On Asynchronous Non-Dominated Sorting for Steady-State Multiobjective Evolutionary Algorithms
This work addresses performance bottlenecks for researchers and practitioners using parallel and distributed multiobjective evolutionary algorithms, though it is incremental as it builds on existing incremental sorting methods.
The authors tackled the problem of inefficient parallelism in steady-state multiobjective evolutionary algorithms due to computationally expensive state updates, specifically non-dominated sorting in NSGA-II, by implementing an asynchronous version using concurrency techniques, resulting in experimental trade-offs between work-efficiency and parallelism.
In parallel and distributed environments, generational evolutionary algorithms often do not exploit the full potential of the computation system since they have to wait until the entire population is evaluated before starting selection procedures. Steady-state algorithms are often seen as a solution to this problem, since fitness evaluation can be done by multiple threads in an asynchronous way. However, if the algorithm updates its state in a complicated way, the threads will eventually have to wait until this update finishes. State update procedures that are computationally expensive are common in multiobjective evolutionary algorithms. We have implemented an asynchronous steady-state version of the NSGA-II algorithm. Its most expensive part, non-dominated sorting, determines the time needed to update the state. We turned the existing incremental non-dominated sorting algorithm into an asynchronous one using several concurrency techniques: a single entry-level lock, finer-grained locks working with non-domination levels, and a non-blocking approach using compare-and-set operations. Our experimental results reveal the trade-off between the work-efficiency of the algorithm and the achieved amount of parallelism.