A Multi-Objective Hybrid Optimization Algorithm for Project Selection Problem

Choosing a portfolio of projects that meets an organization's objectives without exceeding available capital resources is recognized as a critical issue for which the decision maker takes several aspects into consideration. As most of these aspects may be conflicting, the problem can be considered as a multi-objective one. Correspondingly, in this study a special Multi-Objective Evolutionary Algorithm (MOEA) based on harmony search algorithm (HAS) is designed to solve the project selection problem. The original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local Search (CLS). In the proposed algorithm an external repository considered to save nondominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiment results show the capability of the proposed multi-objective algorithm in project selection problem.