A Hardware Implementation Method of Multi-Objective Genetic Algorithms

Abstract

Multi-Objective Genetic Algorithms (MOGAs) are approximation techniques to solve multi-objective optimization problems. Since MOGAs search a wide variety of pareto optimal solutions at the same time, MOGAs require large computation power. In order to solve practical sizes of the multi objective optimization problems, it is desirable to design and develop a hardware implementation method for MOGAs with high search efficiency and calculation speed. In this paper, we propose a new method to easily implement MOGAs as high performance hardware circuits. In the proposed method, we adopt simple Minimal Generation Gap (MGG) model as the generation model, because it is easy to be pipelined. In order to preserve diversity of individuals, we need a special selection mechanism such as the niching method which takes large computation time to repeatedly compare superiority among all individuals in the population. In the proposed method, we developed a new selection mechanism which greatly reduces the number of comparisons among individuals, keeping diversity of individuals. Our method also includes a parallel execution architecture based on Island GA which is scalable to the number of concurrent pipelines and effective to keep diversity of individuals. We applied our method to multi-objective Knapsack Problem. As a result, we confirmed that our method has higher search efficiency than existing method.

Fast Tuning of the PID Controller in An HVAC System Using the Big Bang–Big Crunch Algorithm and FPGA Technology

“…Several FPGA-based implementations of evolutionary algorithms have been presented in the literature to accelerate the optimization process. Most of these approaches focus on the optimal design of hardware accelerators to achieve high performance and flexibility [25,26] and satisfy other genetic algorithm features, such as multi-objective optimization [27]. In our approach, we adopted the BB-BC optimization algorithm due to its low computational time and high global convergence speed.…”