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In this work, we introduce an iterative algorithm for the Euclidean distance matrix completion (EDMC) problem with noisy and incomplete distance measurements. The proposed method is based on semidefinite programming, utilizes a Pareto iterative approach, and performs a projection-free convex optimization over the spectrahedron to solve a level-set problem relevant to EDMC problems. The optimality trade-off between the trace of a positive semidefinite matrix and a loss function is pursued over Pareto optimal points with simple, derivative-free, costly efficient nonlinear equation root finding iterations called Illinois-type methods. We evaluate our approach numerically in a scenario where distance measurements are affected by multiplicative noise.

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactionsvs. instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA-seq and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

This page contains data and resources associated with "Trade-off drives Pareto optimality of within- and among-year emergence timing in response to increasing aridity" by Joseph Waterton, Susan J. Mazer, Justin R. Meyer and Elsa E. Cleland.

Made available in DSpace on 2021-06-25T10:18:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-05-01 The deployment of battery energy storage systems (BESS) is rapidly increasing as a prominent option to support future renewable-based energy systems. However, despite its benefits from a technical perspective, there are still challenges related to its economic viability. On the other hand, sizing BESS considering only their economic viability can be impractical because financial objectives could be in conflict with other aspects, such as battery degradation and grid impact. This article proposes a multi-objective approach to determine the optimal size of BESS providing stackable services, such as frequency regulation and peak shaving. The proposed optimization method comprises financial and technical aspects represented by the payback period, battery life span, and grid impact. Given a set of market rules, a cost-benefit function, a regulation signal, consumption profiles and grid data, an enumerative approach is adopted to provide a set of Pareto optimal solutions. The performance of the proposed method is validated using the regulation market structure from PJM interconnection. Furthermore, a real 240-node distribution grid is used to assess the grid impact via OpenDSS. Simulations demonstrate that the proposed approach is a flexible and practical decision-making tool that investors can exploit when designing new BESS. Department of Energy Systems School of Electrical and Computing of Engineering University of Campinas Department of Electrical Engineering Center for Electric and Energy Technical University of Denmark School of Energy Engineering São Paulo State University (UNESP) School of Energy Engineering São Paulo State University (UNESP)

Adaptation to current and future climates can be constrained by trade‐offs between fitness‐related traits. Early seedling emergence often enhances plant fitness in seasonal environments, but if earlier emergence in response to seasonal cues is genetically correlated with lower potential to spread emergence among years (i.e., bet‐hedging), then this functional trade‐off could constrain adaptive evolution. Consequently, selection favoring both earlier within‐year emergence and greater spread of emergence among years—as is expected in more arid environments—may constrain adaptive responses to trait value combinations at which a performance gain in either function (i.e., evolving earlier within‐ or greater among‐year emergence) generates a performance loss in the other. All such trait value combinations that cannot be improved for both functions simultaneously are described as Pareto optimal and together constitute the Pareto front. To investigate how this potential emergence timing trade‐off might constrain adaptation to increasing aridity, we sourced seeds of two grasses, Stipa pulchra and Bromus diandrus, from multiple maternal lines within populations across an aridity gradient in California and examined their performance in a greenhouse experiment. We monitored emergence and assayed ungerminated seeds for viability to determine seed persistence, a metric of potential among‐year emergence spread. In both species, maternal lines with larger fractions of persistent seeds emerged later, indicating a trade‐off between within‐year emergence speed and potential among‐year emergence spread. In both species, populations on the Pareto front for both earlier emergence and larger seed persistence fraction occupied significantly more arid sites than populations off the Pareto front, consistent with the hypothesis that more arid sites impose the strongest selection for earlier within‐year emergence and greater among‐year emergence spread. Our results provide an example of how evaluating genetically based correlations within populations and applying Pareto optimality among populations can be used to detect evolutionary constraints and adaptation across environmental gradients.

Based on the AMSZ (1989) criterion, we exploit comprehensive datasets to estimate the dynamic efficiency of world economy. The results reveal that the representative econo-mies conform to a "U-shaped pattern" in their evolution of capital accumulation. That is, a period of decreasing efficiency (over-accumulation) followed by increasing efficiency (de-accumulation). Contrary to previous evidence, the bias-corrected estimates show that major economies have been inconsistently dynamically efficient. As a prime example, China today is unquestionably in a state of severe dynamic inefficiency, and the inefficient status is likely to continue in near future. We also document the limitations of the AMSZ criterion and point out promising research directions in the efficiency literature.

A discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW) is proposed to solve the multiobjective Flexible Job-shop Scheduling Problem. The algorithm uses a two-layer coding structure to encode the chromosomes, namely operation sequence (OS) and machine assignment (MA). The initial population combined random selection of OS and the global selection based on operation (GSO) of MA. In order to obtain the Pareto optimal solution, non-dominated fronts are obtained by rapid non-dominated sorting. In the evolution process, the discrete particle swarm optimization algorithm is used to directly solve the values of the next generation chromosomes in the discrete domain, and the population diversity is enhanced by adaptively adjusting the variation of the inertia weight $\omega $ , and the Pareto optimal solution obtained in the process is stored in the Pareto optimal solution set (POS). Finally, numerical simulation based on two sets of international standard instances and comparisons with some existing algorithms are carried out. The comparative results demonstrate the effectiveness and practicability of the proposed DPSO-AIW in solving the multiobjective Flexible Job-shop Scheduling Problem.

