"The problem of determining the optimal long-term operation of a hydroelectric power system has been the subject of numerous publications over the past sixty years. A major problem encountered in operating long-term hydroelectric power... more
"The problem of determining the optimal long-term operation of a hydroelectric power system has been the subject of numerous publications over the past sixty years. A major problem encountered in operating long-term hydroelectric power system is their dimensionality. A great effort to decrease or eliminate possibility of dimensionality problem is addressed through developing innovative optimisation techniques, such as genetic algorithms, artificial neural networks, and so on. Particle swarm optimisation (PSO), a newly developed evolutionary technique, is a population based stochastic search technique with reduced memory requirement, computationally effective and easily implemented compared to other evolutionary algorithm. However, there exist some difficulties in applying PSO to hydropower system. Constrained by complex constraints and hydraulic relationships between upper and lower reservoirs, it is unfeasible to use stochastic search algorithms of PSO directly for most initial populations. In this paper, a two stage PSO algorithm is presented to solve the optimal long-term operation of a hydroelectric power system. The maximisation of electricity generation and maximisation of minimal mean power of the hydropower system are alternatively used as the objective of long- term planning of hydroelectric power for the two stage problem. The maximisation of minimal mean power of the hydropower system is chosen as the objective at the first stage and an initial feasible solution will be generated using PSO. The system objective, ie the maximisation of electricity generation is selected as the objective at the second stage and the optimal result of the first stage will be used as the initial feasible solution. The proposed method is implemented to the optimal long-term operation of a hydroelectric power system in the Yunnan Power Grid which is located in the Yunnan Province of China and consists of 77 dominated hydropower plants with an installed capacity of 3,942.5 MW. The results show that the two stage PSO can give reasonable and efficient solution and that applying PSO to the long-term operation of a hydroelectric power system is feasible.
A hyper-heuristic refers to a search method or a learning mechanism for selecting or generating heuristics to solve computational search problems. Operating at a level of abstraction above that of a metaheuristic, it can be seen as an... more
A hyper-heuristic refers to a search method or a learning mechanism for selecting or generating heuristics to solve computational search problems. Operating at a level of abstraction above that of a metaheuristic, it can be seen as an algorithm that tries to find an appropriate solution method at a given decision point rather than a solution. This paper introduces a new hyper-heuristic that combines elements from reinforcement learning and tabu search. It is applied to solve two complex stochastic scheduling problems arising in mining, namely the stochastic open-pit mine production scheduling problem with one processing stream (SMPS) and one of its generalizations, SMPS with multiple processing streams and stockpiles (SMPS+). The performance of the new hyper-heuristic is assessed by comparing it to several solution methods from the literature: problem-specific algorithms tailored for the two problems addressed in the paper and general hyper-heuristics, which use only limited problem-specific information. The computational results indicate that not only is the proposed new hyper-heuristic approach superior to the other hyper-heuristics, but it also provides results that are comparable to or improve on the results obtained by the state-of-the-art problem-specific methods.
