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Bandaru, S. & Smedberg, H. (2019). A parameterless performance metric for reference-point based multi-objective evolutionary algorithms. In: Manuel López-Ibáñez (Ed.), GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. Paper presented at Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, July 13-17, 2019 (pp. 499-506). New York, NY, USA: ACM Digital Library
Open this publication in new window or tab >>A parameterless performance metric for reference-point based multi-objective evolutionary algorithms
2019 (English)In: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference / [ed] Manuel López-Ibáñez, New York, NY, USA: ACM Digital Library, 2019, p. 499-506Conference paper, Published paper (Refereed)
Abstract [en]

Most preference-based multi-objective evolutionary algorithms use reference points to articulate the decision maker's preferences. Since these algorithms typically converge to a sub-region of the Pareto-optimal front, the use of conventional performance measures (such as hypervolume and inverted generational distance) may lead to misleading results. Therefore, experimental studies in preference-based optimization often resort to using graphical methods to compare various algorithms. Though a few ad-hoc measures have been proposed in the literature, they either fail to generalize or involve parameters that are non-intuitive for a decision maker. In this paper, we propose a performance metric that is simple to implement, inexpensive to compute, and most importantly, does not involve any parameters. The so called expanding hypercube metric has been designed to extend the concepts of convergence and diversity to preference optimization. We demonstrate its effectiveness through constructed preference solution sets in two and three objectives. The proposed metric is then used to compare two popular reference-point based evolutionary algorithms on benchmark optimization problems up to 20 objectives.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2019
Keywords
multi-objective optimization, decision making, reference point, performance metric, comparison
National Category
Computer Sciences
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17515 (URN)10.1145/3321707.3321757 (DOI)978-1-4503-6111-8 (ISBN)
Conference
Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, July 13-17, 2019
Funder
Knowledge Foundation, 41459
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-10-08Bibliographically approved
Deb, K., Bandaru, S. & Seada, H. (2019). Generating Uniformly Distributed Points on a Unit Simplex for Evolutionary Many-Objective Optimization. In: Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed (Ed.), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Paper presented at 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019 (pp. 179-190). Cham, Switzerland: Springer, 11411
Open this publication in new window or tab >>Generating Uniformly Distributed Points on a Unit Simplex for Evolutionary Many-Objective Optimization
2019 (English)In: Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings / [ed] Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed, Cham, Switzerland: Springer, 2019, Vol. 11411, p. 179-190Conference paper, Published paper (Refereed)
Abstract [en]

Most of the recently proposed evolutionary many-objective optimization (EMO) algorithms start with a number of predefined reference points on a unit simplex. These algorithms use reference points to create reference directions in the original objective space and attempt to find a single representative near Pareto-optimal point around each direction. So far, most studies have used Das and Dennis’s structured approach for generating a uniformly distributed set of reference points on the unit simplex. Due to the highly structured nature of the procedure, this method does not scale well with an increasing number of objectives. In higher dimensions, most created points lie on the boundary of the unit simplex except for a few interior exceptions. Although a level-wise implementation of Das and Dennis’s approach has been suggested, EMO researchers always felt the need for a more generic approach in which any arbitrary number of uniformly distributed reference points can be created easily at the start of an EMO run. In this paper, we discuss a number of methods for generating such points and demonstrate their ability to distribute points uniformly in 3 to 15-dimensional objective spaces.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Keywords
Many-objective optimization, Reference points, Das and Dennis points, Diversity preservation
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16713 (URN)10.1007/978-3-030-12598-1_15 (DOI)2-s2.0-85063041223 (Scopus ID)978-3-030-12597-4 (ISBN)978-3-030-12598-1 (ISBN)
Conference
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Projects
Knowledge-Driven Decision Support (KDDS)
Funder
Knowledge Foundation, 41231
Note

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11411)

Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-05-23Bibliographically approved
Wang, W., Bandaru, S. & Sánchez de Ocãna Torroba, A. (2019). Improved Human-Robot Collaboration Through Simulation-Based Optimization. In: Yan Jin, Mark Price (Ed.), Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK. Paper presented at 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK (pp. 153-158). Amsterdam: IOS Press, 9, Article ID 10.3233/ATDE190027.
Open this publication in new window or tab >>Improved Human-Robot Collaboration Through Simulation-Based Optimization
2019 (English)In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 153-158, article id 10.3233/ATDE190027Conference paper, Published paper (Refereed)
Abstract [en]