11th International Conference on Algorithms and Complexity ( 2019: Rome; Italy ), Hedonic games are a prominent model of coalition formation, in which each agent’s utility only depends on the coalition she resides. The subclass of hedonic games that models the formation of general partnerships [21], where output is shared equally among affiliates, is called hedonic games with common ranking property (HGCRP). Aside from their economic motivation, HGCRP came into prominence since they are guaranteed to have core stable solutions that can be found efficiently [2]. Nonetheless, a core stable solution is not necessarily a socially desirable (Pareto optimal) outcome. We improve upon existing results by proving that every instance of HGCRP has a solution that is both Pareto optimal and core stable. We establish that finding such a solution is, however, - by proving the stronger statement that finding any Pareto optimal solution is. We show that the gap between the total utility of a core stable solution and that of the socially optimal solution (OPT) is bounded by |N|, where N is the set of agents, and that this bound is tight. Our investigations reveal that finding a solution, whose total utility is within a constant factor of that of OPT, is intractable. © 2019, Springer Nature Switzerland AG.

The article suggests a modification for numerical fireworks method of the single-objective optimization for solving the problem of multiobjective optimization. The method is metaheuristic. It does not guarantee finding the exact solution, but can give a good approximate result. Multiobjective optimization problem is considered with numerical criteria of equal importance. A possible solution to the problem is a vector of real numbers. Each component of the vector of a possible solution belongs to a certain segment. The optimal solution of the problem is considered a Pareto optimal solution. Because the set of Pareto optimal solutions can be infinite; we consider a method for finding an approximation consisting of a finite number of Pareto optimal solutions. The modification is based on the procedure of non-dominated sorting. It is the main procedure for solutions search. Non-dominated sorting is the ranking of decisions based on the values of the numerical vector obtained using the criteria. Solutions are divided into disjoint subsets. The first subset is the Pareto optimal solutions, the second subset is the Pareto optimal solutions if the first subset is not taken into account, and the last subset is the Pareto optimal solutions if the rest subsets are not taken into account. After such a partition, the decision is made to create new solutions. The method was tested on well-known bi-objective optimization problems: ZDT2, LZ01. Structure of the location of Pareto optimal solutions differs for the problems. LZ01 have complex structure of Pareto optimal solutions. In conclusion, the question of future research and the issue of modifying the method for problems with general constraints are discussed.

Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO 2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used ɛ-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.

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. Evolutionary algorithms (EAs) have shown to be efficient in dealing with many-objective optimization problems (MaOPs) due to their ability to obtain a set of compromising solutions which not only converge toward the Pareto front (PF), but also distribute well. The Pareto-based multi-objective evolutionary algorithms are valid for solving optimization problems with two and three objectives. Nevertheless, when they encounter many-objective problems, they lose their effectiveness due to the weakening of selection pressure based on the Pareto dominance relation. Our major purpose is to develop more effective diversity maintenance mechanisms which cover convergence besides dominance in order to enhance the Pareto-based many-objective evolutionary algorithms. In this paper, we propose a Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation, abbreviated as SPSAT. The space partitioning selection increases selection pressure and maintains diversity simultaneously, which we realize through firstly dividing the normalized objective space into many subspaces and then selecting only one individual with the best proximity estimation value in each subspace. To further enhance convergence and diversity, the angle-based truncation calculates the angle values of any pair of individuals in the critical layer and then gradually removes the individuals with the minimum angle values. From the comparative experimental results with six state-of-the-art algorithms on a series of well-defined optimization problems with up to 20 objectives, the proposed algorithm shows its competitiveness in solving many-objective optimization problems.

This paper investigates the Pareto optimal strategy of discrete-time stochastic systems under H∞ constraint, in which the weighting matrices of the weighted sum cost function can be indefinite. Combining the H∞ control theory with the indefinite LQ control theory, the generalized difference Riccati equations (GDREs) are obtained. By means of the solution of the GDREs, the Pareto optimal strategy with H∞ constraint is derived, and the necessary and sufficient conditions for the existence of the strategy are presented. Then the Pareto optimal solution under the worst-case disturbance is solved. Finally, the efficiency of the obtained results is illustrated by a numerical example.

Adaptation to current and future climates can be constrained by trade-offs between fitness-related traits. Early seedling emergence often enhances plant fitness in seasonal environments, but if earlier emergence in response to seasonal cues is genetically correlated with lower potential to spread emergence among years (i.e., bet-hedging), then this functional trade-off could constrain adaptive evolution. Consequently, selection favoring both earlier within-year emergence and greater spread of emergence among years-as is expected in more arid environments-may constrain adaptive responses to trait value combinations at which a performance gain in either function (i.e., evolving earlier within- or greater among-year emergence) generates a performance loss in the other. All such trait value combinations that cannot be improved for both functions simultaneously are described as Pareto optimal and together constitute the Pareto front. To investigate how this potential emergence timing trade-off might constrain adaptation to increasing aridity, we sourced seeds of two grasses, Stipa pulchra and Bromus diandrus, from multiple maternal lines within populations across an aridity gradient in California and examined their performance in a greenhouse experiment. We monitored emergence and assayed ungerminated seeds for viability to determine seed persistence, a metric of potential among-year emergence spread. In both species, maternal lines with larger fractions of persistent seeds emerged later, indicating a trade-off between within-year emergence speed and potential among-year emergence spread. In both species, populations on the Pareto front for both earlier emergence and larger seed persistence fraction occupied significantly more arid sites than populations off the Pareto front, consistent with the hypothesis that more arid sites impose the strongest selection for earlier within-year emergence and greater among-year emergence spread. Our results provide an example of how evaluating genetically based correlations within populations and applying Pareto optimality among populations can be used to detect evolutionary constraints and adaptation across environmental gradients.

