Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …

Differential evolution (DE) is one of the most efficient evolutionary algorithms for solving
numerical optimization problems; however, it still suffers from premature convergence and …

Electrostatic Force is one of the fundamental force of physical world. The concept of electric
field and charged particles provide us a strong theory for the working force of attraction or …

Dynamic multiobjective optimization problems (DMOPs) are optimization problems with
multiple conflicting optimization objectives, and these objectives change over time. Transfer …

Proposing new mutation strategies to improve the optimization performance of differential
evolution (DE) is an important research study. Therefore, the main contribution of this paper …

This paper presents adaptive guided differential evolution algorithm (AGDE) for solving
global numerical optimization problems over continuous space. In order to utilize the …

This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for
high-dimensional computationally expensive problems. The proposed algorithm includes …

Differential evolution (DE) has been experimentally demonstrated to be effective in solving
optimization problems. However, the effectiveness of DE encounters rapid deterioration in …

Crossover is a crucial operation for generating promising offspring solutions in evolutionary
multi-objective optimization. Among various crossover operators, the simulated binary …

Differential evolution (DE) is a simple and efficient global optimization algorithm. Benefitting
from its concise structure and strong search ability, DE has been widely used in various …