In recent years, several multi-method and multi-operator-based algorithms have been
proposed for solving optimization problems. Generally, their performance is better than other …

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical
optimization. However, the performance of DE significantly relies on its mutation operator …

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 …

Over past few decades, as a powerful analytical tool to characterize the fitness landscape of
a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all …

The traditional particle swarm optimization algorithm learns from the two best experiences:
the best position previously learned by the particle itself and the best position learned by the …

This paper introduces integrated Returnable Transport Items (RTIs) and perishable products
closed loop green inventory routing problem with uncertain demand and pickup. The …

Bat algorithm (BA) turns into the most generally utilized meta-heuristic algorithm to solve the
different sort of global optimization problems. In the optimization of continuous data, BA …

Due to the problems of low accuracy and increasing control parameters in the existing
parameter adaptive methods of differential evolution (DE) algorithm, in this paper a mutation …

The fitness landscape is a theory applied to the evolutionary dynamics of biological
evolution to explain the behavior of evolutionary algorithms in the solution of optimization …

Differential evolution (DE) and Estimation of distribution algorithm (EDA) exhibit
complementary superiority in solving complex continuous optimization and engineering …