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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Grasshopper optimization algorithm for multi-objective optimization problems
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Grasshopper optimization algorithm for multi-objective optimization problems

机译：多目标优化问题的蚱蜢优化算法

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摘要

This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.
机译：这项工作提出了一种新的多目标算法，其自然界中的草船群导航启发。首先采用数学模型来模拟游泳池中的个体的相互作用，包括吸引力，排斥力和舒适区。然后提出一种机制来使用模型在单个客观搜索空间中近似全局最优。之后，归档和目标选择技术被集成到算法以估计帕累托最佳前端以进行多目标问题。为了基准提出的算法的性能，利用了一组不同的标准多目标测试问题。将结果与使用三种性能指示器定量和图形的进化多目标优化文献中最常见的多目标优化文献中的最新算法进行了比较。结果表明，该算法能够在获得Pareto最佳解决方案的准确性和其分布方面提供非常竞争力的结果。

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