Portfolio optimization has always been a challenging proposition in finance and
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …

This paper presents a review of quantum-inspired population-based metaheuristics.
Quantum-inspired algorithms were born when there were no quantum computers; they …

Designing efficient and secure cryptosystems has been a preoccupation for many scientists
and engineers for a long time wherein they use chaotic systems to design new …

In this paper, we have formulated quantum beetle antennae search (QBAS), a meta-heuristic
optimization algorithm, and a variant of beetle antennae search (BAS). We apply it to …

Over the last few decades, quantum machine learning has emerged as a groundbreaking
discipline. Harnessing the peculiarities of quantum computation for machine learning tasks …

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem
which can be found in many research domains. The optimal solution of the RCPSP …

Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations …

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that
hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization …

Quantum-inspired metaheuristics emerged by combining the quantum mechanics principles
with the metaheuristic algorithms concepts. These algorithms extend the diversity of the …

The success of machine learning models over the last few years is mostly related to the
significant progress of deep neural networks. These powerful and flexible models can even …