Pump Scheduling Optimization Using Asynchronous Parallel Evolutionary Algorithms

Optimizing the pump-scheduling is an interesting proposal to achieve cost reductions in water distribution pumping stations. As systems grow, pump-scheduling becomes a very difficult task. In order to attack harder pump-scheduling problems, this work proposes the use of parallel asynchronous evolutionary algorithms as a tool to aid in solving an optimal pump-scheduling problem. In particular, this work considers a pump-scheduling problem having four objectives to be minimized: electric energy cost, maintenance cost, maximum power peak, and level variation in a reservoir. Parallel and sequential versions of different evolutionary algorithms for multi- objective optimization were implemented and their results compared using a set of experimental metrics. Analysis of metric results shows that our parallel asynchronous implementation of evolutionary algorithms is effective in searching for solutions among a wide range of alternative optimal pump schedules to choose from.

Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms

The migration interval is one of the fundamental parameters governing the dynamic behaviour of island models. Yet, there is little understanding on how this parameter affects performance, and how to optimally set it given a problem in hand. We propose schemes for adapting the migration interval according to whether fitness improvements have been found. As long as no improvement is found, the migration interval is increased to minimise communication. Once the best fitness has improved, the migration interval is decreased to spread new best solutions more quickly. We provide a method for obtaining upper bounds on the expected running time and the communication effort, defined as the expected number of migrants sent. Example applications of this method to common example functions show that our adaptive schemes are able to compete with, or even outperform, the optimal fixed choice of the migration interval, with regard to running time and communication effort.