Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

This work aims to provide a broad overview of computational techniques belonging to the
area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both …

One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete
industry which has alone 50% of the world's emissions. One possible remedy to mitigate the …

This study proposed an improved grasshopper optimization algorithm (GOA) for continuous
optimization and applied it successfully to the financial stress prediction problem. GOA is a …

In this paper we present an extension of ant colony optimization (ACO) to continuous
domains. We show how ACO, which was initially developed to be a metaheuristic for …

The particle swarm optimization (PSO) is a relatively new generation of combinatorial
metaheuristic algorithms which is based on a metaphor of social interaction, namely bird …

Heuristic optimization provides a robust and efficient approach for solving complex real-
world problems. The focus of this research is on a hybrid method combining two heuristic …

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths
toward the light source is an effective approach to solve global optimization problems …

A Rosenbrock artificial bee colony algorithm (RABC) that combines Rosenbrock's rotational
direction method with an artificial bee colony algorithm (ABC) is proposed for accurate …

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly
non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony …