Research Article
Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
Algorithm 2
New framework of DE with HLS.
| | Input: D, NP, F, CR, P, MaxFEs | | (1) | Randomly initialize population pop | | (2) | Evaluate the pop by objective function obj_func, get fit | | (3) | FEs = NP | | (4) | while FEs < MaxFEs do | | (5) | for i = 1: NP do | | (6) | Execute the mutation operator to generate a mutation vector | | (7) | Execute the crossover operator to generate a trial vector | | (8) | Evaluate the trial vector to get fit_ui | | (9) | FEs = FEs + 1 | | (10) | if fit_ui < fit(i) | | (11) | pop(i,:) = | | (12) | fit(i) = fit_ui | | (13) | else | | (14) | if rand < P | | (15) | offspring = HLS(, pop(i, :)) | | (16) | ovalue = obj_func(offspring) | | (17) | FEs = FEs + 4; | | (18) | [min_value, min_index] = min(ovalue) | | (19) | if min_value < fit(i) | | (20) | pop(i, :) = offspring(min_index) | | (21) | fit(i) = min_value | | (22) | end | | (23) | end | | (24) | end | | (25) | end | | (26) | end | | | Output: optimal solution |
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