Multi-Working Mode Product Color Planning Using Evolutionary Algorithm and Swarm Intelligence

In this paper, in order to assist designer in color planning during product development, an efficient synthesized evaluation model is presented to evaluate color-combination schemes of multi-working modes products (MMP). The structure in this evaluation model is a simple linear combiner, in which color-combination images in different working modes are considered as evaluating attributes with the corresponding weights. In order to solve the proposed model effectively, this work applies two powerful artificial intelligence optimization techniques known as evolutionary algorithm (EA) and swarm intelligence (SI). Experiments present a comparative study on the color planning problem for one EA, namely the genetic algorithm (GA), and one SI algorithm, namely the particle swarm optimization (PSO). Both algorithms are evaluated against one MMP test scenario, namely the arm-type aerial work platform, which is a two-colored product that has three working modes. Simulation results demonstrate that the proposed method is feasible and efficient.