Comparison of representative heuristic algorithmsfor integrated reservoir optimal operation

Abstract Heuristic algorithms (HAs) are widely used in integrated reservoir optimal operation due to the fast calculation and simple design. Existing literature usually focuses on one or two categories of HAs and simply reviews the state of the art. To provide an overall understanding and specific comparison of HAs in integrated reservoir optimal operation, differential evolution (DE), particle swarm optimization (PSO), and artificial physics optimization (APO), which serves as typical examples of the three categories of HAs, were compared in terms of the development and applications using a designed experiment. Besides, the general model with constraints and fitness function, and the solving process using a hybrid feasible domain restoration method and penalty function method were also presented. Taking a designed experiment with multiple scenarios, four evaluation criteria are used for the comprehensive comparison. Results of the comparison show that (a) the problem of integrated reservoir optimal operation is a mathematic programming problem with the narrow feasible region and monotonic objective function; (b) it is easy to obtain the same optimal objective function value but different optimal solutions when using heuristic algorithms; and (c) DE, PSO and APO does not result in a clear winner, but DE could be more appropriate for integrated reservoir operation optimization according to the four evaluation criteria used in this study.