Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony

This paper proposes a parallel asynchronous evolutionary algorithm (EA) with different asynchrony and verifies its effectiveness on multi-objective optimization problems. We represent such EA with different asynchrony as semi-asynchronous EA. The semi-asynchronous EA continuously evolves solutions whenever a part of solutions in the population completes their evaluations in the master-slave parallel computation environment, unlike a conventional synchronous EA, which waits for evaluations of all solutions to generate next population. To establish the semi-asynchronous EA, this paper proposes the asynchrony parameter to decide how many solutions are waited, and clarifies the effectual asynchrony related to the number of slave nodes. In the experiment, we apply the semi-asynchronous EA to NSGA-II, which is a well-known multi-objective evolutionary algorithm, and the semi-asynchronous NSGA-IIs with different asynchrony are compared with synchronous one on the multi-objective optimization benchmark problems with several variances of evaluation time. The experimental result reveals that the semi-asynchronous NSGA-II with low asynchrony has possibility to perform the best search ability than the complete asynchronous and the synchronous NSGA-II in the optimization problems with large variance of evaluation time.

[1]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[2]  Antonio J. Nebro,et al.  Redesigning the jMetal Multi-Objective Optimization Framework , 2015, GECCO.

[3]  R. Lyndon While,et al.  A Scalable Multi-objective Test Problem Toolkit , 2005, EMO.

[4]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[5]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[6]  Alfredo Milani,et al.  Asynchronous Differential Evolution , 2010, IEEE Congress on Evolutionary Computation.

[7]  Kenneth A. De Jong,et al.  Understanding Simple Asynchronous Evolutionary Algorithms , 2015, FOGA.

[8]  Kenneth A. De Jong,et al.  Evaluation-Time Bias in Asynchronous Evolutionary Algorithms , 2015, GECCO.

[9]  Shigeru Obayashi,et al.  Multi-Objective Design Exploration and its Applications , 2010 .

[10]  Peter J. Fleming,et al.  Parallel Genetic Algorithms: A Survey , 1994 .

[11]  Miguel A. Vega-Rodríguez,et al.  Asynchronous Non-Generational Model to Parallelize Metaheuristics: A Bioinformatics Case Study , 2017, IEEE Transactions on Parallel and Distributed Systems.

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

[13]  Andrew Lewis,et al.  Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments , 2009 .

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

[15]  Sidney R. Maxwell,et al.  Experiments with a coroutine execution model for genetic programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[16]  Matjaz Depolli,et al.  Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization , 2013, Evolutionary Computation.

[17]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[18]  Jeng-Shyang Pan,et al.  A Parallel Particle Swarm Optimization Algorithm with Communication Strategies , 2005, J. Inf. Sci. Eng..

[19]  Mikhail Zhabitsky,et al.  Asynchronous Differential Evolution with Restart , 2012, NAA.

[20]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[21]  Miguel A. Vega-Rodríguez,et al.  Performance evaluation of dominance-based and indicator-based multiobjective approaches for phylogenetic inference , 2016, Inf. Sci..

[22]  Qingfu Zhang,et al.  Distribution of Computational Effort in Parallel MOEA/D , 2011, LION.

[23]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

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

[25]  Byung-Il Koh,et al.  Parallel asynchronous particle swarm optimization , 2006, International journal for numerical methods in engineering.

[26]  Edwin Lughofer,et al.  Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems , 2015, Knowl. Based Syst..