Metaheuristic algorithms are extensively recognized as effective approaches for solving high-
dimensional optimization problems. These algorithms provide effective tools with important …

This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …

The present paper proposes a novel nature-inspired optimization paradigm, which is called
the Water Strider Algorithm (WSA). The WSA is a population-based optimizer inspired by the …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

In this research, a new metaheuristic optimization algorithm, inspired by biological nervous
systems and artificial neural networks (ANNs) is proposed for solving complex optimization …

Meta-heuristic algorithms are problem-solving methods which try to find good-enough
solutions to very hard optimization problems, at a reasonable computation time, where …

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 …

Differential evolution (DE) is a simple, yet very effective, population-based search technique.
However, it is challenging to maintain a balance between exploration and exploitation …

The particle swarm optimization (PSO) algorithm has been the object of many studies and
modifications for more than 25 years. Ranging from small refinements to the incorporation of …

The analysis of the performance of different approaches is a staple concern in the design of
Computational Intelligence experiments. Any proper analysis of evolutionary optimization …