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

The ABC algorithm is one of the popular optimization algorithms and has been used
successfully in solving many real-world problems. Numeric, binary, integer, mixed integer …

Metaheuristics play a critical role in solving optimization problems, and most of them have
been inspired by the collective intelligence of natural organisms in nature. This paper …

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or
artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm …

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play
a crucial role in algorithms performance. It reduces the processing time and accuracy of the …

Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …

In the past decade, considerable advances have been made in the field of computational
intelligence and operations research. However, the majority of these optimisation …

Abstract Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics often
applied to finding approximate solutions to difficult optimization problems. Despite being …

Abstract Travelling Salesman Problem (TSP) is an Np-Hard problem, for which various
solutions have been offered so far. Using the Harris Hawk Optimization (HHO) algorithm, this …

Truck-drone hybrid delivery is a hybrid one combining the advantages including large
capacity of truck and high travel speed of drone together. Vehicle routing problem with …