Multi-dwelling Refurbishment Optimization: Problem Decomposition, Solution, and Trade-off Analysis

A methodology has been developed for the multiobjective optimization of the refurbishment of domestic building stock on a regional scale. The approach is based on the decomposition of the problem into two stages: first to find the energy-cost trade-off for individual houses, and then to apply it tomultiple houses. The approach has been applied to 759 dwellings using buildings data from a survey of the UK housing stock. The energy use of each building and their refurbished variants were simulated using EnergyPlus using automatically-generated input files. The variation in the contributing refurbishment options from least to highest cost along the Pareto front shows loft and cavity wall insulation to be optimal intially, and solid wall insulation and double glazing appearing later

Multi-dwelling refurbishment optimization: problem decomposition, solution and trade-off analysis

A methodology has been developed for the multiobjective optimization of the refurbishment of domestic building stock on a regional scale. The approach is based on the decomposition of the problem into two stages: first to find the energy-cost trade-off for individual houses, and then to apply it tomultiple houses. The approach has been applied to 759 dwellings using buildings data from a survey of the UK housing stock. The energy use of each building and their refurbished variants were simulated using EnergyPlus using automatically-generated input files. The variation in the contributing refurbishment options from least to highest cost along the Pareto front shows loft and cavity wall insulation to be optimal intially, and solid wall insulation and double glazing appearing later

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

An adaptive localized decision variable analysis approach to large scale multi-objective and many-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and many objective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms