Metaheuristic and matheuristic approaches for multi-objective optimization problems in process engineering : application to the hydrogen supply chain design

Complex optimization problems are ubiquitous in Process Systems Engineering (PSE) and are generally solved by deterministic approaches. The treatment of real case studies usually involves mixed-integer variables, nonlinear functions, a large number of constraints, and several conflicting criteria to be optimized simultaneously, thus challenging the classical methods. The main motivation of this research is therefore to explore alternative solution methods for addressing these complex multiobjective optimization problems related to the PSE area, focusing on the recent advances in Evolutionary Computation. If multiobjective evolutionary algorithms (MOEAs) have proven to be robust for the solution of multiobjective problems, their performance yet strongly depends on the constraint-handling techniques for the solution of highly constrained problems. The core of innovation of this research is the adaptation of metaheuristic-based tools to this class of PSE problems. For this purpose, a two-stage strategy was developed. First, an empirical study was performed in the perspective of comparing different algorithmic configurations and selecting the best to provide a high-quality approximation of the Pareto front. This study, comprising both academic test problems and several PSE applications, demonstrated that a method using the gradient-based mechanism to repair infeasible solutions consistently obtains the best results, in particular for handling equality constraints. Capitalizing on the experience from this preliminary numerical investigation, a novel matheuristic solution strategy was then developed and adapted to the problem of Hydrogen Supply Chain (HSC) design that encompasses the aforementioned numerical difficulties, considering both economic and environmental criteria. A MOEA based on decomposition combined with the gradient-based repair was first explored as a solution technique. However, due to the important number of mass balances (equality constraints), this approach showed a poor convergence to the optimal Pareto front. Therefore, a novel matheuristic was developed and adapted to this problem, following a bilevel decomposition: the upper level (discrete) addresses the HSC structure design problem (facility sizing and location), whereas the lower level (Linear Programming problem) solves the corresponding operation subproblem (production and transportation). This strategy allows the development of an ad-hoc matheuristic solution technique, through the hybridization of a MOEA (upper level) with a LP solver (lower level) using a scalarizing function to deal with the two objectives considered. The numerical results obtained for the Occitanie region case study highlight that the hybrid approach produces an accurate approximation of the optimal Pareto front, more efficiently than exact solution methods. Finally, the matheuristic allowed studying the HSC design problem with more realistic assumptions regarding the technologies used for hydrogen synthesis, the learning rates capturing the increasing maturity of these technologies over time and nonlinear relationships for the computation of Capital and Operational Expenditures (CAPEX and OPEX) for the hydrogen production facilities. The resulting novel model, with a non-convex, bi-objective mixed-integer nonlinear programming (MINLP) formulation, can be efficiently solved through minor modifications in the hybrid algorithm proposed earlier, which finds its mere justification in the determination of the timewise deployment of sustainable hydrogen supply chains

Evolutionary population dynamics and multi-objective optimisation problems

Griffith Sciences, School of Information and Communication TechnologyFull Tex

Multi-objective constrained Bayesian optimization for structural design

The planning and design of buildings and civil engineering concrete structures constitutes a complex problem subject to constraints, for instance, limit state constraints from design codes, evaluated by expensive computations such as finite element (FE) simulations. Traditionally, the focus has been on minimizing costs exclusively, while the current trend calls for good trade-offs of multiple criteria such as sustainability, buildability, and performance, which can typically be computed cheaply from the design parameters. Multi-objective methods can provide more relevant design strategies to find such trade-offs. However, the potential of multi-objective optimization methods remains unexploited in structural concrete design practice, as the expensiveness of structural design problems severely limits the scope of applicable algorithms. Bayesian optimization has emerged as an efficient approach to optimizing expensive functions, but it has not been, to the best of our knowledge, applied to constrained multi-objective optimization of structural concrete design problems. In this work, we develop a Bayesian optimization framework explicitly exploiting the features inherent to structural design problems, that is, expensive constraints and cheap objectives. The framework is evaluated on a generic case of structural design of a reinforced concrete (RC) beam, taking into account sustainability, buildability, and performance objectives, and is benchmarked against the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a random search procedure. The results show that the Bayesian algorithm performs considerably better in terms of rate-of-improvement, final solution quality, and variance across repeated runs, which suggests it is well-suited for multi-objective constrained optimization problems in structural design

An integrated framework for strain optimization

The identification of genetic modifications leading to mutant strains able to overproduce compounds of industrial interest is a challenging task in Metabolic Engineering (ME). Several methods have been proposed but, to some extent, none of them is suitable for all the specificities of each particular strain optimization problem. This work proposes an integrated framework that allows its users to configure and fine tune all the various steps involved in a strain optimization strategy, including the loading of models in distinct formats, the definition of a suitable phenotype simulation method and the choice and configuration of the strain optimization engine. Moreover, it is designed to suit the needs of users skilled at programming, as well as less advanced users. The framework includes a GUI implemented as the strain optimization plug-in for the OptFlux workbench (version 3), a reference platform for ME (http://www.optflux.org). All the code is distributed under the GPLv3 licence and it is fully available (http://sourceforge.net/projects/optflux/).This work is partially funded by ERDF- European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP- 01-0124-FEDER-015079 and PTDC/EBB-EBI/104235/2008. This work is also funded by National Funds through the FCT within project PEst-OE/EEI/UI0752/2011. The work of PM was supported by the FCT through the Ph.D. grant SFRH/BD/61465/2009

