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Parallel execution combinatorics with metaheuristics: Comparative study
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AI
DOI：10.1016/j.swevo.2020.100692.png)
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Abstract
En 中文
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.
Keywords:
Parallel
Sequential
Metaheuristics
Optimization
Energy
Genetic algorithm
Variable neighborhood search
Simulated annealing
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