  In this paper, we propose a predictor-corrector type Consensus Based
Optimization (CBO) algorithm on a convex feasible set. Our proposed algorithm
generalizes the CBO algorithm in [11] to tackle a constrained optimization
problem for the global minima of the non-convex function defined on a convex
domain. As a practical application of the proposed algorithm, we study the
portfolio optimization problem in finance. In this application, we introduce an
objective function to choose the optimal weight on each asset in a asset-bundle
which yields the maximal expected returns given a certain level of risks.
Simulation results show that our proposed predictor-corrector type model is
successful in finding the optimal value.
