Framework for wrapping binary swarm optimizers to the hybrid parallel cooperative coevolving version

In recent decades with the increase in the complexity of the problems, the need for high-performance and scalable optimization tools has been inevitable. Among different phenomena introduced to optimization problems, naturally inspired algorithms are favored. Also, encountering large-scale problems, high-performance tools like parallel implementations should be needed. In order to tackle this problem, the framework has been proposed that can wrap any swarm algorithm into an outperformer parallel and hybrid version. Six accepted swarm algorithms are selected to evaluate performance and compare the wrapped version with standard versions. Six nonlinear high-dimension benchmark functions are used to test the proposed algorithms. The experimental results show that wrapped versions outperform standard versions with the measurement of average best fitness.

Automated summary

In recent decades, there has been an inevitable need for high-performance and scalable optimization tools due to the increasing complexity of problems. Naturally inspired algorithms are favored among different phenomena introduced to optimization problems. Additionally, parallel implementations such as the framework proposed are necessary for handling large-scale problems. Six accepted swarm algorithms were selected to compare the performance of the wrapped version with standard versions, using six nonlinear high-dimension benchmark functions. Experimental results indicate that the wrapped versions outperform the standard versions in terms of average best fitness.