Over the recent years, continuous optimization has significantly evolved to become the
mature research field it is nowadays. Through this process, evolutionary algorithms had an …

This review aims to exploit a study on different benchmark test functions used to evaluate the
performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH …

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of
optimization problems. This flexibility makes them attractive for many optimization problems …

A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced
using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency …

SHADE is an adaptive DE which incorporates success-history based parameter adaptation
and one of the state-of-the-art DE algorithms. This paper proposes L-SHADE, which further …

Meta-heuristic search algorithms (MHSs) are methods that take their inspiration from nature.
However, the fitness value information used in the design of the update mechanism in MHSs …

Proposing new mutation strategies to improve the optimization performance of differential
evolution (DE) is an important research study. Therefore, the main contribution of this paper …

The growing complexity of real-world problems has motivated computer scientists to search
for efficient problem-solving methods. Metaheuristics based on evolutionary computation …

Recently, increasing works have been proposed to drive evolutionary algorithms using
machine-learning models. Usually, the performance of such model-based evolutionary …

In this paper we present a differential evolution algorithm (iL-SHADE) for solving single
objective real-parameter optimization problems. It is an improved version of the well-known …