CAJ | 학술논문

为了使钻进过程达到最优，提出了基于机械钻速、钻头寿命和钻头比能的钻进参数多目标优化模型。参考典型的多目标优化进化算法NSGA-Ⅱ，提出了一种多目标粒子群算法（MOPSO）。采用一个钻进参数优化实例对优化模型和算法进行检验，得到分布均匀的Pareto最优解，一些最优解与传统的钻进参数单目标优化的解近似；讨论了算法中的种群规模、迭代次数和外部档案规模三个参数，得到一组兼顾解质量和计算时间的参数值，其计算时间的统计结果证明模型和算法满足钻进参数动态优化的要求。
위료사찬진과정체도최우，제출료기우궤계찬속、찬두수명화찬두비능적찬진삼수다목표우화모형。삼고전형적다목표우화진화산법NSGA-Ⅱ，제출료일충다목표입자군산법（MOPSO）。채용일개찬진삼수우화실례대우화모형화산법진행검험，득도분포균균적Pareto최우해，일사최우해여전통적찬진삼수단목표우화적해근사；토론료산법중적충군규모、질대차수화외부당안규모삼개삼수，득도일조겸고해질량화계산시간적삼수치，기계산시간적통계결과증명모형화산법만족찬진삼수동태우화적요구。
In order to make the drilling process optimal,a multi-objective optimization model of drilling parameters based on the penetration rate,the bit life and the mechanical specific energy of bit is proposed. Inspired by a classic multi-objective evolution optimization algorithm NSGA-II,a multi-objective particle swarm optimization (MOPSO) algorithm is proposed. The optimized model and algorithm were verified with a real example whose drilling parameters were optimized. The evenly distrib-uted Pareto optimal solutions were obtained,some of which are similar to the solutions of the traditional single-objective optimi-zation of drilling parameter. Three parameters of population scale,iteration number and external population scale in the algo-rithm are discussed. A group of parameter values which balance the solution quality and the computing time are chosen. The sta-tistical result of computing time proves that the model and the algorithm meet the need of the dynamic optimization of drilling pa-rameters.