Multi-objective optimization problems (MOPs) are widely encountered in several disciplines including engineering, operations research, and economics. The theoretical solution to such problems comprises not only one but a set of best trade-off designs in the objective space, known as the Pareto-optimal front (PF). Even though there exists a significant body of literature investigating MOPs, there remain several open and emerging challenges. These include, but are not limited to, developing computationally efficient methods to search a good approximation of the PF, identifying preferred solutions on PF for practical implementation, and designing benchmarking instances, metrics and practices for quantitative comparisons between solution methods. In this talk, I will provide a brief overview of several methods developed in the recent years in our research group to address these issues. Most of the methods presented are developed within the framework of evolutionary algorithms (EAs), which are often a viable choice when the underlying objective and/or constraint functions are highly non-linear or black box in nature. Biography: Hemant Kumar Singh is an Associate Professor at the School of Engineering and Technology at the University of New South Wales (UNSW), Australia. He completed his PhD from UNSW in 2011 and B.Tech in Mechanical Engineering from Indian Institute of Technology (IIT) Kanpur in 2007. He worked with General Electric Aviation at John F. Welch Technology Centre as a Lead Engineer during 2011-13. His research interests include development of evolutionary computation methods to deal with various challenges such as multiple objectives, constraints, uncertainties, hierarchical (bi-level) objectives, and decision-making. He has collectively over 125 refereed publications on these topics. He is the recipient of two Discovery Project Grants (2019-22, 2022-24), from Australian Research Council, Endeavour Australia Fellowship 2018-19 and IEEE CEC Best Paper Award 2023, among others. He is an Associate Editor for IEEE Transactions on Evolutionary Computation and has been in the organizing team of several conferences, e.g., IEEE CEC (Program co-chair 2021), SSCI (MCDM co-chair 2020-23), ACM GECCO (RWACMO workshop co-chair 2018-21). More details of his research and professional activities can be found at http://mdolab.net/Hemant/

In this talk I will talk about several industrial case studies in data-driven optimization including (i) heuristic allocation of computational resources (joint project with ARM), (ii) online bidding for product advertisement (joint work with Dream Agility), and (iii) optimization of drug manufacturing processes (joint project with Biopharm Services). The main focus of the seminar will be on project (i), which is about deciding which computational projects (scripts) should run on which cluster such that the clusters are evenly utilized, as many projects as possible completed, and projects belonging to the same group as evenly distributed across the clusters as possible. After providing a formal problem definition, several heuristics to tackle the problem will be introduced and investigated. Finally, projects (ii) and (iii) will be introduced and computational challenges outlined, hopefully, leading to interesting discussions.

Robust (single-objective) optimization has been grown to an important field which is both, practically important and mathematically challenging. In contrast to this, literature on robust multi-objective optimization is rare. This may be due to the fact that the robust counterpart of an optimization problem requires the implicit determination of a worst scenario in the objective function - which in multi-objective optimization turns out to be a multi-objective optimization problem again! This talk will first review classic and more recent robustness concepts for single-objective optimization and then present possible concepts on how robustness for a Pareto solution may be defined. In particular, we will introduce the concepts of flimsily and highly robust solutions, different variants of set-based minmax solutions and also present a generalization of the recent concept of light robustness to multi-objective problems. Furthermore, some solution approaches on how robust efficient solutions can be computed will be shown. The concepts are illustrated on two examples: planning of short and secure flight routes and location problems.

Surrogate models have shown to be effective in assisting meta-heuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted meta-heuristics, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists in two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

Most existing work on evolutionary optimization assumes that there are analytic functions for evaluating the objectives and constraints. In the real-world, however, the objective or constraint values of many optimization problems can be evaluated solely based on data and solving such optimization problems is often known as data-driven optimization. In this paper, we divide data-driven optimization problems into two categories, i.e., off-line and on-line data-driven optimization, and discuss the main challenges involved therein. An evolutionary algorithm is then presented to optimize the design of a trauma system, which is a typical off-line data-driven multi-objective optimization problem, where the objectives and constraints can be evaluated using incidents only. As each single function evaluation involves large amount of patient data, we develop a multi-fidelity surrogate management strategy to reduce the computation time of the evolutionary optimization. The main idea is to adaptively tune the approximation fidelity by clustering the original data into different numbers of clusters and a regression model is constructed to estimate the required minimum fidelity. Experimental results show that the proposed algorithm is able to save up to 90\% of computation time without much sacrifice of the solution quality.

