An efficient multi-objective genetic algorithm for cloud computing: NSGA-G

Overview & Synopsis

Cloud computing provides computing resources with elasticity following a pay-as-you-go model. This raises Multi-Objective Optimization Problems (MOOP), in particular to find Query Execution Plans (QEPs) with respect to users' preferences being for example response time, money, quality, etc. In such a context, MOOP may generate Pareto-optimal front with high complexity. Pareto-dominated based Multi-objective Evolutionary Algorithms (MOEA) are often used as an alternative solution, like Non-dominated Sorting Genetic Algorithms (NSGAs) that provide better computational complexity. This paper presents NSGA-G, a NSGA based on Grid Partitioning for improving complexity and quality of current NSGAs. Experiments on DTLZ test problems using Generational Distance (GD), Inverted Generational Distance (IGD) and Maximum Pareto Front Error prove the relevance of our solution.