The clinical translation of polysarcosine (pSar) as polyethylene glycol (PEG) replacement in the development of novel nanomedicines creates a broad demand of polymeric material in high‐quality making ...high‐purity sarcosine N‐carboxyanhydride (Sar‐NCA) as monomer for its production inevitable. Within this report, we present the use of triethyloxonium tetrafluoroborate in Sar‐NCA synthesis with focus on amino acid and chloride impurities to avoid the sublimation of Sar‐NCAs. With a view towards upscaling into kilogram or ton scale, a new methodology of monomer purification is introduced by utilizing the Meerwein's Salt triethyloxonium tetrafluoroborate to remove chloride impurities by covalent binding and converting chloride ions into volatile products within a single step. The novel straightforward technique enables access to monomers with significantly reduced chloride content (<100 ppm) compared to Sar‐NCA derived by synthesis or sublimation. The derived monomers enable the controlled‐living polymerization in DMF and provide access to pSar polymers with Poisson‐like molecular weight distribution within a high range of chain lengths (Xn 25–200). In conclusion, the reported method can be easily applied to Sar‐NCA synthesis or purification of commercially available pSar‐NCAs and eases access to well‐defined hetero‐telechelic pSar polymers.
The use of triethyloxonium tetrafluoroborate enables efficient chloride removal during sarcosine N‐carboxyanhydride synthesis allowing the controlled amine‐initiated ring opening polymerization. This approach can avoid sublimation or other intensive purification of NCAs and NNCAs easing the synthesis of polypeptides, polypeptoids, and polypeptp(o)ides.
In this paper we solve a practical variant of an arc routing problem. We target the close enough model in which clients can be served from relatively close arcs. This variant, known as the profitable ...close-enough arc routing problem, models real situations, such as inventory management or automated meter reading. We propose a heuristic based on the variable neighborhood search methodology to maximize the sum of profits of the clients served (penalized with the distance traveled). Our method incorporates efficient search strategies to speed up the optimization process, as required in practical applications. We present extensive experimentation over a benchmark of previously reported instances. Specifically, we first set the key search parameters of our method, and then compare it with the state-of-the-art heuristics for this problem. Our heuristic outperforms the previous algorithms published for this problem, as confirmed by the statistical analysis, which permits to draw significant conclusions.
•A literature overview on close-enough is carried out.•A MultiStart-VNS algorithm is proposed and adapted to solve the PCEARP.•The experimental results proved the efficiency of the proposed algorithm.•The metaheuristic algorithm outperforms other existing methods for the problem.•The method has a great potential to be applied in emerging real-life problems.
The min–max edge crossing problem (MMECP) is a challenging and important problem arising in integrated-circuit design, information visualization, and software engineering. Drawing edges as straight ...lines in accordance with the hierarchical graph drawing standard, the goal is to reduce the maximum number of edge crossings in graphs. In this study, we propose a fast path relinking (FPR) method based on dynamic-programming local search to tackle the MMECP, where an efficient neighborhood reduction mechanism is employed to evaluate only the so-called critical vertices instead of all the vertices. Moreover, the proposed FPR can simultaneously manage a number of neighborhood moves at each search iteration, which is significantly different from all the previous approaches based on one neighborhood in the literature. Extensive computational experiments on MMECP instances show that our proposed FPR approach is relatively competitive compared to the best-performing heuristics and the optimization Gurobi solver. In particular, our algorithm improved the best-known solutions for 104 of the 301 publicly available benchmark instances. Additional experiments were conducted to elucidate the key elements and search parameters of the proposed FPR. Furthermore, we made the source code of the algorithm publicly available to facilitate its use in real applications and future research.
•A path relinking method manages only two individuals to achieve a better balance.•A neighborhood reduction strategy only evaluates the critical vertices.•A dynamic programming mechanism simultaneously manages several neighborhood moves.•The conducted experiments show the efficiency of the proposed algorithm.
Scatter search is an evolutionary method that, unlike genetic algorithms, operates on a small set of solutions and makes only limited use of randomization as a proxy for diversification when ...searching for a globally optimal solution. The scatter search framework is flexible, allowing the development of alternative implementations with varying degrees of sophistication. In this paper, we test the merit of several scatter search designs in the context of global optimization of multimodal functions. We compare these designs among themselves and choose one to compare against a well-known genetic algorithm that has been specifically developed for this class of problems. The testing is performed on a set of benchmark multimodal functions with known global minima. PUBLICATION ABSTRACT
•First comparative analysis of diversity models in binary optimization.•Study of geometrical structure of the solutions of each model.•Comparison between dispersion and representativeness in terms of ...diversity.•Introduction of a new bi-level model to overcome the existing limitations.
