強制操作とタブ戦略を導入した進化型計算による発電プラント起動スケジューリング

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Power plant start-up scheduling is aimed mainly at minimizing the start-up time of both boiler and turbine, while limiting turbine rotor stresses to acceptable values. This problem, with a number of local optima, can be formulated as a combinatorial optimization problem. In order to find the optimal or near-optimal start-up schedule efficiently, we applied evolutionary optimization techniques-Genetic Algorithms (GA)-with an "enforcement operator" and "reuse function" ( [Kamiya 95] in English). The enforcement operator is to limit the search of GA-combined with local search strategy-near the boundary of the feasible solution space, where the optimal solution is supposed to exist. The reuse function is to memorize the simulation results of the objective function of those previously generated solutions, and to reuse them whenever an identical solution is generated. In this paper, in order to increase the search efficiency further, we extend our proposed framework to integrate tabu strategy with the local search GA. The tabu strategy is to forbid some moves at a present iteration in order to avoid cycling and to make early escape from a local optimal point possible. Test results suggest that GA integrated with tabu strategy has the best performance among stand-alone GA, stand-alone tabu search and simulated annealing with or without tabu strategy. In addition, the optimized solution reduces the start-up time by approximately 10%, or 20 minutes, relative to conventional methods.
社団法人人工知能学会の論文
1997-01-01

強制操作とタブ戦略を導入した進化型計算による発電プラント起動スケジューリング

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