Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
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DOI：
10.1016/j.advengsoft.2017.07.002
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发表时间：
2017-12-01
影响因子：
4.8
通讯作者：
Mirjalili, Seyed Mohammad
中科院分区：
文献类型：
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作者：
Mirjalili, Seyedali;Gandomi, Amir H.;Mirjalili, Seyed Mohammad
本文提出了两种新的优化算法Salp Swarm算法（SSA）和多目标Salp Swarm算法（MSSA），用于求解单目标和多目标优化问题。SSA和MSSA的主要灵感来自于海鞘在海洋中航行和觅食时的群集行为。这两种算法在几个数学优化函数上进行了测试，以观察和确认它们在寻找优化问题的最优解时的有效行为。数学函数的结果表明，SSA算法能够有效地改善初始随机解，并收敛到最优解。MSSA算例结果表明，该算法能够逼近Pareto最优解，具有较高的收敛性和覆盖率。本文还考虑解决一些具有挑战性和计算昂贵的工程设计问题（如翼型设计和船舶螺旋桨设计）使用SSA和MSSA。真实的案例研究的结果表明，所提出的算法在解决现实世界中的困难和未知的搜索空间的问题的优点。(C)2017爱思唯尔有限公司版权所有
This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multiobjective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces. (C) 2017 Elsevier Ltd. All rights reserved.