Social norms are a set of rules to be followed by the people of a community in order to have a better coexistence, to which the behaviors, tasks, and activities of the human being must be adjusted. The set or system of norms, rules, or duties regulates the actions of individuals among themselves. This work presents a new and original approach to the situations of agreement as well as to the constructions of regulations. This is done by giving a mathematical formalization to the set of all possible agreements or regulations, so that, then, the proximity between them is defined by means of a premetric. Thanks to this mathematical structure that tries to capture the problematic of agreements and modifications of regulations, some currently issues related to game theory or law are now reduced to mathematical optimization problems.

A hot Markovian system can cool down faster than a colder one: this is known as the Mpemba effect. Here, we show that a non-equilibrium driving via stochastic reset can induce this phenomenon, when absent. Moreover, we derive an optimal driving protocol simultaneously optimizing the appearance time of the Mpemba effect, and the total energy dissipation into the environment, revealing the existence of a Pareto front. Building upon previous experimental results, our findings open up the avenue of possible experimental realizations of optimal cooling protocols in Markovian systems., Comment: Accepted Manuscript in New Journal of Physics (in press, 2021)

Starting from the model in Koch-Vargiolu (2019), we test the real impact of current renewable installed power in the electricity price in Italy, and assess how much the renewable installation strategy which was put in place in Italy deviated from the optimal one obtained from the model in the period 2012--2018. To do so, we consider the Ornstein-Uhlenbeck (O-U) process, including an exogenous increasing process influencing the mean reverting term, which is interpreted as the current renewable installed power. Using real data of electricity price, photovoltaic and wind energy production from the six main Italian price zones, we estimate the parameters of the model and obtain quantitative results, such as the production of photovoltaic energy impacts the North zone, while wind is significant for Sardinia and the Central North zone does not present electricity price impact. Then we implement the solution of the singular optimal control problem of installing renewable power production devices, in order to maximize the profit of selling the produced energy in the market net of installation costs. We extend the results of \cite{KV} to the case when no impact on power price is presented, and to the case when $N$ players can produce electricity by installing renewable power plants. We are thus able to describe the optimal strategy and compare it with the real installation strategy that was put in place in Italy.

RESUMEN: La optimización multicriterio surge cuando se desea optimizar más de una función objetivo a la vez. Esto ocurre, por ejemplo, a la hora de planificar inversiones donde se quiere obtener el mayor rendimiento posible, asumiendo el menor riesgo. Sin embargo, estos dos objetivos se contraponen, pues maximizar el rendimiento suele llevar implícito un aumento del riesgo de la inversión. En este trabajo proponemos una doble vertiente: teórica y práctica. En cuanto a la teórica, definiremos el problema de optimización multicriterio y presentaremos algunos resultados de existencia de solución y las condiciones de optimalidad para ese problema, siendo un concepto clave la solución de Pareto. Refiriéndonos ahora a la práctica, motivados por el modelo mono-objetivo para carteras de inversión de Harry Markowitz, el problema de Media-Varianza [7], consideraremos un modelo multicriterio. Por último, se presentarán algunos resultados obtenidos con Matlab para tres bases de datos, empleando la técnica de la ventana deslizante para la última de ellas. ABSTRACT: Multicriteria optimization is a process whereby we wish to optimise more than one objective function simultaneously. This occurs, for example, when planning investments whereby we want to achieve the highest level of return, for each unit of risk. Nevertheless, these two objectives are opposite because maximizing the return usually implies an increase of the investment’s risk. In this paper, we propose a two strand approach: theory and practise. Beginning with the theoretical strand, we define the multicriteria optimization problem and present the optimality conditions for that problem, including the key Pareto-solution concept. Regarding now the practical strand, motivated by the mono-objective optimization model for portfolio investments of Harry Markowitz, the Mean-Variance problem [7], we consider a multicriteria model. Finally, we show some results obtained by Matlab for three databases, using the sliding window technique for the last one. Grado en Matemáticas

In construction projects, owners’ and contractors’ decisions as to whether or not share their tacit knowledge transparently with each other determines the choice of project delivery and contracting methods and their collaborative behaviors in the projects. The dynamic of collaboration is a complex one and is often influenced by the other player’s intent to collaborate. This paper proposes a quantitative framework using game theory to analyze the collaborative dynamic interplay in construction projects. The framework demonstrates that when an owner’s and a contractor’s knowledge leveragability (L) is very high and synergy (S) is enhanced as a result of knowledge sharing in complex construction projects, the Nash equilibrium state lies beneath the Pareto frontier. This means that sharing tacit knowledge transparently about budgets by choosing the best value procurement with GMP or IPD contracts can promote collaboration and provide better payoffs to both stakeholders than not sharing the knowledge by choosing the Design–Bid–Build method using a low-bid procurement in conjunction with a fixed-price contract. This research contributes to the body of knowledge by proposing a theoretical framework that enables both owners and contractors to understand the economics of collaboration in the context of complex projects.

