhythmic contraction and expansion (oscillation phase), reinforce strategies yielding lower peak loads (positive feedback), adjust search strategy based on stochastic oscillation (adaptation), and retain the best-found solutions (selection). (4) Output the Best Solution: Display the best-found solution upon reaching the final iteration.

Authors

• Person: Not available Organization: PLOS ONE
  Demand side management using optimization strategies for efficient ...

Sources

• Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE via serper

Referenced by nodes (3)

• peak load concept
• Slime Mould Algorithm concept
• positive feedback loops concept