This paper presents a novel algorithm based on generalized opposition-based learning
(GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional …

Opposition-based learning (OBL) is an effective strategy to enhance many optimization
methods among which opposition-based differential evolution (ODE) is one of the successful …

Since it first appeared, differential evolution (DE), one of the most successful evolutionary
algorithms, has been studied by many researchers. Theoretical and empirical studies of the …

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with
nonlinear and complex problems. However, these population-based algorithms are …

This work investigates the performance of Differential Evolution (DE) and its opposition-
based version (ODE) on large scale optimization problems. Opposition-based differential …

Solving high-dimensional global optimization problems is a time-consuming task because of
the high complexity of the problems. To reduce the computational time for high-dimensional …

Global optimization has been a hot research topic in various engineering applications,
where differential evolution (DE) is one of the most popular approaches. Actually, it is …

Opposition-based learning (OBL) is an effective strategy that adjusts the population to
accelerate the convergence of the algorithm. However, OBL involves two phases …

The selection of the mutation strategy for differential evolution (DE) algorithm plays an
important role in the optimization performance, such as exploration ability, convergence …

In this paper, an enhanced version of the opposition-based differential evolution (ODE) is
proposed. ODE utilizes opposite numbers in the population initialization and generation …