In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Features Selection (FS) plays an important role in enhancing the performance of machine
learning techniques in terms of accuracy and response time. As FS is known to be an NP …

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to
visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or …

This article presents a multi-agent framework for optimization using metaheuristics, called
AMAM. In this proposal, each agent acts independently in the search space of a …

This paper presents a new metaheuristic algorithm called Trees Social Relations
Optimization Algorithm (TSR). TSR inspired by the hierarchical and collective life of trees in …

Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that
is based on control volume mass balance models, and has shown competitive performance …

With the sustainable development of cities, green transportation is regarded as a critical
approach to mitigate air pollution and greenhouse gas emissions. Driven by this, Electric …

Abstract Big Data optimization (Big-Opt) refers to optimization problems which require to
manage the properties of big data analytics. In the present paper, the Search Manager (SM) …

We present a novel regression algorithm that combines a Fuzzy Inference System (FIS) with
a nature-inspired algorithm to predict variations in GroundWater Levels (GWLs). Initially, we …

Emerging digital technologies and data advances (eg smart machinery, remote sensing) not
only enable Agriculture 4.0 to envisage interconnected agro-ecosystems and precision …