A Multiobjective Particle Swarm Optimization Model for Reservoir Operations and Planning

This paper presents an application of an evolutionary optimization algorithm for multiobjective analysis for reservoir operations and planning. A multiobjective particle swarm optimization (MOPSO) algorithm is used to find nondominated solutions with four objectives: (i) maximize annual firm water supply; (ii) maximize annual firm energy production; (iii) minimize flood risk; and (iv) maximize the overall reliability of the system. The results of this study showed that the MOPSO algorithm was able to find well distributed Pareto solutions in the objective space. An interactive graphical method was also developed to display nondominated solutions in such way that the best compromise solutions can be identified, for different relative importance given to each objective. The method allows the decision maker to explore the Pareto set and visualize not only the best compromise solution but also sets of solutions that provide similar compromises.