In the context of the paradigms founding the Quality by Design and Process Analytical Technology initiatives, the work herein presents a computational approach to support the decision-making process, in particular, about the feasibility of a product defined for some a priori given quality characteristics. The approach is based on the computation of the pareto-optimal front when simultaneously minimizing the expected differences between the predicted and the desired characteristics. Thus, the feasibility is tackled as an optimization problem with the novelty of doing so simultaneously for all the characteristics, preserving the correlation structure, but by separately handling each individual characteristic. With data from a low-density polyethylene production process, with fourteen process variables and five measured characteristics of the final polyethylene, solutions are found to define the Design Space for targeted quality characteristics on the product, and without the need of explicitly inverting the PLS (Partial Least Squares) prediction model fitted to the process., Junta de Castilla y León (BU012P17), and also Spanish MINECO and Agencia Estatal de Investigación under research projects CTQ2014-53157-R, and CTQ2017-88894-R

Ethanol is among the largest fermentation product used worldwide, accounting for more than $$90\%$$ of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to $$ 19.74 \, \hbox {mmol} \, \hbox {gDW}^{-1} \, \hbox {h}^{-1} $$ ( $$+\,832.88 \% $$ with respect to wild-type) and biomass production of $$0.02 \, \hbox {h}^{-1}$$ ( $$-\,98.06 \% $$ ). The analyses on E. coli highlighted a single knockout strategy producing $$16.49 \, \hbox {mmol} \, \hbox {gDW}^{-1} \, \hbox {h}^{-1} $$ ( $$+\,679.29 \%$$ ) ethanol, with biomass equals to $$0.23 \, \hbox {h}^{-1}$$ ( $$-\,77.45 \% $$ ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is $$+\,195.24\%$$ .

A constrained multi-objective optimization approach is applied to optimize the exergy efficiency and material costs of thermocline packed-bed thermal-energy storage systems using air as the heat-transfer fluid. The axisymmetric packed-bed’s height, top and bottom radii, insulation-layer thicknesses, and particle diameter were chosen as design variables. The competing objectives of maximizing the exergy efficiency and minimizing the material costs were treated by a Pareto front. The Pareto front allows identifying the most efficient design for a given cost or the cheapest design for a given efficiency and is an important tool to find the best overall design of storage systems for a specific application. Constraints were imposed to obtain storage systems with specified capacities and limits on the air outflow temperatures during charging and discharging. The results showed that a storage shaped as a truncated cone with the smallest cross-section at the top has a higher exergy efficiency than storages shaped as cylinders or truncated cones with the largest cross-section at the top. The higher efficiency is attributed to the axial temperature distribution in the packed bed and the associated conduction heat losses across the insulated walls. The optimization of an industrial-scale storage allowed identifying a design with an exergy efficiency that was only 4.8% below that of the most efficient design, but a cost that was 81.3% lower than the cost of the most efficient design. Compared to brute-force design approaches, the optimization procedure can reduce the computational time by 91–99%. ISSN:0306-2619 ISSN:1872-9118

In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in this algorithm obtains its Pareto local optimization. After the assembly of local optimizations, the algorithm generates the Pareto global optimization for one server plan. The final solution to our problem is the optimal one among all server plans. Experimental results show the good performance of the proposed method. Comparing with the widely-used Round robin scheduling method, the proposed method requires only 45.5% dynamic energy cost.

In data centers, subject to workloads with heterogeneous (and sometimes short) lifetimes, workload migration is a way of attaining a more efficient utilization of the underlying physical machines. To not introduce performance degradation, such workload migration must take into account not only machine resources, and per-task resource requirements, but also application dependencies in terms of network communication. This paper presents a workload migration model capturing all of these constraints. A linear programming framework is developed allowing accurate representation of per-task resources requirements and inter-task network demands. Using this, a multi-objective problem is formulated to compute a re-allocation of tasks that (1) maximizes the total inter-task throughput, while (2) minimizing the cost incurred by migration and (3) allocating the maximum number of new tasks. A baseline algorithm, solving this multi-objective problem using the $$\varepsilon$$ -constraint method is proposed, in order to generate the set of Pareto-optimal solutions. As this algorithm is compute-intensive for large topologies, a heuristic, which computes an approximation of the Pareto front, is then developed, and evaluated on different topologies and with different machine load factors. These evaluations show that the heuristic can provide close-to-optimal solutions, while reducing the solving time by one to two order of magnitudes.

To counteract the performance degradation in electromagnetic devices due to mechanical tolerances, assembly inaccuracies, poor knowledge of material parameters, etc., many robust design techniques have been proposed. A first class of methods takes advantage from statistical analysis to improve the average performance taking into account actual, randomly distributed values rather than the nominal figure of the parameters design values. A second class, making use of the "sensitivity" of device performance with respect to different parameters, looks for solutions as insensitive as possible with respect to possible modifications of the design parameters. In this paper, with the aim of presenting a synthetic selection of existing methods, a brief survey of the different approaches is presented. A simple test case is considered for demonstration purposes.

