A Parallel General Purpose Multi-Objective Optimization Framework, with Application to Beam Dynamics

Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame a fast and scalable software framework is required. In this paper, we present the implementation of such a general-purpose framework for simulation-based multi-objective optimization methods that allows the automatic investigation of optimal sets of machine parameters. The implementation is based on a master/slave paradigm, employing several masters that govern a set of slaves executing simulations and performing optimization tasks. Using evolutionary algorithms as the optimizer and OPAL as the forward solver, validation experiments and results of multi-objective optimization problems in the domain of beam dynamics are presented. The high charge beam line at the Argonne Wakefield Accelerator Facility was used as the beam dynamics model. The 3D beam size, transverse momentum, and energy spread were optimized

A Hardware Implementation Method of Multi-Objective Genetic Algorithms

CEC2006 : IEEE International Conference on Evolutionary Computation , Jul 16-21, 2006 , Vancouver, BC, CanadaMulti-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