The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has
gained prominence in the last two decades due to its ease of application in unsupervised …

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 …

Many optimization problems in reality involve both continuous and discrete decision
variables, and these problems are called mixed-variable optimization problems (MVOPs) …

The exponential growth in Internet of Things (IoT) devices and the limitations of cloud
computing in terms of latency and quality of service for time-sensitive applications have led …

This paper proposes an improved version of the grasshopper optimization algorithm (GOA)
based on the opposition-based learning (OBL) strategy called OBLGOA for solving …

With a global search mechanism, particle swarm optimization (PSO) has shown promise in
feature selection (FS). However, most of the current PSO-based FS methods use a fix-length …

The increasing popularity of battery-limited electric vehicles puts forward an important issue
of how to charge the vehicles effectively. This problem, commonly referred to as Electric …

The Fog computing paradigm, offering cloud-like services at the edge of the network, has
become a feasible model to support computing and storage capabilities required by latency …

Exact optimization algorithms are not able to provide an appropriate solution in solving
optimization problems with a high-dimensional search space. In these problems, the search …