Dynamic group optimization algorithm with a mean–variance search framework

Abstract

Dynamic group optimization has recently appeared as a novel algorithm developed to mimic animal and human socialising behaviours. Although the algorithm strongly lends itself to exploration and exploitation, it has two main drawbacks. The first is that the greedy strategy, used in the dynamic group optimization algorithm, guarantees to evolve a generation of solutions without deteriorating than the previous generation but decreases population diversity and limit searching ability. The second is that most information for updating populations is obtained from companions within each group, which leads to premature convergence and deteriorated mutation operators. The dynamic group optimization with a mean–variance search framework is proposed to overcome these two drawbacks, an improved algorithm with a proportioned mean solution generator and a mean–variance Gaussian mutation. The new proportioned mean solution generator solutions do not only consider their group but also are affected by the current solution and global situation. The mean–variance Gaussian mutation takes advantage of information from all group heads, not solely concentrating on information from the best solution or one group. The experimental results on public benchmark test suites show that the proposed algorithm is effective and efficient. In addition, comparative results of engineering problems in welded beam design show the promise of our algorithms for real-world applications.