The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydro-power plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with minimal water use. This paper presents an evolutionary approach to optimize the multi-criteria electric dispatch problem in a general HPP, based on a Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm. The proposed approach integrates mathematical models and evolutionary swarm computation. The experimental analysis shows that the proposed MESH algorithm is able to reach competitive results when compared with classical evolutionary algorithms, the NGA-II and SPEA2 basing on ANOVA inference test. Results also show that the proposed MESH is able to save a large amount of water in the energy production process, supplying the requested load, and minimizing blackout risks and generating a profit around $275,000 monthly.

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.

Recommendation systems and the performance of computer network systems have fundamental implications over each other. While recommendation systems impact system performance, the latter can be used to guide the former. In this paper, we study the interconnections between recommendation systems and the performance of the network. Focusing on swarming systems à la Bittorrent, we propose an analytical model to capture the revenue and the cost to a content provider as a function of the quality of its recommendations and the cost to serve the content. The model is then used to suggest heuristics on how to recommend content accounting for service costs and user preferences.