Evolutionary programming based multi-objective optimization for a heterogeneous system

In this paper we present a multi-objective optimization approach to optimize a heterogeneous system such as a ship board cooling system. Genetic Algorithm and Evolutionary Programming were used in combination to design the optimization algorithm. The developed multi-objective optimization approach was first implemented and tested on an electrical power system. For this system, voltage stability and power loss minimization were considered as competing objectives. The algorithm was verified using IEEE 57 bus system. The algorithm was then applied to a simulated small scale cooling system model onboard a ship system. This cooling system is a heterogeneous system consisting of fluid system, electrical system, and thermal system. For this heterogeneous system, water volume, cooling time, power usage, total distance traveled by water and number of switching operations were considered as the competing objectives.