这项研究发展了一个新的元启发式算法，它是受水母在海洋中的行为启发，被称为人工水母搜索（JS）优化器。
This study develops a novel metaheuristic algorithm that is inspired by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer.
水母搜索行为的模拟包括它们跟随洋流、它们在水母群中的运动（主动运动和被动运动）、在这些运动之间切换的时间控制机制，以及收敛到水母花的状态。
The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom.
新算法在基准函数和优化问题上得到了成功的测试。
The new algorithm is successfully tested on benchmark functions and optimization problems.
值得注意的是，JS只有两个控制参数，即群体规模和迭代次数。
Notably, JS has only two control parameters, which are population size and number of iterations.
因此，JS的使用非常简单，并且可能是解决优化问题的一个优秀的元启发式算法。
Therefore, JS is very simple to use, and potentially an excellent metaheuristic algorithm for solving optimization problems.
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