ParEGO is a global optimization algorithm proposed by the presenter in 2005 for expensive multiobjective problems. Although ParEGO has been recognized by the multiobjective optimization community as an efficient method, the original implementation had several limitations. In this talk I will outline changes we have implemented in an updated release (in early 2016), and the plans for further development while keeping true to the ParEGO framework (of a scalarized version of Jones' et al's EGO method). These improvements mostly concern scalability in the decision parameters, and maximum number of evaluations possible with ParEGO, methods for incorporation of prior knowledge, and hacks for greater stability. Improvements in other aspects are also coming in forthcoming work on sParEGO (Purshouse et al), and ParASEGO* (Hakanen et al) -- very briefly outlined here. Finally, there will be a summary of the plans for a Benchmarking suite for multiobjective surrogate-assisted methods.

In this talk I will expose a couple of problems arising from industry and biology related to optimization naturally involving infinite dimensions. The first is a problem coming from the arena of motorbike competitions and deals with the administration of uncertainty for the distribution of a family of trajectories. The rigorous methods available in literature lead to an analysis which is mostly qualitative. The second problem is the attempt to explain the profile of the xylematic channels (the microtubes carrying the water from the roots to the leafs of a tree) by means of an optimization principle. Being the solution (a curve) belonging to an infinite dimensional space and given that the functional is a variable convex combination of two scalar functions, the formulation appears as an instance of multiobjective calculus of variations.

When solving large-scale multiobjective optimization problems, solvers often get stuck with the memory or time limit. In such cases one is left with no information how far to the true Pareto front are the feasible solutions obtained till a solver stops. In this work we show how to provide such information when solving multiobjective multidimensional knapsack problems by commercial mixed-integer linear solvers accessible on open access platforms. We illustrate the proposed approach on bicriteria multidimensional knap sack problems derived from singleobjective multidimensional knapsack problems taken from Beasley OR Library.

The prioritisation and management of emerging threats to human and animal health pose serious challenges for policy makers. Such challenges have many sources. First, the emerging nature of such threats, coupled with limited impact modelling, means that often there is lack of reliable evidence about impacts and probability of an outbreak. Second, the continuing emergence of multiple threats, often contemporary, requires regular prioritization informed by the amalgamation of different sources of quantitative and qualitative data, and experts' judgments. Third, policy makers are often concerned with multiple impacts that go beyond health and economic concerns, including issues related with public perception and capability building. In this paper we suggest how decision analysis could address these challenges both from an analytical and, more critically, organizational perspective. In particular we argue that the development and use of simple and tailored risk management systems, when appropriately embedded into organizational routines, can provide effective support for the assessment of emerging threats and the design of policy recommendations. We illustrate our suggestions with a real- world case study, in which we developed a risk management support system for DEFRA, the UK Government Department for Environment, Food and Rural Affairs, to help with their prioritization of emerging threats to the countrys animal health status.

Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the so-called multi-objective optimization problems). This area, known as EMOO (Evolutionary Multi-Objective Optimization) has had an important growth in the last 15 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this talk, we will review some of the research topics on evolutionary multi-objective optimization that are currently attracting a lot of interest (e.g., handling many objectives, hybridization, indicator-based selection, use of surrogates, etc.) and which represent good opportunities for doing research. Some of the challenges currently faced by this discipline will also be delineated.

Real-world complex engineering optimisation remains a challenging issue in evolutionary optimisation. This talk discusses the major challenges we face in applying evolutionary algorithms (EAs) to complex engineering optimization, including representation, the involvement of time-consuming and multi-disciplinary quality evaluation processes, changing environments, vagueness in formulating criteria formulation, and the involvement of multiple sub-systems. We propose that the successful tackling of all these aspects give birth to a systems approach to evolutionary design optimization characterized by considerations at four levels, namely, the system property level, temporal level, spatial level and process level. Finally, we suggest a few promising future research topics in evolutionary optimisation that consist in the necessary steps towards a life-like design approach, where design principles found in biological systems such as self-organization, self-repair and scalability play a central role.

