Solving economic and environmental optimal control in dumping of sewage with a flexible and parallel evolutionary computation

Abstract

This chapter proposes a solution to a state constrained optimal control problem with parallel implementations of an intelligent optimization system based on evolutionary self-learning named flexible evolution agent (FEA). The agent has a dynamic structure of operators (DSO), enlargement of the genetic code (EGC) of each candidate solution, and uses a central control mechanism (CCM). The solution finds the optimal locations of liquid waste dumping sites in offshore waters, so the concentration of a considered pollutant must not exceed a pre-established value, either at a specific distance from the shore or in different protected areas, as can be fish hatcheries. The chapter also presents an island or coarse-grained parallel model with FEA where the FEA of the master works with integer variables and the FEA of the islands works with real variables. A 3D finite volume Taylor–Galerkin version for the pollutant concentration simulation is considered in the chapter Numerical results with new parallel evolutionary implementations are also presented.