This article presents two new hybrid swarm-human based meta-heuristic optimization algorithms benefitting from the synergy of whale optimization algorithm (WOA) and social group optimization (SGO) known as Hybrid Social Whale Optimization Algorithm (HS-WOA and HS-WOA+). HS-WOA and HS-WOA+ are hybridized combining the exploratory capabilities of WOA and convergence capabilities of SGO with a perfect balance between exploration and exploitation. A comparative analysis of the new proposed hybrid algorithm is performed through various benchmark functions. Various test cases to analyze the algorithm’s performance like influence of population size, effect of dimensionality, effect of iterative count is performed and compared. The proposed algorithms are compared with modern-meta-heuristics and variants of WOA and SGO to justify its performance. The performance is evaluated statistically through the Wilcoxon’s rank-sum test and Friedman’s non-parametric test while the convergence curves and acceleration rates are provided to demonstrate the convergence capabilities of the proposed hybrid algorithms and the computational times are recorded to showcase the computational speeds of all the algorithms used in comparison. Composite benchmarking functions are considered to analyze the exploratory prowess and the algorithms’ capability to avoid local entrapment. To assess and evaluate the performance of the proposed algorithms with real world optimization tasks, four standard engineering problems with penalty constraints are added to the test bench. Further, a multi-unit production planning problem with correction constraints is deployed through the proposed algorithms. The benchmarking results prove that HS-WOA and HS-WOA+ s’ performance is competitive and better than the various algorithms tested against and had a statistically significant performance with lower computational times. The algorithms performed well for both standard engineering problems and the multi-unit production planning problem outperforming the various algorithms in the literature.

本文介绍了两种新的基于混合群人的元启发式优化算法，这些算法得益于鲸鱼优化算法（WOA）和社交群体优化（SGO）的协同作用，称为混合社交鲸鱼优化算法（HS-WOA和HS-WOA +）。HS-WOA和HS-WOA +融合了WOA的探索能力和SGO的融合能力，在勘探与开发之间达到了完美的平衡。通过各种基准功能对新提出的混合算法进行了比较分析。执行并比较了各种测试用例，以分析算法的性能，例如总体大小的影响，维数的影响，迭代计数的影响。将提出的算法与现代元启发式算法以及WOA和SGO的变体进行比较，以证明其性能合理。通过Wilcoxon的秩和检验和Friedman的非参数检验对性能进行统计评估，同时提供收敛曲线和加速速率以证明所提出的混合算法的收敛能力，并记录计算时间以展示所有算法的计算速度比较中使用的算法。考虑使用复合基准测试功能来分析探索能力和算法避免局部陷入的能力。为了评估和评估所提出算法在现实世界中的优化任务的性能，在试验台上增加了四个带有惩罚约束的标准工程问题。此外，通过提出的算法部署了具有修正约束的多单元生产计划问题。基准测试结果证明，HS-WOA和HS-WOA +的性能具有竞争力，并且比所测试的各种算法更好，并且具有统计上显着的性能，且计算时间较短。该算法在标准工程问题和多单元生产计划问题上均表现出色，优于文献中的各种算法。