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Implementation Techniques for Massively Parallel Multi-objective Optimization

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Massively Parallel Evolutionary Computation on GPGPUs

Part of the book series: Natural Computing Series ((NCS))

Abstract

Most real-world search and optimization problems involve more than one goal. The task of finding multiple optimal solutions for such problems is known as multi-objective optimization (MOO). It has been a challenge for researchers and practitioners to find solutions for MOO problems. Many techniques have been developed in operations research and other related disciplines, but the complexity of MOO problems such as large search spaces, uncertainty, noise, and disjoint Pareto curves may prevent the use of these techniques. At this stage, evolutionary algorithms (EAs) are preferred and have been extensively used for the last two decades. The main reason is that EAs are population-based techniques which can evolve a Pareto front in one run. But EAs require a relatively large computation time. A remedy is to perform function evaluations in parallel. But another bottleneck is Pareto ranking in multi-objective evolutionary algorithms. In this chapter, a brief introduction to MOO and techniques used to solve MOO problems is given. Thereafter, a GPGPU-based archive stochastic ranking evolutionary algorithm is discussed that can perform function evaluations and ranking on a massively parallel GPU card. Results are compared with CPU and GPU versions of NSGA-II.

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Notes

  1. 1.

    Source codes for NSGA-II and SPEA2 are available at [16] and [25].

  2. 2.

    Note that only one run is performed for 1 million individuals on each ZDT function.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India

    Deepak Sharma

  2. ICUBE, Université de Strasbourg, Illkirch, France

    Pierre Collet

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  1. Deepak Sharma
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  2. Pierre Collet
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Correspondence to Deepak Sharma .

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Editors and Affiliations

  1. and Information Science, Hannan University Dept. of Management, Matsubara, Osaka, Japan

    Shigeyoshi Tsutsui

  2. ICube Laboratory UMR CNRS 7357, Université de Strasbourg, Illkirch, France

    Pierre Collet

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Sharma, D., Collet, P. (2013). Implementation Techniques for Massively Parallel Multi-objective Optimization. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_13

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  • DOI: https://doi.org/10.1007/978-3-642-37959-8_13

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