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Combining local search method with evolutionary-based algorithm has been introduced recently to improve performance of optimization on complex multi-objective problems (MOPs). Evolutionary algorithms (EAs) are attractive alternatives for solving the MOPs due to its global scope and independence of problem representation. However, it has been criticized for its relative slow convergence. Studies showed using local search method (LSM) can help to enhance convergence speed of the EA. Incorporating the LSM into the EA can mainly follow two ways: serial and concurrent. Serial approach applied the LSM after complement of the EA by predefining a switching time [1, 2]. The approach guarantees a local optimum with improved speed of convergence. Nevertheless, it is difficult to fix a priori switch timing on most of practical problems. Recent studies reported a concurrent approach that embedding the LSM within the EA optimizer [3, 4]. Some or all of the intermediate solutions from the EA can be modified by the LSM during the process. The LSM is treated as another operator in the EA thus to avoid the problem switch timing.


Session S7-01, Special Session: Optimizing Short Term Reservoir Operations I



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