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 field of metaheuristics for the application to combinatorial optimization problems is a
rapidly growing field of research. This is due to the importance of combinatorial optimization …

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global
optimization and engineering design problems. This improvement is proposed to alleviate …

This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA)
and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with …

This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm
(GOA) and applies it to challenging problems in structural optimisation. The proposed …

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame
Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method …

This study tenders a new nature-inspired metaheuristic algorithm (MA) based on the
behavior of the Genghis Khan shark (GKS), called GKS optimizer (GKSO), which is used for …

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 …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Swarm intelligence is a relatively new approach to problem solving that takes inspiration
from the social behaviors of insects and of other animals. In particular, ants have inspired a …