The performance of batteries is critical for the mobility and performance of automotive vehicles. In order to maintain battery life and performance, it is crucial to keep the batteries within the temperature range in which their operating characteristics are optimal. To achieve the desired tension and current required for different applications, the cells are packed together in modules which in turn are connected in parallel or in series. To provide a reliable battery, the temperature of the pack should be kept inside the temperature range. The uniformity of the temperature inside the pack depends mainly on the non-uniform heat transfer efficiency among the cells. Due to the capacity unbalance, some cells may experience over/under charging which leads to premature battery failure. The cell optimal layout inside the battery pack is optimized while considering thermal aspects. Due to a large number of function evaluations, computational fluid dynamics models are not suitable and a simplified lumped parameter thermal model is integrated with the optimization. The optimization problem is formulated as a constrained multiobjective program in which objective functions (to be minimized) represent descrepancies between the operating cell temperatures and the target temperature. Such a formulation with physically homogeneous and comparable objectives is addressed in terms of the equitability preference rather than the traditional Pareto preference. Mathematical aspects of the equitability preference are investigated and the applicability of the equitable solutions to engineering problems is discussed. Furthermore, the battery location in the vehicle is also optimized to improve vehicle dynamics, component accessibility and passenger survivability while considering geometric constraints including collision and overlap among the components. The overall optimization problem is two-level. A solution method to the overall problem is outlined.

In this talk I will raise some topics arising in algorithm parameterization, and on their view as a problem of noisy, non-linear optimization. I will present data obtained with the parameterization of some metaheuristics for combinatorial optimization, and propose a method for automatically tuning parameters. The main objective is to open discussion, and to initiate a brainstorming session on what could be done for advancing the state-of-the-art on this issue.

Optimization has become an indispensable tool for radiation therapy. In this talk, we highlight fundamental aspects of the optimization problems that arise, and also discuss more advanced aspects, such as how to handle conflicting treatment goals and model uncertainty. We initially discuss how problem structure may be taken into account for computing approximate solutions to the fundamental optimization problem that arises in radiation therapy. We then show how conflicting treatment goals may be handled in a multiobjective formulation by approximation of the Pareto surface. Finally, we discuss how uncertainties in range, setup and organ motion may be handled in a robust optimization framework for optimization of proton therapy. The talk is based on joint research between KTH and RaySearch Laboratories AB, in particular research carried out by Rasmus Bokrantz, Albin Fredriksson and Fredrik Lofman.

We develop an algorithm to find the best solution for multiobjective integer programs when the DM's preferences are consistent with a quasiconcave utility function. Based on the convex cones derived from past preferences, we characterize the solution space that excludes inferior regions. We guarantee finding the most preferred solution and our computational results show that the algorithm works effectively.

1. An approximation algorithm for convex multi-objective programming problems In multi-objective optimization, several objective functions have to be minimized simultaneously. We propose a method for approximating the nondominated set of a multi-objective nonlinear programming problem, where the objective functions are convex and the feasible set is convex. This method is an extension of Benson's outer approximation algorithm for multi-objective linear programming problems. We prove that this method provides a set of epsilon-nondominated points. For the case that the objectives and constraints are differentiable, we describe an efficient way to carry out the main step of the algorithm, the construction of a hyperplane seperating an exterior point from the feasible set in objective space. We provide examples that show that this cannot always be done in the same way in the case of non-differentiable objectives or constraints. 2. Finite representation of nondominated sets in multiobjective linear programming In this paper we address the problem of representing the continuous set of nondominated solutions of a multiobjective linear programme by a finite subset of such points. We prove that a related decision problem is NP-hard. Moreover we illustrate the drawbacks of the known global hooting, normal boundary intersection and normal constraint methods concerning the coverage of the nondominated set and uniformity of the representation by examples. We propose a method which combines the global shooting and normal boundary intersection methods. By doing so, we overcome the limitations of these methods. We show that our method computes a set of evenly distributed nondominated points for which the the coverage error and the uniformity level can be measured. Finally, we apply this method to an optimization problem in radiation therapy and present illustrative results for some clinical cases.

Being able to render an efficient frontier quickly is an important attribute of the systems used to support decision making in portfolio selection. In standard portfolio selection, simplifying assumptions generally make this possible. But in the larger problems that are beginning to appear with greater frequency, the assumptions can cause more trouble than they are worth. This is shown along with their computational implications. Also shown are the effects on standard portfolio selection of inserting additional criteria (such as dividends, liquidity, etc.) into the portfolio selection process. One is that this causes the efficient frontier to turn into an efficient surface. Another is that the efficient surface has a tendency to be formed by a collection of platelets (like on the back of a turtle). A third concerns the availability of algorithms capable of computing the platelets. And a fourth is how to search a surface of platelets for one's most preferred portfolio on it. Computational results are reported where possible to support the graphs presented.

The concept of implementable nonlinear stochastic programming by finite series of Monte-Carlo samples is surveyed addressing to topics related with stochastic differentiation, stopping rules, conditions of convergence, rational setting of parameters of algorithms, etc. Our approach distinguishes by treatment of the accuracy of the solution in a statistical manner, testing the hypothesis of optimality according to statistical criteria and estimating confidence intervals of the objective and constraint functions. The rule for adjusting the Monte-Carlo sample size is introduced which ensures the convergence by linear rate and enables us to solve the stochastic optimization problem using a reasonable number of Monte-Carlo trials. Issues of implementation of the developed approach in financial management, business management and engineering are considered, too.

