Analysis of evolutionary swarm intelligence optimization based on neighborhood topology design and benchmark functions

Abstract

The evolutionary algorithms based on swarm intelligence are analyzed in this paper. The artificial bee colony and particle swarm optimization methods are described in detail to better understand the theory behind swarm optimization. The particle swarm optimization with perturbations and PSO in the software-defined network node distribution algorithm was helpful to explore the search space options more clearly. In the state-of-the-art research, while building the PSO algorithm, the balance of the objective and penalty functions is ranked stochastically, and it is tested based on 13 benchmark functions. Also, the optimum solution is dependent on the random connections of the neighborhood topology design and the relation with the benchmark functions. So, the correct optimization process algorithm building will depend on the variable selection, random connection analysis among the neighbor swarm particles, and the usage of the testing functions. In our future work, we are planning to develop an ideal algorithm to utilize all these aspects to create an environment for better network optimization.