Evolving multi-objective neural networks using differential evolution for dynamic economic emission dispatch
Karl Mason, Jim Duggan, Enda Howley · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
This research presents a novel framework for evolving Multi-Objective Neural Networks using Differential Evolution (MONNDE). In recent years, the Differential Evolution algorithm has shown to be an effective and robust global optimisation algorithm. The algorithm uses evolutionary operators to optimise complex and continuous problem spaces and has been applied to a range of problems, recently including neural networks. This research continues this trend by utilizing differential evolution to evolve neural networks capable of addressing dynamic problems with multiple objectives. The proposed MONNDE framework is applied to the Dynamic Economic Emission Dispatch (DEED) problem. This problem consists of scheduling a group of power generators in a manner that minimises both cost and emissions produced by the generators. The power generators must also meet a series of constraints relating to their power output, power demand and network loss. The proposed MONNDE is performs very competitively when compared to algorithms such as NSGA-II, PSO, PSOAWL and MARL.