By Haiping Ma, Dan Simon

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Instead of testing λk against a random number once for each solution feature, we could test λk against a random number only once for each solution, and then if immigration were selected, we could replace all of the solution features in zk. 2. 2. Outline of total immigration-based BBO with a population size of N. { xk } is the population of solutions and { zk } is a temporary population of solutions. xk is the kth candidate solution, and xk ( SIV ) is the solution feature SIV of xk BBO Extensions 53 As a third option, we could first use μk to decide whether or not to emigrate a solution feature from a given solution.

Comparison of real-world optimization results for SGA, SPSO 07, ADE and BBO. The best result in each row is shown in bold BBO Extensions 59 If we use more advanced versions of GA, PSO and DE, it might be possible to obtain better results than those here. However, the same could be said for other improvements of BBO. The purpose of these comparisons is not to tune our algorithms to obtain the best possible performance for specific problems, but rather to show that BBO is a competitive algorithm for real-world optimization problems.

Could depend on the number of ψ iterations, or the HSI of the best habitat, or some other problem-dependent termination criterion. A BBO algorithm can be broadly described as follows: ϑ While not Τ ψ End The above outline of BBO can be written as follows. 5. Outline of a basic BBO algorithm with a population size of N. 5, migration and mutation for each habitat in the current generation occurs before any of the habitats are replaced in the population, which requires the use of the temporary population z .

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