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 …

车辆路径优化问题一直以来是物流研究领域的一个热点和难点. 现实生活的许多问题都可看作是
车辆路径问题(VRP), 因此国内外学者近年来不断提出多种车辆路径优化问题及求解方法以解决 …

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) …

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 …

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