Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gunjan, Abhishek; Bhattacharyya, Siddhartha Title A name given to the resource Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Multilevel Quantum Metaheuristics: Applications in Data Exploration;pp.155-204 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33136-7.00007-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33136-7.00007-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032958834?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032958834?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gunjan A., Christ (Deemed to be) University, Karnataka, Bangalore, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic, Algebra Bernays University, Zagreb, Croatia Description An account of the resource The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource evolutionary algorithm; multilevel quantum-inspired techniques; Portfolio optimization; quantum-inspired ant colony optimization (QiACO); quantum-inspired particle swarm optimization (QiPSO) Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333136-7; 978-044333137-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online