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

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Hybrid Evolutionary Shape Manipulation for Efficient Hull Form Design Optimisation

‘Eco-friendly shipping’ and fuel efficiency are gaining much attention in the maritime industry due to increasingly stringent environmental regulations and volatile fuel prices. The shape of hull affects the overall performance in efficiency and stability of ships. Despite the advantages of simulation-based design, the application of a formal optimisation process in actual ship design work is limited. A hybrid approach which integrates a morphing technique into a multi-objective genetic algorithm to automate and optimise the hull form design is developed. It is envisioned that the proposed hybrid approach will improve the hydrodynamic performance as well as overall efficiency of the design process

New Techniques and Algorithms for Multiobjective and Lexicographic Goal-Based Shortest Path Problems

Shortest Path Problems (SPP) are one of the most extensively studied problems in the fields of Artificial Intelligence (AI) and Operations Research (OR). It consists in finding the shortest path between two given nodes in a graph such that the sum of the weights of its constituent arcs is minimized. However, real life problems frequently involve the consideration of multiple, and often conflicting, criteria. When multiple objectives must be simultaneously optimized, the concept of a single optimal solution is no longer valid. Instead, a set of efficient or Pareto-optimal solutions define the optimal trade-off between the objectives under consideration. The Multicriteria Search Problem (MSP), or Multiobjective Shortest Path Problem, is the natural extension to the SPP when more than one criterion are considered. The MSP is computationally harder than the single objective one. The number of label expansions can grow exponentially with solution depth, even for the two objective case. However, with the assumption of bounded integer costs and a fixed number of objectives the problem becomes tractable for polynomially sized graphs. A wide variety of practical application in different fields can be identified for the MSP, like robot path planning, hazardous material transportation, route planning, optimization of public transportation, QoS in networks, or routing in multimedia networks. Goal programming is one of the most successful Multicriteria Decision Making (MCDM) techniques used in Multicriteria Optimization. In this thesis we explore one of its variants in the MSP. Thus, we aim to solve the Multicriteria Search Problem with lexicographic goal-based preferences. To do so, we build on previous work on algorithm NAMOA*, a successful extension of the A* algorithm to the multiobjective case. More precisely, we provide a new algorithm called LEXGO*, an exact label-setting algorithm that returns the subset of Pareto-optimal paths that satisfy a set of lexicographic goals, or the subset that minimizes deviation from goals if these cannot be fully satisfied. Moreover, LEXGO* is proved to be admissible and expands only a subset of the labels expanded by an optimal algorithm like NAMOA*, which performs a full Multiobjective Search. Since time rather than memory is the limiting factor in the performance of multicriteria search algorithms, we also propose a new technique called t-discarding to speed up dominance checks in the process of discarding new alternatives during the search. The application of t-discarding to the algorithms studied previously, NAMOA* and LEXGO*, leads to the introduction of two new time-efficient algorithms named NAMOA*dr and LEXGO*dr , respectively. All the algorithmic alternatives are tested in two scenarios, random grids and realistic road maps problems. The experimental evaluation shows the effectiveness of LEXGO* in both benchmarks, as well as the dramatic reductions of time requirements experienced by the t-discarding versions of the algorithms, with respect to the ones with traditional pruning

Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

The Kalai-Smorodinski solution for many-objective Bayesian optimization

An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the number of objectives. We thus propose to focus on a specific solution originating from game theory, the Kalai-Smorodinsky solution, which possesses attractive properties. In particular, it ensures equal marginal gains over all objectives. We further make it insensitive to a monotonic transformation of the objectives by considering the objectives in the copula space. A novel tailored algorithm is proposed to search for the solution, in the form of a Bayesian optimization algorithm: sequential sampling decisions are made based on acquisition functions that derive from an instrumental Gaussian process prior. Our approach is tested on four problems with respectively four, six, eight, and nine objectives. The method is available in the Rpackage GPGame available on CRAN at https://cran.r-project.org/package=GPGame

