Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Problem

Multi-objective portfolio optimization (PO) problem is always converted into a single objective problem by using the weighted method, which is sensitive to the pareto optimal front and requires that decision makers must have previous experience about the preference for weights. Based on multi-objective comprehensive learning bacterial foraging optimization (MOCLBFO), this paper proposes an algorithm which is specially designed for multi-objective PO problem. The corresponding coding strategy which considers each particle as a feasible solution is also given. In order to test the validity of the algorithm, multi-objective comprehensive learning particle swarm optimization (MOCLPSO) is chosen as the competing algorithm. Comparative experimental tests on ten assets PO problem demonstrate that MOCLBFO is able to find a more well-distributed Pareto set.