Simulation-based multimodal optimization of decoy system design using an archived noise-tolerant genetic algorithm

The difficulty of warship decoy system design problem is twofold. First, we need to find not just one but as many optimal solutions as possible. Second, it demands a heavy computation to evaluate a candidate solution through a long series of underwater warfare simulations. The previous approach tried to reduce the amount of search by heuristically selecting a set of plausible starting points for the search by a simulated annealing algorithm. However, it shows only limited success and cannot easily scale up to larger problems. This paper proposes an efficient and easy-to-scale-up multimodal optimization algorithm named A-NTGA that is based on a genetic algorithm. A-NTGA quickly evaluates candidate solutions by conducting only a small number of simulations, but instead copes with these inaccurate or noisy fitness values by using a noisy optimization technique. To further enhance the efficiency of search by promoting the population diversity, A-NTGA is provided with an archive to which some good-looking solutions are migrated in order to prevent the population from being too crowded with similar solutions. Usually at the end of the search, many optimal solutions are retrieved from the archive as well as the population. The experimental results show that our method can find multiple optimal solutions more efficiently compared to other methods and can be easily scaled up to larger problems.