Enhanced Optimization with Composite Objectives and Novelty Selection

An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. This paper proposes a solution: The original objectives are replaced by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. In the highly deceptive problem of discovering minimal sorting networks, this approach finds better solutions, and finds them faster and more consistently than standard methods. It is therefore a promising approach to solving deceptive problems through multi-objective optimization.Comment: 7 page

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk

The reduction in energy demand for heating and cooling with insulation materials increases the material related environmental impact. Thus, implementing low embodied energy materials may equilibrate this trade-off. Actual trends in passive house postulate bio-based materials as an alternative to conventional ones. Despite that, the implementation of those insulators should be carried out with a deeper analysis due to their hygroscopic properties. The moisture transfer, the associated condensation risk and the energy consumption for seven biobased materials and polyurethane for a building-like cubicle are analysed. The performance is evaluated combining a software application to model the cubicle (EnergyPlus) and a tool to optimize its performance (jEPlus). The novelty of this optimization approach is to include and evaluate the effects of moisture in these insulation materials, taking into account the mass transfer through the different layers and the evaporation of the different materials. This methodology helps optimise the insulation type and thickness verifying the condensation risk, preventing the deterioration of the materials. The total cost of the different solutions is quantified, and the environmental impact is determined using the life cycle assessment methodology. The effect of climate conditions and the envelope configuration, as well as the risk of condensation, are quantified. The results show that cost and environmental impact can be reduced if bio-based materials are used instead of conventional ones, especially in semiarid climates. Condensation risk occurs for large thicknesses and in humid climates. In our case studies, hemp offered the most balanced solution.The authors would like to acknowledge financial support from the Spanish Government (CTQ2016-77968-C3-1-P, ENE2015-64117-C5-1-R, ENE2015-64117-C5-3-R, MINECO/FEDER, UE). The research leading to these results has received funding from the European Commission Seventh Framework Programme under grant agreement no. PIRSES-GA-2013-610692 (INNOSTORAGE). This project has received funding the European Union's Horizon 2020 Research and Innovation Program under grant agreement No 657466 (INPATH-TES). This article has been possible with the support of the Ministerio de Economía y Competitividad (MINECO) and the Universitat Rovira i Virgili (URV) (FJCI-2016-28789). Authors would like to acknowledge the Brazilian Government for their support by the CNPq (National Council for Scientific and Technological Development). M.P. would like to thank the Brazilian Education Ministry for the financial support received under the PNPD/Capes fellowship. L.F.C. would like to thank the Catalan Government for the quality accreditation given to her research group GREA (2014 SGR 123)

Adaptive game level creation through rank-based interactive evolution

This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.The research is supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the FP7 ICT project C2Learn (project no: 318480).peer-reviewe

Unified clustering and communication protocol for wireless sensor networks

In this paper we present an energy-efficient cross layer protocol for providing application specific reservations in wireless senor networks called the “Unified Clustering and Communication Protocol ” (UCCP). Our modular cross layered framework satisfies three wireless sensor network requirements, namely, the QoS requirement of heterogeneous applications, energy aware clustering and data forwarding by relay sensor nodes. Our unified design approach is motivated by providing an integrated and viable solution for self organization and end-to-end communication is wireless sensor networks. Dynamic QoS based reservation guarantees are provided using a reservation-based TDMA approach. Our novel energy-efficient clustering approach employs a multi-objective optimization technique based on OR (operations research) practices. We adopt a simple hierarchy in which relay nodes forward data messages from cluster head to the sink, thus eliminating the overheads needed to maintain a routing protocol. Simulation results demonstrate that UCCP provides an energy-efficient and scalable solution to meet the application specific QoS demands in resource constrained sensor nodes. Index Terms — wireless sensor networks, unified communication, optimization, clustering and quality of service