@article{scholars18795,
       publisher = {Institute of Advanced Engineering and Science},
         journal = {IAES International Journal of Artificial Intelligence},
           pages = {305--314},
            year = {2023},
           title = {An adaptive metaheuristic approach for risk-budgeted portfolio optimization},
          number = {1},
            note = {cited By 1},
          volume = {12},
             doi = {10.11591/ijai.v12.i1.pp305-314},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140461793&doi=10.11591\%2fijai.v12.i1.pp305-314&partnerID=40&md5=7a8e4feae49fc89941a2feebab80435b},
        abstract = {An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential \& arithmetic) together with the hall of fame (HF) via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study. {\^A}{\copyright} 2023, Institute of Advanced Engineering and Science. All rights reserved.},
            issn = {20894872},
          author = {Gandikota, N. S. K. and Hasan, M. H. and Jaafar, J.}
}