中文摘要：

Optimization of architecture design has recently drawn research interest. System deployment optimization(SDO) refers to the process of optimizing systems that are being deployed to activities. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal solutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are minimized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm(HGA), as well as the combination of heuristic method and particle swarm optimization(HPSO). Experiment results show that the HPSO method generates solutions faster than particle swarm optimization(PSO) and genetic algorithm(GA) in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.

英文摘要：

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.