求解约束和无约束优化问题的并行Whale优化算法
Parallel Whale Optimization Algorithm for Solving Constrained and Unconstrained Optimization Problems
摘要：
近年来，工程优化问题的计算量大，求解时间长，即使在高多处理器计算设备上。在本文中，一个受OpenMP启发的并行版本的鲸鱼优化算法（PWOA），以获得增强的计算吞吐量和全局搜索能力。它自动检测可用处理器的数量，并在它们之间划分工作负载，以实现可用资源的有效利用。PWOA被应用于20个多维无约束优化函数和5个约束优化工程函数。所提出的并行PWOA算法的性能进行评估，使用并行指标，如加速比，效率。比较结果表明，所提出的PWOA算法获得了相同的结果，而在性能上超过了顺序版本。与标准WOA算法相比，PWOA算法在计算时间和并行度量速度方面都取得了优于顺序处理的结果。
英文摘要：
Recently the engineering optimization problems require large computational demands and long solution time even on high multi-processors computational devices. In this paper, an OpenMP inspired parallel version of the whale optimization algorithm (PWOA) to obtain enhanced computational throughput and global search capability is presented. It automatically detects the number of available processors and divides the workload among them to accomplish the effective utilization of the available resources. PWOA is applied on twenty unconstrained optimization functions on multiple dimensions and five constrained optimization engineering functions. The proposed parallelism PWOA algorithms performance is evaluated using parallel metrics such as speedup, efficiency. The comparison illustrates that the proposed PWOA algorithm has obtained the same results while exceeding the sequential version in performance. Furthermore, PWOA algorithm in the term of computational time and speed of parallel metric was achieved better results over the sequential processing compared to the standard WOA.
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