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首页> 外文期刊>Decision Science Letters >A hybrid of genetic algorithm and Fletcher-Reeves for bound constrained optimization problems
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A hybrid of genetic algorithm and Fletcher-Reeves for bound constrained optimization problems

机译：遗传算法与Fletcher-Reeves的混合求解约束约束优化问题

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In this paper a hybrid algorithm for solving bound constrained optimization problems having continuously differentiable objective functions using Fletcher Reeves method and advanced Genetic Algorithm (GA) have been proposed. In this approach, GA with advanced operators has been applied for computing the step length in the feasible direction in each iteration of Fletcher Reeves method. Then this idea has been extended to a set of multi-point approximations instead of single point approximation to avoid the convergence of the existing method at local optimum and a new method, called population based Fletcher Reeves method, has been proposed to find the global or nearer to global optimum. Finally to study the performance of the proposed method, several multi-dimensional standard test functions having continuous partial derivatives have been solved. The results have been compared with the same of recently developed hybrid algorithm with respect to different comparative factors.
机译：在本文中，提出了一种混合算法，该算法使用Fletcher Reeves方法和高级遗传算法（GA）解决具有连续可微分目标函数的约束约束优化问题。在这种方法中，具有高级算子的GA已被应用于计算Fletcher Reeves方法的每次迭代中可行方向上的步长。然后，该思想已扩展为一组多点逼近而不是单点逼近，以避免现有方法在局部最优条件下收敛，并且提出了一种新的方法，称为基于总体的Fletcher Reeves方法，以求全局或全局最优解。接近全局最优。最后，为了研究该方法的性能，解决了具有连续偏导数的几个多维标准测试函数。对于不同的比较因素，将结果与最近开发的混合算法进行了比较。

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