Research Article
An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies
| (1) | Set , , , n, and | | (2) | Randomly initialize the test points | | (3) | | | (4) | Calculate fitness value | | (5) | | | (6) | while | | (7) | Calculate average value of all test points and its fitness value | | (8) | | | (9) | | | (10) | if | | (11) | | | (12) | end | | (13) | Generate quasi-opposite test points | | (14) | , | | (15) | if | | (16) | ; | | (17) | | | (18) | end | | (19) | Select good solutions from the original test points and the quasi-opposite test points | | (20) | Downward leader movement toward the ground | | (21) | for i = 1: | | (22) | randomly select | | (23) | if | | (24) | | | (25) | else | | (26) | | | (27) | end | | (28) | Calculate fitness value of new test points | | (28) | if | | (29) | | | (30) | end | | (31) | end | | (32) | Upward leader movement | | (33) | for i = 1: | | (34) | | | (35) | | | (36) | if | | (37) | | | (38) | end | | (39) | end | | (40) | Enhance intensive dimensional search | | (41) | Find | | (42) | for j = 1: n | | (43) | | | (44) | Calculate fitness value of the new solution | | (45) | | | (46) | if | | (47) | | | (48) | | | (49) | end | | (50) | end | | (51) | | | (52) | End |
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