In the last three decades, the field of computational intelligence has seen a profusion of
population‐based metaheuristics applied to a variety of problems, where they achieved …

Differential evolution (DE) algorithm suffers from high computational time due to slow nature
of evaluation. Micro-DE (MDE) algorithms utilize a very small population size, which can …

One of the main disadvantages of population-based evolutionary algorithms (EAs) is their
high computational cost due to the nature of evaluation, specially when the population size …

This paper proposes a novel implementation of micro-Differential Evolution (μDE) that
incorporates within the DE scheme an extra search move that attempts to improve the best …

The present paper offers an analysis strategy to examine benchmark sets of combinatorial
search problems. Experimental analysis has been widely used to compare a set of …

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic
Programming (GP) which has been applied to many optimization problems such as symbolic …

Differential evolution (DE) is a high performance and easy to implement evolutionary
algorithm. The DE algorithm with small population size (ie, micro-DE) can further increase …

Wireless‐powered mobile edge computing is a new network computing paradigm that
combines with the advantages of wireless power transfer and mobile edge computing. When …

The Bin Packing Problem is a classic optimization problem, over the years many heuristics
have been developed to obtain better results. There are many approaches to generating …

Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE).
A small population size decreases the computational complexity but also reduces the …