The subject of this research work is to stabilize the network TCP (transmission control protocol) as well as the queue of the router congestion by designing an Active Queue Management scheme able to ensure this role. The problem is dealt with under the theory of the command by using a tuned PID (proportional–integral–derivative) controller based on an extension of the Hermite–Biehler theorem applied to quasi-polynomials. This tuning approach uses Hurwitz stability concept, that is to say, a sufficient and necessary condition must be given so the roots of the quasi-polynomial lie in the left half plane. Since this stabilization method gives rise to a set of values for parameters ‘P’, ‘I’ and ‘D’, it turns out relevant to optimize the results achieved within this stability region. To achieve this purpose, a multi-criterion Ant Colony Optimization based on Pareto optimality is used, given the conflictual character of the closed-loop system performance parameters. The set of optimal solutions of the problem is given by determining the Pareto front of objective functions. The effectiveness of the proposed control scheme is evaluated via a series of numerical simulations in MATLAB and SIMULINK. The results are compared with those of the genetic algorithm and Ziegler–Nichols methods.

Real-world optimization problems may involve a number of computationally expensive functions with a large number of input variables. Metamodel-based optimization methods can reduce the computational costs of evaluating expensive functions, but this does not reduce the dimension of the search domain nor mitigate the curse of dimensionality effects. The dimension of the search domain can be reduced by functional anova decomposition involving Sobol' sensitivity indices. This approach allows one to rank decision variables according to their impact on the objective function values. On the basis of the sparsity of effects principle, typically only a small number of decision variables significantly affects an objective function. Therefore, neglecting the variables with the smallest impact should lead to an acceptably accurate and simpler metamodel for the original problem. This appealing strategy has been applied only to single-objective optimization problems so far. Given a high-dimensional optimization problem with multiple objectives, a method called anova-mop is proposed for defining a number of independent low-dimensional subproblems with a smaller number of objectives. The method allows one to define approximated solutions for the original problem by suitably combining the solutions of the subproblems. The quality of the approximated solutions and both practical and theoretical aspects related to decision making are discussed. peerReviewed

Variable ordering heuristic plays a central role in solving constraint satisfaction problems. Many heuristics have been proposed and well-studied. In order to take advantage of the fact that many generic variable ordering heuristics work well for different problems, we propose a novel method in this paper, namely ParetoHeu , to combine variable ordering heuristics. At each node of the search tree, a set of candidate variables is generated by a new strategy based on Pareto optimality and a variable is selected from the set randomly. The method is easy to be implemented in constraint solvers. The experiments on various benchmark problems show that ParetoHeu is more efficient than both the participant heuristics which are popular in constraint solvers. It is also more robust than some classical strategies which have been used to combine variable ordering heuristics.

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. Achieving balance between convergence and diversity is a challenge in many-objective optimization problems (MaOPs). Many-objective evolutionary algorithms (MaOEAs) based on dominance and decomposition have been developed successfully for solving partial MaOPs. However, when the optimization problem has a complicated Pareto front (PF), these algorithms show poor versatility in MaOPs. To address this challenge, this paper proposes a co-guided evolutionary algorithm by combining the merits of dominance and decomposition. An elitism mechanism based on cascading sort is exploited to balance the convergence and diversity of the evolutionary process. At the same time, a reference point adaptation method is designed to adapt to different PFs. The performance of our proposed method is validated and compared with seven state-of-the-art algorithms on 200 instances of 27 widely employed benchmark problems. Experimental results fully demonstrate the superiority and versatility of our proposed method on MaOPs with regular and irregular PFs.

Background Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. Methods We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. Results We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. Conclusions These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment. Graphical abstract

We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. We are motivated by practical applicability and focus on two main challenges faced by practitioners in industry: 1) meaningful formulation of the optimization problem reflecting the needs of a decision maker and 2) finding a desirable solution based on a decision maker’s preferences when solving a problem with computationally expensive function evaluations. For the first challenge, we describe the procedure of modelling a component in the air intake ventilation system with commercial simulation tools. The problem to be solved involves time consuming computational fluid dynamics simulations. Therefore, for the second challenge, we extend a recently proposed Kriging-assisted evolutionary algorithm K-RVEA to incorporate a decision maker’s preferences. Our numerical results indicate efficiency in using the computing resources available and the solutions obtained reflect the decision maker’s preferences well. Actually, two of the solutions dominate the baseline design (the design provided by the decision maker before the optimization process). The decision maker was satisfied with the results and eventually selected one as the final solution. peerReviewed

We tackle the problem of partitioning players into groups of fixed size, such as allocating eligible students to shared dormitory rooms. Each student submits preferences over the other individual students. We study several settings, which differ in the size of the rooms to be filled, the orderedness or completeness of the preferences, and the way of calculating the value of a coalition---based on the best or worst roommate in the coalition. In all cases, we determine the complexity of deciding the existence, and then finding a Pareto optimal assignment, and the complexity of verifying Pareto optimality for a given assignment., Comment: appeared at MATCH-UP 2019

We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. We forecast demand with a Bayesian model, which is based on sales data. After identifying relevant objectives relying on the demand model, we formulate an optimisation problem to determine lot sizes for multiple future time periods. Our approach combines different interactive multi-objective optimisation methods for finding the best balance among the objectives. For that, a decision maker with substance knowledge directs the solution process with one’s preference information to find the most preferred solution with acceptable trade-offs. As a proof of concept, to demonstrate the benefits of the approach, we utilise real-world data from a production company and compare the optimised lot sizes to decisions made without support. With our approach, the decision maker obtained very satisfactory solutions. peerReviewed

