The set-based particle swarm optimisation algorithm is a swarm-based meta-heuristic that
has gained popularity in recent years. In contrast to the original particle swarm optimisation …

The size, nature, and diversity of the initial population of population-based metaheuristic
algorithms and the number of times the algorithm iterates play a significant role in the …

Metaheuristics have emerged as one of the prominent problem-solving methodologies to
handle optimization problems as they can provide good quality solutions within acceptable …

In last decades, lots of nature-inspired optimization algorithms are developed and presented
to the literature for solving optimization problems. Generally, these optimization algorithms …

Particle Swarm Optimization (PSO) algorithm is a population-based strong stochastic search
strategy empowered from the inherent way of the bee swarm or animal herds for seeking …

Artificial bee colony (ABC) optimisation algorithm is relatively a simple and recent population-
based probabilistic approach for global optimisation. ABC has been outperformed over …

Although various population initialization techniques have been employed in evolutionary
algorithms (EAs), there lacks a comprehensive survey on this research topic. To fill this gap …

Particle swarm optimiser (PSO) is one of the most accepted evolutionary computation
algorithms that have attracted incremental interest from scientists and engineers. The paper …

Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used
to solve nonlinear and complex problems. ABC is one of the simplest and up to date …

Particle swarm optimization (PSO) is a population-based stochastic algorithm modeled on
the social behaviors observed in flocking birds. Over the past quarter century, the particle …