OpenAlex 运筹优化方向近两年代表论文标题、作者与摘要

… field optimization is given from the point of view of operations research. Reservoir equations for a simple reservoir system are derived and discretized and included in optimization …

… mainly deals with optimization algorithms, while the second is more oriented to optimization and … Wilppu, presents new algorithms of nonconvex multiobjective optimization based on the …

… Many optimization problems of practical as well as theoretical importance consist of the … Among the latter ones we find a class of problems called Combinatorial Optimization (CO) …

… In the remaining chapters we shall indicate some strategies to cope with NP-hard combinatorial optimization problems. Here approximation algorithms must be mentioned in the first …

… with a series of discrete optimization models associated to the … The combinatorial optimization models described in the … We shall describe below a simple combinatorial optimization …

… 5, hybrid stochastic gradient procedures are very important tools for the iterative solution of stochastic optimization problems as discussed in Chaps. 1–4: … In many practical …

… Stochastic optimization algorithms have been growing … optimization problems.This chapter provides a synopsis of some of the critical issues associatedwith stochastic optimization and a …

This chapter summarizes the theoretical fundamentals and materials of stochastic optimization methods. Many of classical optimization methods are classified into local optimization methods, which can often quickly find a local minimum. Local optimization algorithms, e.g., nonlinear programming and local search, usually search the neighborhood of a trial point sequentially to find a local solution. In structural optimization, response functions do not have convexity or special analytical form in many cases, and hence application of an algorithm leads to a local solution that most likely is not the global solution. The theory of probability describes and predicts statistics of mass phenomena. The chapter summarizes random search (RS) methods. RS algorithm has been studied extensively for knowledge discovery, estimation of average and worst computational costs of an algorithm, and finding an approximate optimal solution of a combinatorial problem.

… stochastic modeling problems and optimization algorithms than we have been able to in our lectures, as stochastic optimization … and online convex optimization provide complementary …

… We review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, …

Abstract Stochastic optimization is an umbrella term that includes over a dozen fragmented communities, using a patchwork of sometimes overlapping notational systems with algorithmic strategies that are suited to specific classes of problems. This paper reviews the canonical models of these communities, and proposes a universal modeling framework that encompasses all of these competing approaches. At the heart is an objective function that optimizes over policies that is standard in some approaches, but foreign to others. We then identify four meta-classes of policies that encompasses all of the approaches that we have identified in the research literature or industry practice. In the process, we observe that any adaptive learning algorithm, whether it is derivative-based or derivative-free, is a form of policy that can be tuned to optimize either the cumulative reward (similar to multi-armed bandit problems) or final reward (as is used in ranking and selection or stochastic search). We argue that the principles of bandit problems, long a niche community, should become a core dimension of mainstream stochastic optimization.

… , stochastic optimization methods, randomized and distributed methods, nonconvex stochastic optimization … We solve for each case using our presumed stochastic optimization algorithm …

… stochastic problem. We summarize that a stochastic optimization model is based upon … We emphasize that the basic assumption in stochastic optimization is that the distribution of is …

… Observe that this class of stochastic programs is rich enough to model stochastic problems with scenario-dependent recourse (that is, stage II) costs. All the stochastic optimization …

… in stochastic calculus. The second part of the book is on the stochastic optimization methods … In Chapter 4 we study the Bellman equation of stochastic control problems; a set of sufficient …

Significance Sensitivity of optimization algorithms to problem and algorithmic parameters leads to tremendous waste in time and energy, especially in applications with millions of parameters, such as deep learning. We address this by developing stochastic optimization methods demonstrably—both by theory and by experimental evidence—more robust, enjoying optimal convergence guarantees for a variety of stochastic optimization problems. Additionally, we highlight the importance of method sensitivity to problem difficulty and algorithmic parameters. Standard stochastic optimization methods are brittle, sensitive to stepsize choice and other algorithmic parameters, and they exhibit instability outside of well-behaved families of objectives. To address these challenges, we investigate models for stochastic optimization and learning problems that exhibit better robustness to problem families and algorithmic parameters. With appropriately accurate models—which we call the aprox family—stochastic methods can be made stable, provably convergent, and asymptotically optimal; even modeling that the objective is nonnegative is sufficient for this stability. We extend these results beyond convexity to weakly convex objectives, which include compositions of convex losses with smooth functions common in modern machine learning. We highlight the importance of robustness and accurate modeling with experimental evaluation of convergence time and algorithm sensitivity.

