Abstract
To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms.
Highlights
As an important branch in artificial intelligence, Evolutionary Algorithms (EAs) are derived from the simulation of complex biological systems in nature with the ability to cognize things of humans based on their interaction with nature
Benchmarks F3-F5 are simple and multimodal; the MBBO algorithm uses the Affinity Propagation which can relatively accurately judge the global optimal solution according to the mutual affinity relationship between different solutions
The MBBO algorithm uses Affinity Propagation which can relatively accurately judge the global optimal solution according to the mutual affinity relationship between different solutions
Summary
As an important branch in artificial intelligence, Evolutionary Algorithms (EAs) are derived from the simulation of complex biological systems in nature with the ability to cognize things of humans based on their interaction with nature. This work proposes an improved BBO algorithm with Affinity Propagation (AP) [8] based on the Memetic framework [9,10,11,12] (MBBO). This algorithm improves the migration operation of the basic BBO algorithm by using the AP strategy to promote exploration. This work proposed an improved BBO algorithm using the AP strategy to modify the migration operation to promote exploration and. Proposed a MBBO algorithm using the Memetic framework and SA as the local search strategy to promote exploitation
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