Hybrid particle swarm optimization for multiobjective resource allocation

Resource allocation (RA) is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such as maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, an improved particle swarm algorithm (mO_PSO) is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.