Coupled models of the soil-vegetation-atmosphere systems are increasingly used to investigate interactions between the system components. Due to the different spatial and temporal scales of relevant processes and computational restrictions, the atmospheric model generally has a lower spatial resolution than the land surface and subsurface models. We employ multi-objective Genetic Programming (MOGP) using the Strength Pareto Evolutionary Algorithm (SPEA) to bridge this scale gap. We generate high-resolution atmospheric fields using the coarse atmospheric model output and high-resolution land surface information (e.g., topography) as predictors. High-resolution atmospheric simulations serve as reference. It is impossible to perfectly reconstruct the reference fields with the available information. Thus, we simultaneously optimize the root mean square error (RMSE) and two objective functions quantifying spatial variability. Minimization solely with respect to the RMSE provides too smooth high-resolution fields. Additional objectives help to recover spatial variability. We apply MOGP to the downscaling of 10 m temperature. Our approach reproduces a larger part of the variability and is applicable for a wider range of weather conditions than a linear regression based downscaling. Original publication: T. Zerenner, V. Venema, P. Friederichs, and C. Simmer. Downscaling near-surface atmospheric fields with multiobjective Genetic Programming. Environmental Modelling and Software, 84(2016), 85--98.

Designing cluster trials depends on the knowledge of the intracluster correlation coefficient. To overcome the issue of parameter dependence, Bayesian designs are proposed for two level models with and without covariates. These designs minimize the variance of the treatment contrast under certain cost constraints. A pseudo Bayesian design approach is advocated that integrates and averages the objective function over a prior distribution of the intracluster correlation coefficient. Theoretical results on the Bayesian criterion are noted when the intracluster correlation follows a uniform distribution. Two data sets based on educational surveys conducted in schools are used to illustrate the proposed methodology. (C) 2016 Elsevier B.V. All rights reserved.

The ranking of multi-metric scientific achievements is a challenging task. For example, the scientific ranking of researchers utilizes two major types of indicators, namely, number of publications and citations. In fact, they focus on how to select proper indicators, considering only one indicator or combination of them. The majority of ranking methods combine several indicators, but these methods are faced with a challenging concern&mdash, the assignment of suitable/optimal weights to the targeted indicators. Pareto optimality is defined as a measure of efficiency in the multi-objective optimization which seeks the optimal solutions by considering multiple criteria/objectives simultaneously. The performance of the basic Pareto dominance depth ranking strategy decreases by increasing the number of criteria (generally speaking, when it is more than three criteria). In this paper, a new, modified Pareto dominance depth ranking strategy is proposed which uses some dominance metrics obtained from the basic Pareto dominance depth ranking and some sorted statistical metrics to rank the scientific achievements. It attempts to find the clusters of compared data by using all of indicators simultaneously. Furthermore, we apply the proposed method to address the multi-source ranking resolution problem which is very common these days, for example, there are several world-wide institutions which rank the world&rsquo, s universities every year, but their rankings are not consistent. As our case studies, the proposed method was used to rank several scientific datasets (i.e., researchers, universities, and countries) for proof of concept.

von Neumann and Morgenstern, while introducing games in extensive form in their book [1], also supplied a method for transforming such games into normal form. Once more in their book [1], the same authors provided a method for transforming games from characteristic function form into normal form, although limited to constant-sum games. In his paper [2], Gambarelli proposed a generalization of this method to variable-sum games. In this generalization, the strategies are the requests made by players to join any coalition, with each player making the same request to all coalitions. Each player’s payment consists of the player’s request multiplied by the probability that the player is part of a coalition really formed. Gambarelli introduced a solution for games in characteristic function form, made up of the set of Pareto-Optimal payoffs generated by Nash Equilibria of the transformed game.

Economists recommend to partly redistribute gains to losers from a structural reform, which in many cases may be required for making the reform politically viable. However, taxation is distortionary. Then, it is unclear that compensatory transfers can support a Pareto-improving reform. This paper provides sufficient conditions for this to occur, despite tax distortions. I consider an economy where workers have sector-specific skills and some sectors are regulated by a price floor. Transfers have to be financed by proportional taxation on firms revenues or, equivalently, labor income. Labor supply is elastic to net post-tax real wages, and hence reduced by taxation. In a setting where preferences are isoelastic, deregulation is implementable in a Pareto- improving way through compensatory lump-sum transfers, despite that these are financed by distortionary taxes. In a more general setting, there always exist Pareto-improving reforms but they may involved tightening regulation for some goods. I provide sufficient conditions for deregulation, i.e. a general reduction in price floors, to be Pareto-improving. They imply that demand cross-price elasticities should not be too large and that the reform should not be too unbalanced. Finally, I consider counter-examples where some people earn rents associated with informational or institutional frictions. In such situations, Pareto improvements are unlikely. If losers have veto power, the reform may only be supported by a minority of people. Broadening reform scope is especially useful to raise its political support when its impact is uneven across consumers.

International audience We consider a pure exchange overlapping generations economy with a varying number of commodities and consumers per period having possibly non-complete non transitive preferences. We provide a geometric and direct proof of the Balasko-Shell characterization of Pareto optimal allocations. As a by-product, we compute an explicit Pareto improving transfer when the criterion is not satisfied, which is minimal for some suitable distance.

