Parallel Whale Optimization Algorithm for Solving Constrained and Unconstrained Optimization Problems
This work addresses computational efficiency for engineering optimization problems, but it is incremental as it applies an existing method (parallelization) to a known algorithm.
The paper tackled the problem of high computational demands in engineering optimization by proposing a parallel version of the whale optimization algorithm (PWOA) using OpenMP, which achieved better performance metrics like speedup and efficiency compared to the sequential version while maintaining the same results.
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.