Optimization arises everywhere in industrial and engineering fields, with complex and time-consuming problems to be solved. Exact search techniques cannot afford practical solutions for most of the real-life problems in reasonable time-bound. Metaheuristics proved to be numerically efficient solvers for such problems in terms of solution quality, however, they could require large time and energy to get the optimal solution. Parallelization (i.e., distributed) is a promising approach for overcoming the overwhelming energy and time consumption values of these methods. Despite recent approaches in running metaheuristics in parallel, the community still lacks for novel studies comparing and benchmarking the canonical optimization techniques while being running in parallel. In this work, we present two extensive studies to the solution quality, energy consumption, and execution time for three different metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their distributed counterparts. The main aim of our studies is exploring the efficiency of parallel execution of the metaheuristics while being running in new computing environments. Here, we want to identify the combinatorics between metaheuristics and solving optimization problems while being run in parallel. For our studies, we consider a multicore machine with 32 cores. This choice to a recent and commonly used system shall enrich the existing literature for multicore systems against the enormous existing studies over cluster systems. The analyses and discussions for the results of the different algorithms exhibit the combinatorics between the different metaheuristics and the parallel execution using a different number of cores. The outcome of these studies builds a guide for future designs of efficient and energy-aware optimization techniques.

在工业和工程领域，优化无处不在，需要解决复杂而耗时的问题。精确的搜索技术无法在合理的时间范围内为大多数现实生活中的问题提供实用的解决方案。在解决方案质量方面，元启发法被证明是解决此类问题的有效方法，但是，它们可能需要大量时间和精力才能获得最佳解决方案。并行化（即分布式）是克服这些方法的压倒性的能量和时间消耗值的有前途的方法。尽管最近有一些方法可以并行运行元启发式算法，但社区仍然缺乏新颖的研究，可以在并行运行时对规范优化技术进行比较和基准测试。在这项工作中，我们针对解决方案质量进行了两项广泛的研究，三种不同的元启发式算法（遗传算法，可变邻域搜索和模拟退火）及其分布式副本的能耗，执行时间。我们研究的主要目的是探索在新的计算环境中运行时并行执行元启发式算法的效率。在这里，我们要确定元启发法和并行运行时解决优化问题之间的组合。对于我们的研究，我们考虑使用32核的多核计算机。相对于对集群系统的大量现有研究，对于最近且常用的系统的选择将丰富多核系统的现有文献。对不同算法结果的分析和讨论显示了不同元启发式算法和使用不同核数的并行执行之间的组合。这些研究的结果为将来的高效节能意识优化技术设计提供了指南。