Drift Operator for States of Matter Search Algorithm

Abstract

States of matter search (SMS) algorithm is based on the simulation of the states of matter phenomenon. In SMS, individuals emulate molecules which interact to each other by using evolutionary operations which are based on the physical principle of the thermal-energy motion mechanism. Although the SMS algorithms have been used to solve many optimization problems, there still slow convergence and easy to fall into local optimum in some applications. In this paper, a novel drift operator-based states of matter search algorithm (DSMS) is proposed. The main idea involves using drift operator to keep the concept of location and abandon the concept of velocity for accelerate the convergence speed while simplifying algorithm, meanwhile a new variable differential evolution (DE) strategy is introduced to diversify the individuals in the search space for escape from the local optima. The proposed method is applied to several benchmark problems and is compared to four modern meta-heuristic algorithms. The experimental results show that the proposed algorithm outperforms other peer algorithms.