The concept of green logistics stems from green economic concepts which are inherently driven by the environmental sustainability challenges. In this work, measures of carbon dioxide (CO2) emission are added to the canonical capacitated vehicle routing problem. The proposed multi-objective optimization model tackles the conflicting objectives of the emission reduction while holding-off the economic cost uplift, leading to a set of Pareto optimal solutions. A biologically inspired Ant Colony Optimization (ACO) based evolutionary constructive heuristic is used to obtain routing plans with minimum financial impact. A Variable Neighborhood Search (VNS) algorithm is designed to obtain low emission routes by exploring the neighborhood of the ant foraging paths. The hybrid ACO-VNS heuristic will provide a set of non-dominated solutions leading to the Pareto optimal solution frontier. For consistency of solutions and solution convergence, the algorithm is tested on randomly generated problem instances.

In this article, we explore how pooling of economic risks is dealt with in insurance and reinsurance. Consider an average individual. Instead of facing risks in isolation, the individual may sort out a larger group of people with similar risks, and together work out a scheme how to share these. This should be done so that everyone comes out better than, or at least as good as, standing alone. In the article, I try to explain, by several examples, how pooling works in insurance. Pension insurance is our first example, where we point out how how pooling takes place along several different dimensions. A major concept is the mutuality principle, which we relate to both pension insurance and the theory of syndicates. We present some examples outside the field of insurance, with the same basic features, that of pooling individual risks. The last aspect of pooling that we discuss is best explained in the framework of information economics. This we do in a section about adverse selection. Keywords: pension insurance; reinsurance exchange; adverse selection

Routing in wireless multihop networks (WMHNs) relies on a delicate balance of diverse and often conflicting parameters, when aiming for maximizing the WMHN performance. Classified as a non-deterministic polynomial-time hard problem, routing in WMHNs requires sophisticated methods. As a benefit of observing numerous variables in parallel, quantum computing offers a promising range of algorithms for complexity reduction by exploiting the principle of quantum parallelism (QP), while achieving the optimum full-search-based performance. In fact, the so-called non-dominated quantum optimization (NDQO) algorithm has been proposed for addressing the multiobjective routing problem with the goal of achieving a near-optimal performance, while imposing a complexity of the order of $O(N)$ and $O(N\sqrt {N})$ in the best and worst case scenarios, respectively. However, as the number of nodes in the WMHN increases, the total number of routes increases exponentially, making its employment infeasible despite the complexity reduction offered. Therefore, we propose a novel optimal quantum-assisted algorithm, namely, the non-dominated quantum iterative optimization (NDQIO) algorithm, which exploits the synergy between the hardware and the QP for the sake of achieving a further complexity reduction, which is on the order of $O(\sqrt {N})$ and $O(N\sqrt {N})$ in the best and worst case scenarios, respectively. In addition, we provide simulation results for demonstrating that our NDQIO algorithm achieves an average complexity reduction of almost an order of magnitude compared with the near-optimal NDQO algorithm, while having the same order of power consumption.

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively, The authors acknowledge funding from Spanish MINECO (and the European Regional Development Fund) project SYNBIOFACTORY (Grant No. DPI2014-55276-C5-2-R)

We consider rate allocation for video users in the case where the available bandwidth fluctuates. Simply minimizing the objective distortion or optimizing the stability of video qualities does not optimize subjective quality. We formulate a utility-based solution, considering that a user's preference of video quality often varies over a range with upper and lower thresholds of quality. Our iterative pricing-based resource allocation procedure reallocates the bandwidth not only between different users within a time slot but also between different time slots, such that no user suffers quality degradation on average by participating in the multiplexing process. Experimental results show that, compared with equal resource allocation and existing rate allocation solutions, the subjective result becomes increasingly better with the increase of bandwidth fluctuation rate or bandwidth fluctuation range. Moreover, as the number of users increases, the results improve.

none 2 no Topological persistence has proven to be a promising framework for dealing with problems concerning the analysis of data. In this context, it was originally introduced by taking into account 1-dimensional properties of data, modeled by real-valued functions. More recently, topological persistence has been generalized to consider multidimensional properties of data, coded by vector-valued functions. This extension enables the study of multidimensional persistent Betti numbers, which provide a representation of data based on the properties under examination. In this contribution, we establish a new link between multidimensional topological persistence and Pareto optimality, proving that discontinuities of multidimensional persistent Betti numbers are necessarily pseudocritical or special values of the considered functions. none Andrea Cerri;Patrizio Frosini Andrea Cerri;Patrizio Frosini