Optimization problems in class room go out from a well defined problem setting: the set of feasible solutions is given as well as the objective function(s). When it comes to practice we often find an incomplete description of what is feasible and what not. Even more complicated is it to distinguish between good and bad or even to characterize optimality. The talk emphasizes this major problem of not rigorously given problems and shows how multicriteria optimization and interactive decision support by visualization of solutions might help in this dilemma. Problems and methods are discussed in the context of industrial problems Fraunhofer ITWM is currently involved in.

Scenario planning in its various forms is a widely used approach to strategic planning. It provides a mechanism for sharing understanding of major sources of risk and uncertainty in decision making. In many instances, however, scenario planning does not make use of formal analytical tools for evaluation of potential courses of action. The field of multiple criteria decision analysis (MCDA), on the other hand, has developed powerful tools and algorithms for the evaluation and choice of alternative strategies in the presence of multiple and conflicting objectives. Many approaches to MCDA, however, do not employ formal methods for dealing with substantial uncertainties in outcomes. We thus discuss some approaches to integration of scenario planning and multiple criteria decision analysis, to capture the power of both. The concepts are applicable to any of the broad schools of MCDA, as well as to both discrete choice and continuous problems.

Statistical models of multimodal objective functions have been successfully applied for the construction of black box single objective global optimization algorithms. P-algorithm is based on a statistical model and is defined as the repeated decision making under uncertainty. The axioms of rationality are formulated taking into account the situation of selecting a point for the current computation of the objective function value. The formulated axioms imply the selection of the point of maximum probability to improve the best known value of the objective function. In the present talk the P-algorithm is generalized to multiobjective optimization. An example illustrating properties of the newly proposed algorithm is included.

The presentation will describe some mechanical design problems that are considered complex, namely packaging (compact packing) and layout or configuration design. Complexity in this context will be defined highlighting the multiple interactions that need to be considered and the geometry and other characteristics that contribute to that complexity. Next, the past work on developing an approach to deal with that complexity will be shown as it evolved over the years leading to its current state. The presentation will describe the development of an archive based micro genetic algorithm as well as various geometrical considerations that had to be efficiently managed to solve the problem. The talk will conclude with some of the extensions of that work, describing the design of heterogeneous components.

IMRT can be used for curative treatment even if the tumor is of complicated shape or close to important risk organs. Since the degrees of freedom vastly exceed the number one can manually handle, the planning is done using so called inverse planning. Here, each considered plan is the result of a large-scale optimization problem. The planning problem naturally is a multi-criteria problem: To each relevant organ at risk a function is assigned. Furthermore, the different tumor volumes each get one to two functions. The planning process now tries to find a compromise between the specified goals. Currently, this most often done by iterative changes to the model or iterative changes to the weights with which the different functions contribute to the overall objective. We will call this tedious and time consuming procedure human iteration loop (HIL). Explicitly treating the problem as a multi-criteria problem offers the possibility to greatly improve the planning process. Yet the problem under consideration does not make it easily accessible for multi-criteria optimization: There is no generally acknowledged model for the quality of a plan, the problem is large scale and gets almost intractable, if all degrees of freedom are included into the optimization. The talk will introduce IMRT planning and describe how the described problems were tackled and what open questions are still actively being researched.

Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: (i) how to formalize what type of Pareto set approximation is sought, (ii) how to use this information within an algorithm to efficiently search for a good Pareto set approximation, and (iii) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only few studies can be found that consider all questions under one roof. This talk is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general, preference-independent hill climber for multiobjective optimization with theoretical convergence properties. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.

Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: (i) how to formalize what type of Pareto set approximation is sought, (ii) how to use this information within an algorithm to efficiently search for a good Pareto set approximation, and (iii) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only few studies can be found that consider all questions under one roof. This talk is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general, preference-independent hill climber for multiobjective optimization with theoretical convergence properties. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.

The problem for process optimizing of metal building-up by welding is investigated. A multiple criteria model with four objectives is suggested. The interactive satisfying trade-off method of Nakayama is used to solve the model.

