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Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies
Mathematics and Computers in Simulation ( IF 4.6 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.matcom.2020.09.027
Hao Ren , Jun Li , Huiling Chen , ChenYang Li

Abstract The salp swarm algorithm (SSA) is a recent and straightforward swarm intelligent optimizer. It mainly simulates the foraging and navigational behavior of salp in the ocean by forming a salp chain. The salp in the front of the chain guides the moving direction of the population, which makes the algorithm easy to fall into local optimum and lead to premature convergence. In order to tackle this shortcoming, an improved SSA integrated with adaptive weight and levy flight mechanism is proposed, which is called WLSSA. In this research, the adaptive weight is proposed to extend the exploratory scope of conventional SSA throughout the early stages and speeds up the convergence swiftness of the method in the later stages. By random walk of levy flight to explore the solution space, the global exploratory and local exploitation capabilities of the algorithm are more well-adjusted and enhanced. Under the cooperation and concurrent influence of the two mechanisms, the overall performance of the algorithm is significantly boosted in terms of the excellence of solutions. Twenty-three essential classical functions and selected IEEE CEC 2014 test functions are utilized to validate the effectiveness of the proposed WLSSA and compare and analyze the optimization capacity of WLSSA versus six mainstream meta-heuristic algorithms and eight improved advanced algorithms in solving function optimization problems. The results of the test cases confirm the significant improvements of the proposed SSA-based algorithm over the original SSA, and it also shows strong competitiveness compared to the associated technique. Also, to study the potential of WLSSA in treating practical problems in the real world, three constrained engineering cases are considered. Similarly, the comparison results reveal that it is possible to find a better solution using the proposed WLSSA to the same problem compared to the existing methods.

中文翻译:

自适应征税辅助 Salp 群算法:分析和优化案例研究

摘要 Salp swarm 算法(SSA)是一种最新的、简单的swarm 智能优化器。它主要通过形成salp链来模拟salp在海洋中的觅食和航行行为。链前的salp引导种群的运动方向,使算法容易陷入局部最优,导致早熟收敛。为了解决这个缺点,提出了一种集成了自适应权重和征税飞行机制的改进 SSA,称为 WLSSA。在本研究中,提出自适应权重以在整个早期阶段扩展常规 SSA 的探索范围,并加快该方法在后期阶段的收敛速度。通过随机游走征飞行探索解空间,算法的全局探索和局部开发能力得到更好的调整和增强。在两种机制的协同和并发影响下,算法的整体性能在解的卓越性方面得到显着提升。利用二十三个基本经典函数和选定的 IEEE CEC 2014 测试函数来验证所提出的 WLSSA 的有效性,并比较和分析 WLSSA 与六种主流元启发式算法和八种改进的高级算法在解决函数优化问题方面的优化能力。测试用例的结果证实了所提出的基于 SSA 的算法对原始 SSA 的显着改进,并且与相关技术相比,它还显示出强大的竞争力。还,为了研究 WLSSA 在处理现实世界中的实际问题方面的潜力,我们考虑了三个受约束的工程案例。同样,比较结果表明,与现有方法相比,使用所提出的 WLSSA 可以找到更好的解决方案来解决相同的问题。
更新日期:2021-03-01
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