Abstract
This paper presents an experimental study with the objective’s functions of a portfolio optimization problem. This study is done by three optimization problems with a different number of objectives. A hybrid approach has been adopted for this which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP), and optimization techniques. Teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), and fuzzy multi-objective linear programming (FMOLP) are compared in this paper for portfolio optimization. From this research, the conclusion comes that there should not be more options in the objective functions, otherwise the motive of the portfolio becomes misleading, but many more parameters can be used for stock valuation.
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