This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO) for the multi-dimensional numerical problems. The best practices of ABC and TLBO are implemented to provide robust algorithms on a distributed memory computation environment using MPI libraries. Island parallel versions of the proposed hybrid algorithm are observed to obtain much better results than those of sequential versions. Parallel pseudorandom number generators are used to provide diverse solution candidates to prevent stagnation into local optima. The performances of the proposed hybrid algorithms are compared with eight different metaheuristics algorithms of particle swarm optimization, differential evolution variants, ABC variants and evolutionary algorithm. The empirical results show that the new hybrid parallel algorithms are scalable and the best performing algorithms when compared to the state-of-the-art metaheuristics.

中文翻译：

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用于多维数值优化的鲁棒并行混合人工蜂群算法

本研究针对多维数值问题提出了一套基于人工蜂群（ABC）和基于教学优化（TLBO）的新的鲁棒并行混合元启发式算法。实现了 ABC 和 TLBO 的最佳实践，以在使用 MPI 库的分布式内存计算环境中提供稳健的算法。观察到所提出的混合算法的岛并行版本比顺序版本获得更好的结果。并行伪随机数生成器用于提供不同的候选解决方案，以防止陷入局部最优。将所提出的混合算法的性能与粒子群优化、差分进化变体、ABC变体和进化算法的八种不同元启发式算法进行了比较。