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International Conference on Agents and Artificial Intelligence

ICAART 2019: Agents and Artificial Intelligence pp 317–337Cite as

The Effect of Laziness on Agents for Large Scale Global Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11978))

Abstract

Practical optimization problems from engineering often suffer from non-convexity and rugged, multi-modal fitness or error landscapes and are thus hard to solve. Especially in the high-dimensional case, a lack of derivatives entails additional challenges to heuristics. High-dimensionality leads to an exponential increase in search space size and tightens the problem of premature convergence. Parallelization for acceleration often involves domain-specific knowledge for data domain partition or functional or algorithmic decomposition. On the other hand, fully decentralized agent-based procedures for global optimization based on coordinate descent and gossiping have no specific decomposition needs and can thus be applied to arbitrary optimization problems. Premature convergence can be mitigated by introducing laziness. We scrutinized the effectiveness of different levels of laziness on different types of optimization problems and for the first time applied the approach to a real-world optimization problem: to predictive scheduling in virtual power plant orchestration. The lazy agent approach turns out to be competitive and often superior to the non-lazy one and to standard heuristics in many cases including the real world problem.

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  1. University of Oldenburg, 26129, Oldenburg, Germany

    Jörg Bremer & Sebastian Lehnhoff

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  1. Jörg Bremer
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  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  3. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

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Bremer, J., Lehnhoff, S. (2019). The Effect of Laziness on Agents for Large Scale Global Optimization. In: van den Herik, J., Rocha, A., Steels, L. (eds) Agents and Artificial Intelligence. ICAART 2019. Lecture Notes in Computer Science(), vol 11978. Springer, Cham. https://doi.org/10.1007/978-3-030-37494-5_16

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