The article investigates Wicksell's change of mind about the machinery question between 1890 and 1900/1901. Wicksell at first sided with the so-called “compensation theory” that workers are not harmed by the introduction of machinery. In his lecture notes of April 1900, made available here for the first time, Wicksell deployed marginal productivity theory to discuss the effects of labour-saving technical progress, with inconclusive results. Finally, in his published 1901 Lectures and in his 1900 article about marginal productivity, Wicksell claimed that the introduction of machinery increases output and reduces wages. His analysis was based on the demonstration that free competition maximizes output, but not total utility. It is argued that Wicksell's change of mind and his mature formulation of the machinery question result from his critical assessment of the then new concept of Pareto optimality, together with his reinterpretation and rejection of Ricardo's (1821) contention that the introduction of machinery may diminish output and employment. O artigo investiga a mudança de opinião de Wicksell sobre a questão da maquinaria entre 1890 e 1900/1901. Wicksell inicialmente se aliou à “teoria da compensação” de que os trabalhadores não são prejudicados pela introdução de maquinaria. Em suas notas de aula de abril de 1900, tornadas disponíveis aqui pela primeira vez, Wicksell empregou a teoria da produtividade marginal para tratar dos efeitos do progresso técnico poupador de trabalho, com resultados inconclusivos. Finalmente, em suas Lectures de 1901, Wicksell argumentou que a introdução de maquinaria aumenta o produto a diminui salários. Sua análise se baseou na demonstração de que a livre concorrência maximiza o produto, mas não a utilidade total. O artigo mostra que a mudança de opinião de Wicksell e sua formulação madura da questão da maquinaria resultaram de sua avaliação crítica do então novo conceito de ótimo de Pareto, juntamente com sua reinterpretação e rejeição do ponto de Ricardo (1821) de que a introdução de maquinaria pode diminuir produto e emprego.

Supply Chain Management (SCM) has become part of the c-management agenda in Western countries since the 1990s, particularly in the manufacturing and retail industries (Chopra and Meindl 2012). More recently, interest in SCM has also been growing in the agrifood industry, both in developed and developing countries. Executives of agrifood companies are aware that successful coordination, integration, and management of key business processes in the supply chain will determine their competitive success. Sustainable Food Supply Chain Management (SFSCM) refers to all forward processes in the food chain, like procurement of materials, production and distribution, as well as the reverse processes to collect and process returned used or unused products and/or parts of products in order to ensure a socioeconomically and ecologically sustainable recovery (Bloemhof and van Nunen 2008).

Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modelled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. We introduce a biobjective mixed integer problem, for which Pareto optimal solutions are obtained. Those Pareto optimal solutions correspond to different classification rules, among which the user would choose the one yielding the most appropriate compromise between the cost and the expected misclassification rate. Ministerio de Ciencia y Tecnología Plan Andaluz de Investigación

Determining the geographic connections between breeding and nonbreeding populations, termed migratory connectivity, is critical to advancing our understanding of the ecology and conservation of migratory species. Assignment models based on stable isotopes historically have been an important tool for studying migratory connectivity of small‐bodied species, but the low resolution of these assignments has generated interest into combining isotopes with other sources in information. Abundance is one of the most appealing data sources to include in isotope‐based assignments, but there are currently no statistical methods or guidelines for optimizing the contribution of stable isotopes and abundance for inferring migratory connectivity. Using known‐origin stable‐hydrogen isotope samples of six Neotropical migratory bird species, we rigorously assessed the performance of assignment models that differentially weight the contribution of the isotope and abundance data. For two species with adequate sample sizes, we used Pareto optimality to determine the set of models that simultaneously minimized both assignment error rate and assignment area. We then assessed the ability of the top models from these two species to improve assignments of the remaining four species compared to assignments based on isotopes alone. We show that the increased precision of models that include abundance is often offset by a large increase in assignment error. However, models that optimally weigh the abundance data relative to the isotope data can result in higher precision and, in some cases, lower error than models based on isotopes alone. The top models, however, depended on the distribution of relative breeding abundance, with patchier distributions requiring stronger downweighting of abundance, and we present general guidelines for future studies. These results confirm that breeding abundance can be an important source of information for studies investigating broad‐scale movements of migratory birds and potentially other taxa.

This thesis tackles a series of phenomena across diverse fields from the point of view of optimization. The problems studied often bring together diverse conflicting constraints that cannot be fully satisfied. Out of these conflicts emerges a notable complexity. Pareto optimality theory deals with the optimization of simultaneous targets and was consequently used as a preferred framework. In this thesis, it is shown how mathematical properties of Pareto optimal designs affect very diverse complex systems in universal ways. The implications for the different phenomena studied are discussed. Especial attention was paid to the optimization of complex networks, the optimality of communication codes and human language, and the tradeoff between complex biological structures versus the replication efficiency required by Darwinian dynamics., Esta tesis estudia una serie de fenómenos de diferentes campos desde el punto de vista de la optimización. Los problemas estudiados suelen imponer a sus soluciones una serie de requisitos que se contradicen entre sí, y que, por lo tanto, no pueden ser del todo satisfechos. De este tipo de conflictos emerge una complejidad notable. La teoría de Pareto optimalidad trata, en efecto, sobre la optimización de varios objetivos contradictorios. Este es el marco teórico seleccionado para estudiar la mayoría de los problemas. En esta tesis se muestra cómo las propiedades matemáticas de los diseños Pareto óptimos afectan de manera universal a sistemas complejos muy diversos. Las implicaciones en cada fenómeno concreto son discutidas. Se presta especial atención a la optimización de redes complejas, a las características óptimas de posibles códigos de comunicación, y al compromiso que emerge entre estructuras biológicas complejas frente a la eficiencia en la replicación impuesta por la selección Darwiniana.

An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory Pareto optimal solution can be found for her or his. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the role of the decision maker is very important in interactive methods, methods presented are classified according to the type of preference information that the decision maker is assumed to provide. peerReviewed