1. Delays in Technology Development: Their Impact on the Issues of Determinism, Autonomy and Controllability of Technology The paper provides a discussion of diverse delays occurring in the development and utilization of technology products, and an explanation of reasons why, when seen holistically from outside, the process of technology development might appear as an autonomous, self-determining, uncontrollable process. When seen from inside, however, e.g., from the perspective of software development and evaluation, the process is far from being uncontrollable. This paradox is explained by the fact that technology development contains many processes with delays, in total amounting sometimes to fifty years; when seen from outside, such a process might appear uncontrollable, even if it is very much controllable when approached internally and in detail. Therefore, the definition and some types of technology creation as well as some stages of technological processes are discussed in some detail in this paper. Some aspects of the contemporary informational revolution and some recent results on micro-theories of knowledge and technology creation are also reviewed. It is suggested that one of possible ways of changing the paradigmatic attitude of philosophy of technology is to invite some such philosophers to participate in the development of modern tools of knowledge civilization era: software development and evaluation; moreover, inputs from philosophy of technology might enrich such processes. On the other hand, without participating in software development and evaluation, philosophy of technology runs the risk of becoming outdated and sterile. The conclusions of the paper stress the need of essentially new approaches to many issues, such as software development and evaluation versus philosophy of technology, in the time when informational revolution results in a transition towards knowledge civilization. 2. Ontology Construction and Its Applications in Local Research Communities Ontological engineering has been widely used for diverse purposes in different communities and a number of approaches have been reported for developing ontologies; however, few works address issues of specific ontology construction for local communities, especially when taking into account the specificity of academic knowledge creation. This Chapter summarizes efforts done in two cooperating communities in Japan and in Poland, including attempts to clarify the concept and the field of knowledge science, to create an ontology characterizing a research program in this field, then to apply related results in another field – contemporary telecommunications. The distinctive approach to ontology creation (see Ren at al. 2008) is based on a combination of bottom-up and top-down approaches with the purpose of combining explicit knowledge with tacit, intuitive and experiential knowledge for constructing an ontology. Other possible views on constructing ontology are also presented and discussed; lessons from an ongoing application of this approach to a local research community working on contemporary telecommunication issues in Poland are also discussed. The combination of explicit and tacit, intuitive and experiential knowledge has led to a development of a software system named adaptive hermeneutic agent (AHA), a toolkit for documents gathering, keywords extracting, keywords clustering, and ontology visualization.

The principles of sustainability imply the joint consideration of economical, social and environmental criteria in every decisional process. Electricity is, of course, a basic need of our modern society, but the production processes in the Spanish region of Andalucia have traditionally been aggressive for the environment, due to the high number of plants using fossil fuels (mainly coal and petrol) some decades ago, and combined cycle plants that work with natural gas (more recently). On the other hand, the alternative renewable sources (eolic, solar, hydraulic...), being more respectful for the environment, are usually much more expensive. In such a framework, multiple criteria decision making techniques can be extremely helpful. This talk reports the use of interactive multiobjective methods to determine the most adequate electrical mix for Andalucia. This study was financed by the Regional Ministry of Environment, and we have considered eight different electricity generation techniques (comprising non renewable and renewable sources). As for the criteria, we have used the yearly costs and the vulnerability (dependence on imported fuels) as economical-strategic criteria, and the environmental issues have been addressed through the consideration of twelve impact categories which have in turn been assessed using a life cycle analysis scheme. The problem is to decide the percentage of the electricity to be produced by each of the eight generation techniques. This problem has been solved using the interactive multiobjective programming package PROMOIN. This package allows the simultaneous use of different interactive multiobjective techniques, in such a way the the Decision Maker (DM) can change the type of information he wishes to provide at each iteration (local tradeoffs or weights, reference points, choosing a solution among several ones...), and the interactive procedures is changed accordingly. This provides a flexible resolution framework, which was highly appreciated by the DMs. This talk reports both the modeling of the problem, and the resolution process that was carried out.

Applied optimization problems such as process design or optimal control are multi - criteria problems in essence. It is important to construct feasible solution set, but in case these problems are combined with the use of nonlinear models, generation of reliable Pareto front can be difficult. A case study in process design is used to illustrate the multi-step procedure for generating Pareto front for a two criteria problem. The base of this procedure is high-dimensional data analysis and visualization techniques. The results show that the use of data analysis and visualization can help gain insight into the Pareto optimal. Another case study in practical optimal control problem from biotechnology is used for comparison of several multi-criteria optimization methods . Two criteria are taken into account: yield of biomass and natural process duration. Theoretical analysis of the problem is difficult because of non-linearity of the process’ model. The problem is reduced to a parametric optimization problem by means of parameterization of control functions. Several evolutionary multi-criteria optimization algorithms and a scalarization based direct search algorithms are considered. The methods are compared with respect to the precision and the solution time.