Research Article
Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method
(1) Inputs: Generate uniform and random points, from the search space to form population ; | (2) : the number of points selected for LS; | (3) : the number of iterations of LS for concentration; | (4) : the number of iterations of LS for refining solution; | (5) : population size; | (6) FES: number of function evaluations; | (7) : generation counter; | (8) : interval between the LS calls; | (9) error: desired accuracy for LS method; | (10) , ; | (11) Evaluate the population; | (12) Set and ; | (13) while do | (14) Start the algorithm with JADE by using (4) for generating mutant vector, (2) for trial vector, | (3) for best solution selection and (6) and (7) for adaptation of control parameters; | (15) Explore the population for generations. | (16) Sort the objective values; | (17) Select best points; | (18) for to do | (19) Apply iteration of BFGS to these points; | (20) if then | (21) Break; | (22) else if then | (23) Update the population by adding new points to it such that its size becomes ; | (24) Sort the objective values; | (25) Delete the worse individuals from ; | (26) end if | (27) end for | (28) Apply JADE to this new population until next generations; | (29) if then | (30) Break; | (31) else | (32) ; | (33) end if | (34) end while |
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