Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm
motivated by intelligent collective behavior of some animals such as flocks of birds or …

Over the ages, nature has constantly been a rich source of inspiration for science, with much
still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial …

A gravitational search algorithm (GSA) uses gravitational force among individuals to evolve
population. Though GSA is an effective population-based algorithm, it exhibits low search …

In most metaheuristic algorithms, the updating process fails to make use of information
available from individuals in previous iterations. If this useful information could be exploited …

Particle swarm optimizer is a well-known efficient population and control parameter-based
algorithm for global optimization of different problems. This paper focuses on a new and …

This paper explores biogeography-based learning particle swarm optimization (BLPSO).
Specifically, based on migration of biogeography-based optimization (BBO), a new …

The hybrid flowshop scheduling problem (HFSP) has been widely studied in the past
decades. The most commonly used criterion is production efficiency. Green criteria, such as …

Over the last few decades, many population-based swarm and evolutionary algorithms were
introduced in the literature. It is well known that population topology or sociometry plays an …

In this work, a comprehensive review of the multi-objective grey wolf optimizer (MOGWO) is
provided. In multi-objective optimization (MO), more than one objective function must be …