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

Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer
(WSO) to solve optimization problems over a continuous search space. The core ideas and …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Metaheuristics are popularly used in various fields, and they have attracted much attention
in the scientific and industrial communities. In recent years, the number of new metaheuristic …

Metaheuristics are an impressive area of research with extremely important improvements in
the solution of intractable optimization problems. Major advances have been made since the …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

This paper deals with the problem of coordinating a truck and multiple heterogeneous
unmanned aerial vehicles (UAVs or drones) for last-mile package deliveries. Existing …

Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired
by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has …