hybrid multi-population genetic algorithm for multi criteria project selection

Resources scarcity, available capabilities and cost-bene t point of view, make it essential to select the best project(s) from available projects. project selection process has a signi cant role in the success of investment. the main question is what projects should be nanced? applied approach to answer this, should be real, fast, global, exible, economic and easy to use. it is clear that choosing a good approach for project selection problem with economic and non-economic criteria can be vital for a project manager to success within constraints. the complexity of the problem increases when the number of projects and the number of objectives increase. therefore, in this research we aim to present a new heuristic method based on genetic and simulates annealing to select and rank available projects based on economic and non-economic criteria. presented method starts from initial solutions including multi population generated solutions, and moves toward the nal solution based on genetic operators and objective function. the proposed algorithm is evaluated on a set of randomly generated test problems with varying complexity. comparison studies between our method with other recently method in the literature demonstrates the capability of it to nd a good basket of projects. experimental results prove that this method is applicable for all kinds of projects basket.