Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

Algorithm 2

Pseudocode of proposed firefly algorithm based memetic algorithm.

Begin

 Initialize the parameters in MA, such as, swarm size 𝑛, scaling parameters 𝑆_𝑘, the maximum iteration

 number, and default search step Δ in PS;

 Generate the initial population of fireflies {𝑥_𝑖,𝑖=1,2,...,𝑛}, using Latin Hypercube Sampling (LHS)

 method;

 Evaluate the fitness of each firefly in the population: use each firefly to train the SVR based forecasting

 model and calculate the fitness (MAPE);

 While (Termination conditions are not satisfied)

  For  𝑖=1: 𝑛 in all 𝑛 fireflies

   For  𝑗=1: 𝑖 in all 𝑛 fireflies

    If (𝐼_𝑗>𝐼_𝑖) firefly 𝑖 move to a new position according to (11); End If

    Evaluate the fitness of each firefly and update its light intensity;

   End For

  End For

  Rank the fireflies and find the current best;

  %%%% Conduct individual learning with pattern search with a probability pl

  For  𝑘=1: 𝑛 in all 𝑛 fireflies

   If (rand>pl(𝑥_𝑘))

    Conduct individual learning for firefly 𝑥_𝑘 with pattern search (as shown in Algorithm 1);

   End If

  End For

 End While

 Output the best firefly as the final optimal solution

End