Population-based heuristic optimization methods like differential evolution (DE) depend
largely on the generation of the initial population. The initial population not only affects the …

Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

Real world optimization problems are challenging as they often involve a large number of
variables and highly nonlinear constraints and objective functions. While a number of …

Differential evolution (DE) is a variant of the evolutionary algorithm and has good
performance in solving continuous optimization problems. Two important parameters, F and …

Differential evolution (DE) is a simple and efficient global optimization algorithm. When
differential evolution is applied in complex optimization problems, it has the shortages of …

In this paper, we present a first attempt at self-adapting the population size parameter in
addition to self-adapting crossover and mutation rates for the Differential Evolution (DE) …

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of
Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization …

Differential evolution algorithm is a strong effective method for optimization problems.
Parameter setting is one crucial point to improve the DE's performance. Hence, a DE based …

Differential evolution (DE) algorithms tend to be limited to local optimization when solving
complex optimization problems. Different iteration schemes lead to different convergence …

Differential evolution (DE) is a powerful and versatile evolutionary algorithm for global
optimization over continuous search space, whose performance is significantly influenced …