Research Article
Morphing Wing Structural Optimization Using Opposite-Based Population-Based Incremental Learning and Multigrid Ground Elements
Initialization Probability matrix , opposite probability matrix , external Pareto archive Pareto = . | (1) Generate a binary population from . | (2) Decode the binary population to be and find the objective values . | (3) Update Pareto by replacing it with the non-dominated solutions of a union set Pareto . | (4) If the number of members in Pareto exceeds the predefined archive size , remove some of them by using an archiving | technique. | (5) If the termination criterion is fulfilled, stop the procedure. Otherwise, go to step 6: | (6) Update and create a binary population | (6.1) Set a binary population . | (6.2) For = 1 to . | (6.2.1) Select binary solutions from Pareto randomly. | (6.2.2) Find using (4). | (6.2.3) Update the th row of by using (3). | (6.2.4) Compute and generate the th row of the opposite probability matrix using (9). | (6.2.5) Generate rand a uniform random number. | (6.2.6) If rand < the predefined mutation probability, update the th row of and using (5). | (6.2.7) Generate binary subpopulations and from the th row of and respectively. | (6.2.8) Set | (6.3) Next . | (7) Go to step 2. |
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