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 …

Ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in
the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions …

In recent years, various heuristic optimization methods have been developed. Many of these
methods are inspired by swarm behaviors in nature. In this paper, a new optimization …

Computer vision (CV) is a big and important field in artificial intelligence covering a wide
range of applications. Image analysis is a major task in CV aiming to extract, analyze and …

Abstract Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics often
applied to finding approximate solutions to difficult optimization problems. Despite being …

Swarm behaviors in nature have inspired the emergence of many heuristic optimization
algorithms. They have attracted much attention, particularly for complex problems, owing to …

Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior
of ant species that ants deposit pheromone on the ground for foraging. In this paper, ACO is …

Blood vessels in ophthalmoscope images play an important role in diagnosis of some
serious pathologies on retinal images. Hence, accurate extraction of vessels is becoming a …

Purpose The purpose of this paper is to investigate the performance of chaotic gravitational
search algorithm (CGSA) in solving mechanical engineering design frameworks including …

Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in
solving combinatorial and NP-hard optimization problems in various research fields …