In order to pursue the dream combination of human flexibility and robot automation, human robot collaboration (HRC) is increasingly being investigated through academic research and industrial scenarios. HRC involves several challenges ranging from safety and comfort of the human to process efficiency and cost of robot operation. Achieving the right balance between these aspects is critical to implementing a safe, profitable and sustainable HRC environment. In this paper,we propose the use of simulation-based optimization (SBO) for assembly task allocation and scheduling for a HRC working cell in which an industrial robot assists a human worker. The list of product assembly operations are classified according to the capability of human worker and robot, and the sequencing constraints on them are the initial inputs of the method. The operators’ ergonomic load scores and cycletime of the assembly process are achieved by simulation. The optimized solutions are sorted to find the trade-offs between ergonomics and cycle time. We demonstratethe feasibility of the proposed approach through an industrial case study.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2019
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 9
Keywords
simulation based optimization, task allocation, scheduling, assembly, human robot collaboration
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
INF201 Virtual Production Development; VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17792 (URN)10.3233/ATDE190027 (DOI)978-1-64368-008-8 (ISBN)978-1-64368-009-5 (ISBN)
Conference
17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-25Bibliographically approved
Amouzgar, K., Bandaru, S., Andersson, T. & Ng, A. H. C. (2019). Metamodel based multi-objective optimization of a turning process by using finite element simulation. Engineering optimization (Print)
Open this publication in new window or tab >>Metamodel based multi-objective optimization of a turning process by using finite element simulation
2019 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273Article in journal (Refereed) Epub ahead of print
Abstract [en]

This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2019
Keywords
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering; Virtual Manufacturing Processes
Identifiers
urn:nbn:se:his:diva-17520 (URN)10.1080/0305215X.2019.1639050 (DOI)000477101800001 ()
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-11-06Bibliographically approved
Bandaru, S. & Ng, A. H. C. (2019). Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space. In: Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed (Ed.), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Paper presented at 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019 (pp. 605-617). Cham, Switzerland: Springer, 11411
Open this publication in new window or tab >>Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space
2019 (English)In: Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings / [ed] Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed, Cham, Switzerland: Springer, 2019, Vol. 11411, p. 605-617Conference paper, Published paper (Refereed)
Abstract [en]

Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Keywords
Visualization, Data mining, Multi-criteria decision making, Decision space, Trend analysis, Objective space
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16712 (URN)10.1007/978-3-030-12598-1_48 (DOI)2-s2.0-85063032277 (Scopus ID)978-3-030-12597-4 (ISBN)978-3-030-12598-1 (ISBN)
Conference
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Projects
Knowledge-Driven Decision Support (KDDS)
Funder
Knowledge Foundation, 41231
Note

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11411)

Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-05-23Bibliographically approved
Amouzgar, K., Bandaru, S., Andersson, T. J. & Ng, A. H. C. (2018). A framework for simulation based multi-objective optimization and knowledge discovery of machining process. The International Journal of Advanced Manufacturing Technology, 98(9-12), 2469-2486
Open this publication in new window or tab >>A framework for simulation based multi-objective optimization and knowledge discovery of machining process
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, no 9-12, p. 2469-2486Article in journal (Refereed) Published
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15136 (URN)10.1007/s00170-018-2360-8 (DOI)000444704300020 ()2-s2.0-85049664435 (Scopus ID)
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2019-05-08
Bandaru, S. & Ng, A. H. C. (2018). An empirical comparison of metamodeling strategies in noisy environments. In: Hernan Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018): . Paper presented at Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018 (pp. 817-824). New York, NY, USA: ACM Digital Library, Article ID 3205509.
Open this publication in new window or tab >>An empirical comparison of metamodeling strategies in noisy environments
2018 (English)In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018) / [ed] Hernan Aguirre, New York, NY, USA: ACM Digital Library, 2018, p. 817-824, article id 3205509Conference paper, Published paper (Refereed)
Abstract [en]

Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2018
Series
GECCO '18
Keywords
simulation, optimization, metamodeling, noise
National Category
Computer Sciences Other Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15966 (URN)10.1145/3205455.3205509 (DOI)2-s2.0-85050638821 (Scopus ID)978-1-4503-5618-3 (ISBN)
Conference
Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018
Projects
Synergy KDDS
Funder
Knowledge Foundation, 41231
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2019-03-27
Amouzgar, K., Bandaru, S. & Ng, A. H. C. (2018). Radial basis functions with a priori bias as surrogate models: A comparative study. Engineering applications of artificial intelligence, 71, 28-44
Open this publication in new window or tab >>Radial basis functions with a priori bias as surrogate models: A comparative study
2018 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 71, p. 28-44Article in journal (Refereed) Published
Abstract [en]

Radial basis functions are augmented with a posteriori bias in order to perform robustly when used as metamodels. Recently, it has been proposed that the bias can simply be set a priori by using the normal equation, i.e., the bias becomes the corresponding regression model. In this study, we demonstrate the performance of the suggested approach (RBFpri) with four other well-known metamodeling methods; Kriging, support vector regression, neural network and multivariate adaptive regression. The performance of the five methods is investigated by a comparative study, using 19 mathematical test functions, with five different degrees of dimensionality and sampling size for each function. The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness. Furthermore, a rigorous statistical analysis of performance metrics is performed. The results show that the proposed radial basis function with a priori bias achieved the best performance in most of the experiments in terms of all three metrics. When considering the statistical analysis results, the proposed approach again behaved the best, while Kriging was relatively as accurate and support vector regression was almost as fast as RBFpri. The proposed RBF is proven to be the most suitable method in predicting the ranking among pairs of solutions utilized in evolutionary algorithms. Finally, the comparison study is carried out on a real-world engineering optimization problem.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Kriging, Metamodeling, Multivariate adaptive regression splines, Neural networks, Radial basis function, Support vector regression, Surrogate models, Errors, Evolutionary algorithms, Functions, Heat conduction, Image segmentation, Interpolation, Mean square error, Optimization, Regression analysis, Statistical methods, Radial basis functions, Support vector regression (SVR), Surrogate model, Radial basis function networks
National Category
Mechanical Engineering
Research subject
Mechanics of Materials; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14999 (URN)10.1016/j.engappai.2018.02.006 (DOI)000436213000003 ()2-s2.0-85042877194 (Scopus ID)
Available from: 2018-04-01 Created: 2018-04-03 Last updated: 2019-04-12Bibliographically approved
Bandaru, S., Ng, A. H. C. & Deb, K. (2017). Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey. Expert systems with applications, 70, 139-159
Open this publication in new window or tab >>Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 70, p. 139-159Article, review/survey (Refereed) Published
Abstract [en]

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. 

Keywords
Data mining, Multi-objective optimization, Descriptive statistics, Visual data mining, Machine learning, Knowledge-driven optimization
National Category
Computer Sciences
Research subject
Technology; Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13267 (URN)10.1016/j.eswa.2016.10.015 (DOI)000389162000009 ()2-s2.0-84995972531 (Scopus ID)
Projects
KDISCO and Knowledge Driven Decision Support via Optimization (KDDS)
Funder
Knowledge Foundation, 41231
Available from: 2016-12-29 Created: 2016-12-29 Last updated: 2019-01-24Bibliographically approved
Bandaru, S., Ng, A. H. C. & Deb, K. (2017). Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications. Expert systems with applications, 70, 119-138
Open this publication in new window or tab >>Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 70, p. 119-138Article in journal (Refereed) Published
Abstract [en]

The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker's preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences. 

Keywords
Data mining, Knowledge discovery, Multi-objective optimization, Discrete variables, Production systems, Flexible pattern mining
National Category
Computer Sciences
Research subject
Technology; Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13266 (URN)10.1016/j.eswa.2016.10.016 (DOI)000389162000008 ()2-s2.0-84995977095 (Scopus ID)
Projects
KDISCO and Knowledge Driven Decision Support via Optimization (KDDS)
Funder
Knowledge Foundation, 41231
Available from: 2016-12-29 Created: 2016-12-29 Last updated: 2019-01-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5436-2128

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