Helper and Equivalent Objectives:Efficient Approach for Constrained Optimization

Numerous multi-objective evolutionary algorithms have been designed for constrained optimisation over past two decades. The idea behind these algorithms is to transform constrained optimisation problems into multi-objective optimisation problems without any constraint, and then solve them. In this paper, we propose a new multi-objective method for constrained optimisation, which works by converting a constrained optimisation problem into a problem with helper and equivalent objectives. An equivalent objective means that its optimal solution set is the same as that to the constrained problem but a helper objective does not. Then this multi-objective optimisation problem is decomposed into a group of sub-problems using the weighted sum approach. Weights are dynamically adjusted so that each subproblem eventually tends to a problem with an equivalent objective. We theoretically analyse the computation time of the helper and equivalent objective method on a hard problem called ``wide gap''. In a ``wide gap'' problem, an algorithm needs exponential time to cross between two fitness levels (a wide gap). We prove that using helper and equivalent objectives can shorten the time of crossing the ``wide gap''. We conduct a case study for validating our method. An algorithm with helper and equivalent objectives is implemented. Experimental results show that its overall performance is ranked first when compared with other eight state-of-art evolutionary algorithms on IEEE CEC2017 benchmarks in constrained optimisation

Multiobjective differential evolution enhanced with principle component analysis for constrained optimization

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimization. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimization. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimization, but MOEAs based on non-dominance, such as PMODE, may not

Model transformation for multi-objective architecture optimisation for dependable systems

Model-based engineering (MBE) promises a number of advantages for the development of embedded systems. Model-based engineering depends on a common model of the system, which is refined as the system is developed. The use of a common model promises a consistent and systematic analysis of dependability, correctness, timing and performance properties. These benefits are potentially available early and throughout the development life cycle. An important part of model-based engineering is the use of analysis and design languages. The Architecture Analysis and Design Language (AADL) is a new modelling language which is increasingly being used for high dependability embedded systems development. AADL is ideally suited to model-based engineering but the use of new language threatens to isolate existing tools which use different languages. This is a particular problem when these tools provide an important development or analysis function, for example system optimisation. System designers seek an optimal trade-off between high dependability and low cost. For large systems, the design space of alternatives with respect to both dependability and cost is enormous and too large to investigate manually. For this reason automation is required to produce optimal or near optimal designs.There is, however, a lack of analysis techniques and tools that can perform a dependability analysis and optimisation of AADL models. Some analysis tools are available in the literature but they are not able to accept AADL models since they use a different modelling language. A cost effective way of adding system dependability analysis and optimisation to models expressed in AADL is to exploit the capabilities of existing tools. Model transformation is a useful technique to maximise the utility of model-based engineering approaches because it provides a route for the exploitation of mature and tested tools in a new model-based engineering context. By using model transformation techniques, one can automatically translate between AADL models and other models. The advantage of this model transformation approach is that it opens a path by which AADL models may exploit existing non-AADL tools.There is little published work which gives a comprehensive description of a method for transforming AADL models. Although transformations from AADL into other models have been reported only one comprehensive description has been published, a transformation of AADL to petri net models. There is a lack of detailed guidance for the transformation of AADL models.This thesis investigates the transformation of AADL models into the HiP-HOPS modelling language, in order to provide dependability analysis and optimisation. HiP-HOPS is a mature, state of the art, dependability analysis and optimisation tool but it has its own model. A model transformation is defined from the AADL model to the HiP-HOPS model. In addition to the model-to-model transformation, it is necessary to extend the AADL modelling attributes. For cost and dependability optimisation, a new AADL property set is developed for modelling component and system variability. This solves the problem of describing, within an AADL model, the design space of alternative designs. The transformation (with transformation rules written in ATLAS Transformation Language (ATL)) has been implemented as a plug-in for the AADL model development tool OSATE (Open-source AADL Tool Environment). To illustrate the method, the plug-in is used to transform some AADL model case-studies

Water-related modelling in electric power systems: WATERFLEX Exploratory Research Project: version 1

Water is needed for energy. For instance, hydropower is the technology that generates more electricity worldwide after the fossil-fuelled power plants and its production depends on water availability and variability. Additionally, thermal power plants need water for cooling and thus generate electricity. On the other hand, energy is also needed for water. Given the increase of additional hydropower potential worldwide in the coming years, the high dependence of electricity generation with fossil-fuelled power plants, and the implications of the climate change, relevant international organisations have paid attention to the water-energy nexus (or more explicitly within a power system context, the water-power nexus). The Joint Research Centre of the European Commission, the United States Department of Energy, the Institute for Advanced Sustainability Studies, the Midwest Energy Research Consortium and the Water Council, or the Organisation for Economic Co-operation and Development, among others, have raised awareness about this nexus and its analysis as an integrated system. In order to properly analyse such linkages between the power and water sectors, there is a need for appropriate modelling frameworks and mathematical approaches. This report comprises the water-constrained models in electric power systems developed within the WATERFLEX Exploratory Research Project of the European Commission’s Joint Research Centre in order to analyse the water-power interactions. All these models are deemed modules of the Dispa-SET modelling tool. The version 1 of the medium-term hydrothermal coordination module is presented with some modelling extensions, namely the incorporation of transmission network constraints, water demands, and ecological flows. Another salient feature of this version of Dispa-SET is the modelling of the stochastic medium-term hydrothermal coordination problem. The stochastic problem is solved by using an efficient scenario-based decomposition technique, the so-called Progressive Hedging algorithm. This technique is an Augmented-Lagrangian-based decomposition method that decomposes the original problem into smaller subproblems per scenario. The Progressive Hedging algorithm has multiple advantages: — It is easy parallelizable due to its inherent structure. — It provides solution stability and better computational performance compared to Benders-like decomposition techniques (node-based decomposition). — It scales better for large-scale stochastic programming problems. — It has been widely used in the technical literature, thus demonstrating its efficiency. Its implementation has been carried out through the PySP software package which is part of the Coopr open-source Python repository for optimisation. This report also describes the cooling-related constraints included in the unit commitment and dispatch module of Dispa-SET. The cooling-related constraints encompass limitations on allowable maximum water withdrawals of thermal power plants and modelling of the power produced in terms of the river water temperature of the power plant inlet. Limitations on thermal releases or water withdrawals could be imposed due to physical or policy reasons. Finally, an offline and decoupled modelling framework is presented to link such modules with the rainfall-runoff hydrological LISFLOOD model. This modelling framework is able to accurately capture the water-power interactions. Some challenges and barriers to properly address the water-power nexus are also highlighted in the report.JRC.C.7-Knowledge for the Energy Unio