Multi-population based nature-inspired optimization algorithms have attracted wide research
interests in the last decade, and become one of the frequently used methods to handle real …

The performance of most metaheuristic algorithms depends on parameters whose settings
essentially serve as a key function in determining the quality of the solution and the …

Differential evolution (DE) is among the most efficient evolutionary algorithms (EAs) for
global optimization and now widely applied to solve diverse real-world applications. As the …

This article presents the second Part of a two-Part survey that reviews evolutionary dynamic
optimization (EDO) for single-objective unconstrained continuous problems over the last two …

Many real-world optimization problems are dynamic. The field of dynamic optimization deals
with such problems where the search space changes over time. In this two-part article, we …

The sine cosine algorithm (SCA) is a population-based metaheuristic strategy that has been
demonstrated competitive performance and has received significant attention from scientists …

This paper presents a novel framework for improving the performance of multi-population
algorithms in solving dynamic optimization problems (DOPs). The fundamental idea of the …

Differential evolution (DE) is a population-based stochastic optimization technique that can
be applied to solve global optimization problems. The selected mutation strategies and the …

The main objective of this paper is to correct the unreasonable and inaccurate criticism to
our previous experiments using Teaching–Learning-Based Optimization algorithm and to …

This paper presents a multi-population differential evolution algorithm to address dynamic
optimization problems. In the proposed approach, a cellular learning automaton adjusts the …