A fuzzy decision variables framework for large-scale multiobjective 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.In large-scale multiobjective optimization, too many decision variables hinder the convergence search of evolutionary algorithms. Reducing the search range of the decision space will significantly alleviate this puzzle. With this in mind, this paper proposes a fuzzy decision variables framework for largescale multiobjective optimization. The framework divides the entire evolutionary process into two main stages: fuzzy evolution and precise evolution. In fuzzy evolution, we blur the decision variables of the original solution to reduce the search range of the evolutionary algorithm in the decision space so that the evolutionary population can quickly converge. The degree of fuzzification gradually decreases with the evolutionary process. Once the population approximately converges, the framework will turn to precise evolution. In precise evolution, the actual decision variables of the solution are directly optimized to increase the diversity of the population so as to be closer to the true Pareto optimal front. Finally, this paper embeds some representative algorithms into the proposed framework and verifies the framework’s effectiveness through comparative experiments on various large-scale multiobjective problems with 500 to 5000 decision variables. Experimental results show that in large-scale multiobjective optimization, the framework proposed in this paper can significantly improve the performance and computational efficiency of multiobjective optimization algorithms

What can we learn from multi-objective meta-optimization of Evolutionary Algorithms in continuous domains?

Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs' performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs' meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that "going multi-objective" allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones

Algorithms and tools for in silico design of cell factories

PhD thesis in BioengineeringThe progressive shift from chemical to biotechnological processes is one of the pillars of the 21st century industrial biotechnology. Projections from the Organization for Economic Co-operation and Development estimate that, within the next two decades, about 35% of the production of chemicals will be guaranteed by biotechnological processes. The development of efficient cell-factories, capable of outperforming current chemical processes, is vital for this leap to happen. The development of constraint-based models of metabolism and rational computational strain optimization algorithms (CSOMs) hold the promise to accelerate these e orts. Here, we aim to provide an in depth and critical review of the currently available constraint-based CSOMs, their strengths and limitations, as well as to discuss future trends in the field. Then, we cover in detail the main tasks in strain design and provide a taxonomy of the main CSOMs. These are presented in detail and their features and limitations are explored. One of the identified problems is their limited offering of trade-o solutions of biotechnological objectives (e.g. overproducing desired compounds or minimizing the cost of solutions) versus cellular objectives (e.g. maximizing biomass). To tackle this problem we developed an evolutionary multi-objective (MO) framework for strain optimization capable of finding high-quality, trade-off solutions that can be explored by metabolic engineering experts. Also, the majority of the strain optimization algorithms rely on phenotype prediction methods based on debatable biological assumptions. We verified that, for a large percentage of solutions generated by a CSOM using one phenotype prediction method, the results would not hold when simulated with an alternative method. Leveraging on the previously developed framework and driven by the MO nature of this problem, we proposed a tandem approach capable of finding strain designs that comply with the assumptions of distinct phenotype prediction methods, validating the approach with multiple case studies. Finally, all the algorithms developed during this work are made available in the form of an open and flexible software framework. This framework is a powerful tool for both common users, interested in exploring the available methods, and experienced programmers which are able to easily extend it to support new features.A conversão de processos químicos em processos biotecnológicos e um dos grandes objetivos da biotecnologia industrial para o seculo XXI. A Organização para a Cooperação e Desenvolvimento Economico estima que, nas próximas duas décadas, cerca de 35% da produção de compostos químicos sejam assegurados por processos biotecnológicos. O desenvolvimento de fabricas celulares eficientes, capazes de superar o rendimento dos atuais processos químicos, é vital para que este avanço seja possível. O desenvolvimento de modelos metabólicos e algoritmos para otimização de estirpes (AOEs), e uma das grandes esperanças para acelerar estes esforços. Neste trabalho, pretendemos efetuar uma revisão aprofundada dos AOEs atuais baseados na modelação por restrições, analisar os seus pontes fortes e limitações, e discutir temas de interesse futuro na área. De seguida, estudamos em detalhe os tipos de estratégias comuns para o desenho de estirpes e formulamos uma taxonomia para os principais AOEs. Estes são avaliados em detalhe e as suas características principais são devidamente exploradas. Um dos problemas identificados prende-se com a sua oferta limitada de soluções de compromisso entre objetivos industriais (como produzir em excesso um composto de interesse, ou reduzir o custo de implementar uma solução) e objetivos celulares (como a maximização do crescimento). Para enfrentar este problema, desenvolvemos uma plataforma para otimização de estirpes baseada em computação evolucionária multiobjectivo, capaz de encontrar soluções de compromisso de elevada qualidade, que podem ser exploradas por peritos em engenharia metabólica. Para além disso, a grande maioria dos AOEs baseia-se em métodos de previsão de fenótipos que, por sua vez, são construídos sobre assunções biológicas discutíveis. Verificamos que uma grande percentagem das soluções geradas por um AOE, usando um método de previsão de fenótipos, deixaria de ser valida quando simulada com um método alternativo. Tirando partido da plataforma desenvolvida anteriormente e motivados pela natureza multiobjectivo deste problema, propusemos uma abordagem capaz de encontrar estirpes que respeitassem as assunções de diferentes métodos de previsão de fenótipos. Esta abordagem foi validada com vários casos de estudo. Por fim, todos os algoritmos desenvolvidos ao longo deste trabalho são disponibilizados sob a forma de uma aplicação de software aberto. Esta constitui uma ferramenta poderosa, tanto para utilizadores comuns interessados em explorar os métodos disponibilizados, como para programadores experientes que podem estendê-la facilmente com novos métodos.Esta investigação foi financiada pela Fundação para a Ciência e Tecnologia através da concessão de uma bolsa de doutoramento (SFRH/BD/61465/2009), co-financiada pelo POPH – QREN – Tipologia 4.1 – Formação Avançada – e comparticipado pelo Fundo Social Europeu (FSE) e por fundos nacionais do Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)