Research On The Optimization Of Time-cost Based On Hybrid Genetic Algorithm

It can expand the space of available profits and bring considerable economic benefits to the project by reasonable arrangements for schedule and cost-cutting in the project management. Traditional time-cost optimization methods such as mathematical programming commonly and heuristic algorithms have some defects. Owing to the characteristics of high computational efficiency and excellent results, hybrid algorithms cause the concern of scholars in recent years, and achieved good results in many areas. Focus on some problems existing in the conventional methods on the basis of previous studies, this paper uses the unique advantages of hybrid genetic algorithm in solving double target combination optimization problems. This method is applied to the time-cost optimization of network plan by combining hybrid genetic algorithm and ant colony algorithm. While optimizing the time and cost objectives at the same time, the hybrid cross-linking work between the two algorithms can be done using genetic algorithm’s advantage of wide-ranged search and positive feed back high calculated efficiency of ant colony algorithm. For time-cost optimization problems in which it is continuous relationship between time and cost of activaty, our research focus on this situation. By analyzing and comparing the results of hybrid algorithm with the traditional algorithm and ant colony algorithm, it can be found that the hybrid algorithm used in finding the critical path has higher efficiency and more reasonable and accurate in obtaining results duration. To a certain extent, it proves the practical feasibility of the mathematical optimization model and provides a new thinkingway and problem-solving method for time-cost optimization.