Evolutionary model type selection for global surrogate modeling

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type

Multiobjective metaheuristic approaches for mean-risk combinatorial optimisation with applications to capacity expansion

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Técnicas de otimização na agricultura : o problema de rotação de culturas

Orientadores: Akebo Yamakami, Priscila Cristina Berbert RampazzoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Rotação de culturas é o futuro da agricultura sustentável. Diversidade na sequência de rotação melhora as propriedades físicas e químicas do solo sem demandar todas as exaustivas práticas convencionais de manejo do solo ou grandes quantidades de insumos agrícolas. Cultivar plantas de cobertura ao longo da rotação também desempenha um papel fundamental no controle de pestes e ervas daninhas, melhora a fertilidade do solo e reduz os processos erosivos. Embora esta pesquisa concentre-se na promoção de práticas agrícolas mais sustentáveis, as propriedades rurais precisam ser lucrativas e resilientes para prosperar num futuro incerto. Então, o planejamento das rotações de culturas precisa equilibrar os cenários econômicos potenciais e a conservação ambiental, sendo que as técnicas de otimização conseguem realizar este balanço naturalmente. Após considerar o fluxo de nutrientes nos campos cultiváveis e muitas vantagens do cultivo das plantas de rotação, foram propostos novos modelos para o Problema de Rotação de Culturas (PRC). A pesquisa prosseguiu com a avaliação das técnicas de otimização disponíveis para o PRC e com a proposta de novos métodos. Das abordagens clássicas, foram analizados métodos de otimização multiobjetivo, tais como o método da soma ponderada e as técnicas de escalarização. Em busca de métodos mais eficientes, os algoritmos evolutivos (AE), que são baseados na evolução biológica, tais como herança genética e mutação, são alternativas interessantes. Foram desenvolvidos algoritmos genéticos para otimização mono-objetivo e para otimização multi-objetivo. Após a realização de diversos testes utilizando dados reais do PRC, os resultados encontrados confirmam que os algoritmos propostos têm desempenho satisfatório. Esta pesquisa contribuiu para os campos da Agricultura, com os modelos propostos para o PRC, e da Otimização, com o desenvolvimento de algoritmos evolutivosAbstract: Crop rotation is the future of sustainable agriculture. Diversity in the cropping sequence can improve soil physical and chemical properties without demanding all the conventional tillage practices or large amounts of agricultural chemicals. Growing cover crops along the rotation also plays a fundamental role in controlling pests and weeds, improving soil fertility and reducing erosion. Although we have focused on bringing about more sustainable agrarian practices, farms ought to be profitable and resilient to thrive in an uncertain future. Therefore, planning crop rotations needs to balance the potential economic scenarios and the environmental conservation, which optimization techniques can manage this balance naturally. Our main effort in this research is to develop the crop rotation¿s concepts in the optimization perspective. After carefully considering the nutrient flow in agricultural fields and many advantages of seeding cover crops, we have proposed new models for the Crop Rotation Problem (CRP). Our research proceeds with evaluating optimization techniques for the CRP and proposing new alternatives. From classical methodologies, we have analyzed multiobjective optimization methods such as the weighted sum and the achievement scalarizing function technique. Looking for more efficient methods, evolutionary algorithms (EAs), which are based on biological evolution, such as genetic inheritance and mutation, are interesting alternatives. We have developed a mono-objective genetic algorithm and a multiobjective one. After running several tests using real data of the CRP, the achieved results confirm that the proposed algorithms have satisfactory performance. This research contributed to the fields of Agriculture, with the proposed models of CRP and Optimization, with the development of evolutionary algorithmsMestradoAutomaçãoMestre em Engenharia Elétrica88882.329362/2019-01CAPE

A Critical Review of Optimization Methods for Road Vehicles Design

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77078/1/AIAA-2006-6998-235.pd