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首页> 外文期刊>Arabian Journal for Science and Engineering >Hybrid Particle Swarm Optimization with Sine Cosine Algorithm and Nelder–Mead Simplex for Solving Engineering Design Problems
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Hybrid Particle Swarm Optimization with Sine Cosine Algorithm and Nelder–Mead Simplex for Solving Engineering Design Problems

机译：基于正弦余弦算法和Nelder-Mead单纯形的混合粒子群优化解决工程设计问题

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This paper introduces a novel hybrid evolutionary algorithm that combines particle swarm optimization (PSO) algorithmwith sine–cosine algorithm (SCA) and Nelder–Mead simplex (NMS) optimization technique. However, the algorithm ofPSO has some drawbacks like locating local minima rather than global minima, low converge rate and low balance betweenexploration and exploitation. In this paper, the combination of PSO algorithm with update positions mathematical equationin SCA and NMS technique is presented in order to solve these problems. So a new hybrid strategy called PSOSCANMSis introduced. The SCA algorithm is based on the behavior of sine and cosine functions in the mathematical formula usedfor solutions. However, the NMS mathematical formulations attempt to replace the worst vertex with a new point, whichdepends on the worst point and the center of the best vertices. The combined effect of both mathematical formulations ofPSO ensures a consistency of exploitation and exploration that makes the search in the search space more effective. Further,it escapes into the local minimum issue and resolves the low converge rate problem. In order to test PSOSCANMS’s performance,a set of 23 well-known unimodal and multimodal functions have been benchmarked. Experimental results showedthat PSOSCANMS is more successful than PSO and outperforms the other state-of-the-art compared algorithms over thetested optimization problems. Moreover, an engineering design problem such as spring compression, welded beam is alsoconsidered. The result of the problems in engineering design and application problems shows that the algorithm proposedis relevant in difficult cases involving unknown search areas.
机译：本文介绍了一种新颖的混合进化算法，该算法结合了粒子群优化（PSO）算法和正弦余弦算法（SCA）和Nelder-Mead单形（NMS）优化技术。但是，PSO算法具有定位局部最小值而不是全局最小值，收敛速度慢，探索与开发之间的平衡低等缺点。为了解决这些问题，本文提出了PSO算法与SCA中更新位置数学方程的结合以及NMS技术。因此，引入了一种称为PSOSCANMS的新混合策略。 SCA算法基于解决方案所用数学公式中正弦和余弦函数的行为。但是，NMS数学公式试图用新点替换最坏的顶点，该点取决于最坏点和最佳顶点的中心。 PSO两种数学公式的组合效果确保了开发和探索的一致性，从而使搜索空间中的搜索更加有效。此外，它逃脱到局部最小问题中并解决了低收敛速率问题。为了测试PSOSCANMS的性能，已经对23种著名的单峰和多峰函数进行了基准测试。实验结果表明，在经过测试的优化问题上，PSOSCANMS比PSO更成功，并且优于其他最新比较算法。此外，还考虑了诸如弹簧压缩，焊接梁的工程设计问题。工程设计问题和应用问题的结果表明，所提出的算法在涉及未知搜索区域的困难情况下是相关的。

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