A Parallel Island Model for Hypervolume-Based Many-Objective Optimization

Parallelism arises as an attractive option when Multi-Objective Evolutionary Algorithms (MOEAs) demand an intensive use of CPU or memory. The computational complexity of a MOEA depends on the scalability of its input parameters (i.e., the population size, number of decision variables, objectives, etc.) and on the computational cost of evaluating the objectives of the problem. Nonetheless, current research efforts have focused only on the second case. Therefore, in this chapter, we investigate the performance and behavior of S-PAMICRO, a recently proposed parallelization of SMS-EMOA that inhibits exponential execution time as the number of objectives increases. The idea behind S-PAMICRO is to divide the overall population into several semi-independent subpopulations each of which has very few individuals. Each subpopulation evolves a serial SMS-EMOA with an external archive for maintaining diversity. Our experimental results show that S-PAMICRO outperforms the standard island version of some state-of-the-art MOEAs in most instances of the many-objective Deb-Thiele-Laumanns-Zitzler (DTLZ) and Walking-Fish-Group (WFG) test suites.

[1]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[2]  Enrique Alba,et al.  A Multi-Objective Evolutionary Algorithm based on Parallel Coordinates , 2016, GECCO.

[3]  Günter Rudolph,et al.  Comparing Asynchronous and Synchronous Parallelization of the SMS-EMOA , 2016, PPSN.

[4]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[5]  Alfred Inselberg,et al.  Parallel Coordinates: Visual Multidimensional Geometry and Its Applications , 2003, KDIR.

[6]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[7]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[8]  Günter Rudolph,et al.  Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets , 1998, Evolutionary Programming.

[9]  Ofer M. Shir,et al.  A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization , 2008, MCDM.

[10]  R. E. Carlson,et al.  Monotone Piecewise Cubic Interpolation , 1980 .

[11]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[12]  Enrique Alba,et al.  A Parallel Version of SMS-EMOA for Many-Objective Optimization Problems , 2016, PPSN.

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[15]  Mark Fleischer,et al.  The measure of pareto optima: Applications to multi-objective metaheuristics , 2003 .

[16]  Carlos A. Brizuela,et al.  A survey on multi-objective evolutionary algorithms for many-objective problems , 2014, Comput. Optim. Appl..

[17]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[18]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Heike Trautmann,et al.  On the properties of the R2 indicator , 2012, GECCO '12.

[20]  Hisao Ishibuchi,et al.  Modified Distance Calculation in Generational Distance and Inverted Generational Distance , 2015, EMO.

[21]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .