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Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems

机译：实数编码遗传算法求解约束优化问题的改进分析及应用

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An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. First, a sorting grouping selection method is given with the advantage of easy realization and not needing to calculate the fitness value. Secondly, a heuristic normal distribution crossover (HNDX) operator is proposed. It can guarantee the cross-generated offsprings to locate closer to the better one among the two parents and the crossover direction to be very close to the optimal crossover direction or to be consistent with the optimal crossover direction. In this way, HNDX can ensure that there is a great chance of generating better offsprings. Thirdly, since the GA in the existing literature has many iterations, the same individuals are likely to appear in the population, thereby making the diversity of the population worse. In IRCGA, substitution operation is added after the crossover operation so that the population does not have the same individuals, and the diversity of the population is rich, thereby helping avoid premature convergence. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, this paper proposes a combinational mutation method, which makes the mutation operation take into account both local search and global search. The computational results with nine examples show that the IRCGA has fast convergence speed. As an example application, the optimization model of the steering mechanism of vehicles is formulated and the IRCGA is used to optimize the parameters of the steering trapezoidal mechanism of three vehicle types, with better results than the other methods used.
机译：提出了一种改进的实编码遗传算法（IRCGA）来解决约束优化问题。首先，给出一种分类分组选择方法，该方法易于实现，不需要计算适应度值。其次，提出了一种启发式正态分布交叉算子。它可以保证杂交产生的后代在两个亲本中的位置更靠近亲本，并且杂交方向非常接近最佳杂交方向或与最佳杂交方向一致。这样，HNDX可以确保有很大的机会产生更好的后代。第三，由于现有文献中的遗传算法有很多迭代，因此同一个体很可能出现在种群中，从而使种群的多样性更加恶化。在IRCGA中，在交叉操作之后添加了替换操作，以使总体上没有相同的个体，并且群体的多样性丰富，从而有助于避免过早收敛。最后，针对单个变异算子不能同时兼顾局部搜索和全局搜索的缺点，提出了一种组合变异方法，使变异操作同时考虑局部搜索和全局搜索。九个例子的计算结果表明，IRCGA的收敛速度较快。作为示例应用，制定了车辆转向机构的优化模型，并使用IRCGA优化了三种车辆类型的转向梯形机构的参数，其效果优于其他方法。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|5760841.1-5760841.16|共16页
  • 作者

    Wang Jiquan; Cheng Zhiwen; Ersoy Okan K.; Zhang Panli; Dai Weiting; Dong Zhigui;

  • 作者单位

    Northeast Agr Univ, Coll Engn, Harbin 150030, Heilongjiang, Peoples R China;

    Northeast Agr Univ, Coll Engn, Harbin 150030, Heilongjiang, Peoples R China;

    Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA;

    Northeast Agr Univ, Coll Engn, Harbin 150030, Heilongjiang, Peoples R China;

    Northeast Agr Univ, Coll Engn, Harbin 150030, Heilongjiang, Peoples R China;

    Northeast Agr Univ, Coll Engn, Harbin 150030, Heilongjiang, Peoples R China;

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