Performance Evaluation of Genetic Algorithm & Fuzzy Logic for Portfolio Optimization
Darsha Panwar1, Manoj Jha2, Namita Srivastava3
1Darsha Panwar, Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal (M.P.), India.
2Manoj Jha, Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal (M.P.), India.
3Namita Srivastava, Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal (M.P.), India
Manuscript received on 7 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 1996-2002 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4494098319/19©BEIESP | DOI: 10.35940/ijrte.C4494.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Teaching-learning based optimization (TLBO), biogeography-based optimization (BBO) and fuzzy multi-objective linear programming (FMOLP) are compared in this paper for portfolio optimization. A hybrid approach has been adopted for this comparative study which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP) and optimization techniques. Return, risk, liquidity, coefficient of variation (CV) and AHP weighted scores are used as the objective function for optimization.
Keywords- Analytical hierarchy process; Biogeography-based algorithm; Cluster analysis; Fuzzy multi-objective linear programming; Portfolio optimization; Teaching-learning based algorithm.
Scope of the Article: Fuzzy Logics