Maximum diversity problems arise in many practical settings from facility location to social networks, and constitute an important class of NP-hard problems in combinatorial optimization. There has been a growing interest in these problems in recent years, and different mathematical programming models have been proposed to capture the notion of diversity. They basically consist of selecting a subset of elements of a given set in such a way that a measure based on their pairwise distances is maximized to achieve dispersion or representativeness. In this paper, we perform an exhaustive comparison of four mathematical models to achieve diversity over the public domain library MDPLIB, studying the structure of the solutions obtained with each of them. We extend this library by including new Euclidean instances which permit to analyze the geometrical distribution of the solutions. Our study concludes which models are better suited for dispersion and which ones for representativeness in terms of the structure of their solutions, as well as which instances are difficult to solve. We also identify in our conclusions one of the models which is not recommended in any setting. We finalize by proposing two improvements, one related to the models and one to solving methods. The computational testing shows the value of the analysis and merit of our proposals.
The algorithm described here, called OptQuest/NLP or OQNLP, is a heuristic designed to find global optima for pure and mixed integer nonlinear problems with many constraints and variables, where all ...problem functions are differentiable with respect to the continuous variables. It uses OptQuest, a commercial implementation of scatter search developed by OptTek Systems, Inc., to provide starting points for any gradient-based local solver for nonlinear programming (NLP) problems. This solver seeks a local solution from a subset of these points, holding discrete variables fixed. The procedure is motivated by our desire to combine the superior accuracy and feasibility-seeking behavior of gradient-based local NLP solvers with the global optimization abilities of OptQuest. Computational results include 155 smooth NLP and mixed integer nonlinear program (MINLP) problems due to Floudas et al. (1999), most with both linear and nonlinear constraints, coded in the GAMS modeling language. Some are quite large for global optimization, with over 100 variables and 100 constraints. Global solutions to almost all problems are found in a small number of local solver calls, often one or two.
In the last years, many areas in science, business, and engineering have experienced an enormous growth in the amount of data that they are required to analyze. In many cases, this analysis relies ...intimately on data visualization and, as a result, graph drawing has emerged as a new field of research. This paper addresses the challenge of drawing hierarchical graphs, which is one of the most widely used drawing standards. We introduce a new mathematical model to automatically represent a graph based on the alignment of long arcs, which we combine with the classic arc crossing minimization objective in hierarchical drawings. We complement our proposal with a heuristic algorithm that can obtain high-quality results in the short computational time required by graph drawing systems. Our algorithm joins two methodologies, tabu search and strategic oscillation (SOS), to perform a fast and effective exploration of the search space. We conduct extensive experimentation that integrates our new mathematical programming formulation and the SOS tabu search that targets large instances. Our statistical analysis confirms the effectiveness of this proposal.
In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), ...simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for ‘agile’ optimisation via parallel computing, and learnheuristics (combination of statistical/machine learning with metaheuristics) to deal with
NP-hard
and large-scale optimisation problems in areas such as transport and logistics, manufacturing and production, smart cities, telecommunication networks, finance and insurance, sustainable energy consumption, health care, military and defence, e-marketing, or bioinformatics. The manuscript provides the main related concepts and updated references that illustrate the applications of these hybrid optimisation–simulation–learning approaches in solving rich and real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits that these approaches can offer across different application fields. Finally, this work concludes by highlighting open research lines on the combination of these methodologies to extend the concept of simulation-based optimisation.
Fifty years of metaheuristics Martí, Rafael; Sevaux, Marc; Sörensen, Kenneth
European journal of operational research,
4/2024
Journal Article
Recenzirano
Odprti dostop
In this paper, we review the milestones in the development of heuristic methods for optimization over the last 50 years. We propose a critical analysis of the main findings and contributions, mainly ...from a European perspective. Starting with the roots of the area that can be traced back to the classical philosophers, we follow the historical path of heuristics and metaheuristics in the field of operations research and list the main milestones, up to the latest proposals to hybridize metaheuristics with machine learning. We pay special attention to the theories that changed our way of thinking about problem solving, and to the role played by the European Journal of Operational Research in the development of these theories. Our approach emphasizes methodologies and their connections with related areas, which permits to identify potential lines of future research.
•A review in the development of heuristic methods over the last 50 years.•The historical path of heuristics in Operations Research with the main milestones.•From heuristics to metaheuristics that changed our view on problem solving.•The role of EJOR in the metaheuristic literature.•The future of heuristic optimization.
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