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An extended crowding genetic algorithm (ECGA) is introduced for solving optimal pumpconfiguration problem, which was presented by T.Westerlund in 1994. This problem has been found tobe non-convex, and the objective hation contained several local optima and global optimality couldnot be ensured by all the traditional MINLP optimization method. The concepts of species conservingand composite encoding are introduced to crowding genetic algorithm (CGA) for mantain the diver-sity of population more effectively and coping with the continuous and/or discrete variables in MINLPproblem. The solution of three-levels pump configuration got from DICOPT++ software (OA algo-rithm) is also given. By comparing with the solutions obtained from DICOPT++, ECP method, andMIN-MIN method, the ECGA algorithm proved to be very effective in finding the global optimalsolution of multi-levels pump configuration via using the problem-specific information.
An extended crowding genetic algorithm (ECGA) is introduced for solving optimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem has been found to be non-convex, and the objective of constructing a local optima and global optimality could not be ensured The all of the traditional MINLP optimization method. The concepts of species conservation and composite encoding are introduced to crowding genetic algorithm (CGA) for mantain the diver-sity of population more effectively and coping with the continuous and / or discrete variables in MINLP problem. The solution of Three-levels pump configuration got from DICOPT ++ software (OA algo-rithm) is also given. By comparing with the solutions obtained from DICOPT ++, ECP method, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the global optimal solution of multi-levels pump configuration via using the problem-specific information.