Abstract: In order to improve the convergence rate and the distribution of the solution set of the multi-objective shark algorithm, this paper proposes a multi-objective shark smell optimization algorithm based on decomposition and vector (DVMOSSO). Firstly, aiming at the problem that the convergence and diversity of the basic shark algorithm are difficult to balance, this paper uses the reference vector to calculate the angle penalty distance scalar value to balance the convergence and diversity of the solution in the target space in the process of elite centralized mining. In addition, the basic shark algorithm is easy to converge prematurely and fall into local optimum in the late iteration. In this paper, Gaussian mutation strategy is used to reinitialize the particles, and polynomial mutation is used to increase the diversity of the population in the elite solution set. Finally, in order to verify the effectiveness of the proposed algorithm, the proposed DVMOSSO algorithm is compared with NSGAII-DS, MOEA/D, MMOPSO, MOSSO and dMOSSO algorithm in the standard test function. The experimental results show that the proposed algorithm has good convergence and distribution, higher convergence accuracy and stronger optimization ability.

Key words: shark smell optimization algorithm, elite set, decomposition, vector, reinitialization, polynomial mutation