Differential evolution (DE) is one of the highly acknowledged population-based optimization
algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems …

Metaheuristics are popularly used in various fields, and they have attracted much attention
in the scientific and industrial communities. In recent years, the number of new metaheuristic …

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm
Optimization and Differential Evolution. Their initial versions were very simple, but rapidly …

Jaya algorithm (JAYA) is a new metaheuristic algorithm, which has a very simple structure
and only requires population size and terminal condition for optimization. Given the two …

Most research on improving differential evolution algorithms has focused on mutation
operator and parameter control. In this paper, a new selection operator is proposed to …

Differential evolution and its variants have already proven their worth in the field of
evolutionary optimization techniques. This study further enhances the success history-based …

In this paper, we propose a Two-stage Differential E volution (TDE) with novel parameter
control for real parameter single objective global optimization. In the TDE algorithm, the …

In recent years, many different differential evolution (DE) variants have been proposed to
solve real-world optimization problems. However, the performance of them is largely …

In this paper, we proposed a novel fitness-value-independent Differential Evolution to tackle
complex real-parameter single-objective optimization. There were three innovations in the …

The main parameters that affect the performance of a differential evolution algorithm are the
scaling factor, crossover rate, and population size. To adaptively adjust the population …