Multi-Objective Probability Collectives

Waldock, Antony; Corne, David

doi:10.1007/978-3-642-12239-2_48

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Recently, the use of surrogate models has become prominent for such applications [22,23]. Of the methods we found in the literature, those described by Suman and co-workers [15,16] and by Waldock and Corne [18] most closely resemble ours. We differ from these approaches in part by using surrogates.…”

Section: Introductionsupporting

confidence: 66%

“…Multi-objective optimization is a large active area of engineering research, spanning diverse fields. A variety of techniques exist, examples of which are given in [11][12][13][14][15][16][17][18][19][20][21]. In most cases, the datasets in these applications are much larger than those produced here, and the various methods differ in the manner in which these data are searched for the optimal set.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers

Mannodi-Kanakkithodi

Pilania

Ramprasad

et al. 2016

Computational Materials Science

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We present two Monte Carlo algorithms to find the Pareto front of the chemical space of a class of dielectric polymers that is most interesting with respect to optimizing both the bandgap and dielectric constant. Starting with a dataset generated from density functional theory calculations, we used machine learning to construct surrogate models for the bandgaps and dielectric constants of all physically meaningful 4-block polymers (that is, polymer systems with a 4-block repeat unit). We parameterized these machine learning models in such a way that the surrogates built for the 4-block polymers were readily extendable to polymers beyond a 4-block repeat unit. By using translational invariance, chemical intuition, and domain knowledge, we were able to enumerate all possible 4, 6, and 8 block polymers and benchmark our Monte Carlo sampling of the chemical space against the exact enumeration of the surrogate predictions. We obtained exact agreement for the fronts of 4-block polymers and at least a 90% agreement for those of 6 and 8-block polymers. We present fronts for 10-block polymer that are not possible to obtain by direct enumeration. We note that our Monte Carlo methods also return polymers close to the predicted front and a measure of the closeness. Both quantities are useful information for the design and discovery of new polymers.

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Section: Introductionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers

Mannodi-Kanakkithodi

Pilania

Ramprasad

et al. 2016

Computational Materials Science

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“…Multi Objective Evolutionary Algorithms Based on Decomposition (MOEA/D) [14] was selected as the current state of the art in multi-objective optimisation, Speed-constrained Multi-objective PSO (SMPSO) [18] is used to provide a baseline algorithm from the Particle Swarm Optimisation (PSO) community, Non-dominated Sorting Genetic Algorithm II (NSGAII) [8] is used as a baseline Genetic Algorithm (GA) while Multi-Objective Probability Collectives (MOPC) [21] is a preliminary algorithm developed using a new approach based on Probability Collectives [11,2]. The implementations of MOEA/D, SMPSO and NSGAII have been taken from JMetal: A framework for Multi-Objective Optimisation 4 and the results presented are averaged over 30 independent runs which were limited to 200,000 evaluations (same as the single objective results).…”

Section: Multiple Objectivesmentioning

confidence: 99%

Multiple objective optimisation applied to route planning

Waldock

Corne

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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This paper presents an evaluation of the benefits of multiobjective optimisation algorithms, compared to single objective optimisation algorithms, when applied to the problem of planning a route over an unstructured environment, where a route has a number of objectives defined using real-world data sources.The paper firstly introduces the problem of planning a route over an unstructured environment (one where no predetermined set of possible routes exists) and identifies the data sources, Digital Terrain Elevation Data (DTED) and NASA Landsat Hyperspectral data, used to calculate the route objectives (time taken, exposure and fuel consumed). A number of different route planning problems are then used to compare the performance of two single-objective optimisation algorithms and a range of multi-objective optimisation algorithms selected from the literature.The experimental results show that the multi-objective optimisation algorithms result in significantly better routes than the single-objective optimisation algorithms and have the advantage of returning a set of routes that represent the trade-off between objectives. The MOEA/D and SMPSO algorithms are shown, in these experiments, to outperform the other multi-objective optimisation algorithms for this type of problem. Future work will focus on how these algorithms can be integrated into a route planning tool and especially on reducing the time taken to produce routes.

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“…In [22], Paskin and Guestrin also apply their architecture (see section 4.3 for an explanation of the application to information processing) for robust inference to the collective sensing strategy problem (what they call optimal control problem), by distributing a typical algorithm for centralized inference in a way that its computation and communication cost can be minimized. In [29] Waldock and Nicholson show how Probability Collectives (PC), a powerful new framework for distributed optimisation, can be used for cooperative sensing in a decentralised sensor network. PC are used for sampling the joint space of sensor actions to discover an optimal collective sensing strategy.…”

Section: Collectivementioning

confidence: 99%

A Survey on Sensor Networks from a Multiagent Perspective

Vinyals

Rodŕıguez-Aguilar

Cerquides

2010

The Computer Journal

106

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Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of real-world applications. MultiAgent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling autonomous self-aware sensors in a flexible way. Firstly, this survey summarizes the actual challenges and research areas concerning sensor networks while identifying the most relevant MAS contributions. Secondly, we propose a taxonomy for sensor networks that classifies them depending on their features (and the research problems they pose). Finally, we identify some open future research directions and opportunities for MAS research.

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Multi-Objective Probability Collectives

Cited by 6 publications

References 23 publications

Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers

Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers

Multiple objective optimisation applied to route planning

A Survey on Sensor Networks from a Multiagent Perspective

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