Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping

Some signal processing and multimedia applications can be specified by synchronous dataflow (SDF) models. The problem of SDF mapping to a given set of heterogeneous processors has been known to be NP-hard and widely studied in the design automation field. However, modern embedded applications are becoming increasingly complex with dynamic behaviors changes over time. As a significant extension to the SDF, the multi-mode dataflow (MMDF) model has been proposed to specify such an application with a finite number of behaviors (or modes) and each behavior (mode) is represented by an SDF graph. The multiprocessor mapping of an MMDF is far more challenging as the design space increases with the number of modes. Instead of using traditional genetic algorithm (GA)-based design space exploration (DSE) method that encodes the design space as a whole, this article proposes a novel cooperative co-evolutionary genetic algorithm (CCGA)-based framework to efficiently explore the design space by a new problem-specific decomposition strategy in which the solutions of node mapping for each individual mode are assigned to an individual population. Besides, a problem-specific local search operator is introduced as a supplement to the global search of CCGA for further improving the search efficiency of the whole framework. Furthermore, a fitness approximation method and a hybrid fitness evaluation strategy are applied for reducing the time consumption of fitness evaluation significantly. The experimental studies demonstrate the advantage of the proposed DSE method over the previous GA-based method. The proposed method can obtain an optimization result with 2×−3× better quality using less (1/2−1/3) optimization time.

A Comprehensive Study on Fitness Approximation Techniques in Shape Optimization of Aerofoil Forging Preform Tools

In the process of complex engineering designs or optimizations, a large number of physical experiments or numerical simulations are required to evaluate certain performance qualities before a satisfactory result can be obtained. In both cases, constructing an approximate model is often necessary to provide a reliable response as an alternative to experiments or simulations. In this paper, three types of approximation models were developed and applied in a shape design of an aerofoil forging preform tool. Their modeling techniques are presented in detail. An optimal Latin hypercube technique was employed for the design of the experiment and sampling with the expected coverage of parameter space. Finite element (FE) simulations of multistep forging processes were implemented to acquire the objective function values for evaluating the forging performance. By a parametric study, the effects of design variables on objective responses and correlations were investigated for a clear insight into their functional nature. Comprehensive analyses and comparisons between different approximate models have been carried out. Finally, an optimization design of a preform tool was successfully achieved based on a particle swarm (PSO) algorithm combined with the proposed approximate model.