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A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization

机译：基于分段线性混沌映射和顺序二次规划的鲁棒混合粒子群算法

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摘要

This paper presents a novel robust hybrid particle swarm optimization (RHPSO) based on piecewise linear chaotic map (PWLCM) and sequential quadratic programming (SQP). The aim of the present research is to develop a new single-objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems. This novel algorithm makes the best of ergodicity of PWLCM to help PSO with the global search while employing the SQP to accelerate the local search. Five unconstrained benchmarks, eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach. The simulation results compared with other state-of-art methods demonstrate the effectiveness and robustness of the proposed RHPSO for both unconstrained and constrained problems of different dimensions.
机译：本文提出了一种基于分段线性混沌映射（PWLCM）和顺序二次规划（SQP）的新型鲁棒混合粒子群优化算法（RHPSO）。本研究的目的是开发一种新的单目标优化方法，该方法不需要针对无约束和有约束的优化问题都调整其参数。这种新颖的算法充分利用了PWLCM的遍历性，可以帮助PSO进行全局搜索，同时采用SQP来加速本地搜索。通过使用所提出的混合方法，解决了文献中的五个无约束基准，十八个约束基准和三个工程优化问题。仿真结果与其他最新方法相比，证明了所提出的RHPSO对于不同尺寸的无约束和受约束问题的有效性和鲁棒性。

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