This work is a product of the Collaborative Research Center 531, “Computational Intelligence, ” at the Technische Universität Dortmund and was printed with financial

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Multi-objective Optimization

Oliver Schütze, Carlos Hernández · Studies in computational intelligence · 2021

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Multi-Objective Optimization

Gade Pandu Rangaiah · Advances in process systems engineering · 2016

Introduction (G P Rangaiah) Multi-Objective Optimization Applications in Chemical Engineering (Masuduzzaman & G P Rangaiah) Techniques: Multi-Objective Evolutionary Algorithms: A Review of the State of the Art and Some of Their Applications in Chemical Engineering (A L Jaimes & C A Coello Coello) The Jumping Gene Adaptations of Multi-Objective Genetic Algorithm and Simulated Annealing (M Ramteke & S K Gupta) Multi-Objective Optimization Using Surrogate-Assisted Evolutionary Algorithm (T Ray) Why Use Interactive Multi-Objective Optimization in Chemical Process Design? (K Miettinen & J Hakanen) Net Flow and Rough Set: Two Methods for Ranking the Pareto Domain (J Thibault) Applications: Multi-Objective Optimization of Gas-Phase Refrigeration Systems for LNG (N Shah et al.) A Multi-Objective Evolutionary Algorithm for Practical Residue Catalytic Cracking Feed Optimization (K C Tan et al.) Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives (E S Q Lee et al.) Multi-Objective Emergency Response Optimization around Chemical Plants (P S Georgiadou et al.) Array Informatics Using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks (S Garg) Multi-Objective Optimization of a Multi-Product Microbial Cell Factory for Multiple Objectives - A Paradigm for Metabolic Pathway Recipe (F C Lee et al.).

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Multi-Objective Optimization

Kalyanmoy Deb, Karthik Sindhya, Jussi Hakanen · Decision Sciences · 2016

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Multi-objective Combinatorial Optimization

Jacques Teghem · Encyclopedia of Optimization · 2025

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Scalable multi-objective optimization

Martí Orosa, Luis · e-Archivo (Carlos III University of Madrid) · 2011

This thesis is concerned with the three open in multi-objective optimization: (i) the development of strategies for dealing with problems with many objective functions; (ii) the comprehension and solution of the model-building issues of current MOEDAs, and; (iii) the formulation of stopping criteria for multi-objective optimizers. We argue about what elements of MOEDAs should be modified in order to achieve a substantial improvement on their performance and scalability. However, in order to supply a solid ground for that discussion, some other elements are to be discussed as well. In particular, this thesis: sketches the supporting theoretical corpus and the fundamentals of MOEA and MOEDA algorithms; analyzes the scalability issue of MOEAs from both theoretical and experimental points of view; discusses the possible directions of improvement for MOEAs’ scalability, presenting the current trends of research; gives reasons of why EDAs can be used as a foundation for achieving a sizable improvement with regard to the scalability issue; examines the model-building issue in depth, hypothesizing on how it affects MOEDAs performance; proposes a novel model-building algorithm, the model-building growing neural gas (MBGNG), which fulfill the requirements for a new approach derived from the previous debate, and; introduces a novel MOEDA, the multi-objective neural EDA, that is constructed using MB-GNG as foundation. The formulation of an strategy for stopping multi-objective optimizers became obvious and necessary as this thesis was developed. The lack of an adequate stopping criterion made the rendered any experimentation that had to do with many objectives a rather cumbersome task. That is why it was compulsory to deal with this issue in order to proceed with further studies. In this regard, the thesis: provides an updated and exhaustive state-of-the-art of this matter; examines the properties and characteristics that a given stopping criterion should exhibit; puts forward a new stopping criterion, denominated MGBM, after the authors last names, that has a small computational footprint, and; experimentally validates MGBM in a set of experiments. Theoretical discussions and algorithm proposals are experimentally contrasted with current state-of-the-art approaches when required. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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