In this chapter, we study the application of some exemplary algorithms to the traveling salesman problem (TSP) in which the information about all or many previously visited …

We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to gradient-based distributed optimization algorithms where a master node performs parameter updates while worker nodes compute stochastic gradients based on local information in parallel, which may give rise to delays due to asynchrony. We take motivation from statistical problems where the size of the data is so large that it cannot fit on one computer; with the advent of huge datasets in biology, astronomy, and the internet, such problems are now common. Our main contribution is to show that for smooth stochastic problems, the delays are asymptotically negligible and we can achieve order-optimal convergence results. We show n-node architectures whose optimization error in stochastic problems-in spite of asynchronous delays-scales asymptotically as O(1/√nT) after T iterations. This rate is known to be optimal for a distributed system with n nodes even in the absence of delays. We additionally complement our theoretical results with numerical experiments on a logistic regression task.

Optimization models have been widely used in the power … deterministic approaches to stochastic optimization for unit … and computational aspects of stochastic optimization (SO) …

… mixed integer program is an optimization problem in which a nonempty subset of integer … After presenting several practical applications of mixed integer programming, we describe …

… We begin by discussing basic mixed-integer programming formulation principles and tricks, … to solve mixed-integer programs. We illustrate the use of mixed-integer programming in the …

… 18.1 Linear Programming The focus of this chapter is on computational mixed-integer programming. However, advances in computational linear programming are a fundamental part of …

The first 50 years of Integer and Mixed-Integer Programming have taken us to a very stable paradigm for solving problems in a reliable and effective way. We run over these 50 exciting …

… 309–325, 2004) provided an analysis of the performance impact of the main mixed integer programming features and improvements up to CPLEX 8.0 for a workshop in honor of …

… Abstract This paper reports on the fifth version of the Mixed Integer Programming Library. … and from industry, all of whom work in integer programming. There was mutual consent that the …

This survey presents cutting planes that are useful or potentially useful in solving mixed integer programs. Valid inequalities for (i) general integer programs, (ii) problems with local …

… • We first solve the relaxed program in which the integer variables for periods 1 to 4 are not … integer variables at their current values; • We resolve the program where now the integer …

For many of us, modern-day linear programming (LP) started with the work of George Dantzig in 1947. However, it must be said that many other scientists have also made seminal contributions to the subject, and some would argue that the origins of LP predate Dantzig’s contribution. It is matter open to debate [36]. However, what is not open to debate is Dantzig’s key contribution to LP computation. In contrast to the economists of his time, Dantzig viewed LP not just as a qualitative tool in the analysis of economic phenomena, but as a method that could be used to compute actual answers to specific real-world problems. Consistent with that view, he proposed an algorithm for solving LPs, the simplex algorithm [12]. To this day the simplex algorithm remains a primary computational tool in linear and mixed-integer programming (MIP). In [11] it is reported that the first application of Dantzig’s simplex algorithm to the solution of a non-trivial LP was Laderman’s solution of a 21 constraint, 77 variable instance of the classical Stigler Diet Problem [41]. It is reported that the total computation time was 120 man-days! The first computer implementation of an at-least modestly general version of the simplex algorithm is reported to have been on the SEAC computer at the then National Bureau of Standards [25]. (There were apparently some slightly earlier implementations for dealing with models that were “triangular”, that is, where all the linear systems could be solved by simple addition and subtraction.) Orchard-Hays [35] reports that several small instances having as many as 10 constraints and 20 variables were solved with this implementation. The first systematic development of computer codes for the simplex algo-rithm began very shortly thereafter at the RAND Corporation in Santa Mon-ica, California. Dantzig’s initial LP work occurred at the Air Force following

Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method for the aggregation problem. This method aims to minimise changes of classes and to create compact shapes, subject to hard constraints ensuring aggregates of sufficient size for the target scale. To quantify class changes we apply a semantic distance measure. We give a graph theoretical problem formulation and prove that the problem is NP-hard, meaning that we cannot hope to find an efficient algorithm. Instead, we present a solution by mixed-integer programming that can be used to optimally solve small instances with existing optimisation software. In order to process large datasets, we introduce specialised heuristics that allow certain variables to be eliminated in advance and a problem instance to be decomposed into independent sub-instances. We tested our method for a dataset of the official German topographic database ATKIS with input scale 1:50,000 and output scale 1:250,000. For small instances, we compare results of this approach with optimal solutions that were obtained without heuristics. We compare results for large instances with those of an existing iterative algorithm and an alternative optimisation approach by simulated annealing. These tests allow us to conclude that, with the defined heuristics, our optimisation method yields high-quality results for large datasets in modest time.

… This paper presents a mixed-integer programming formulation to find optimal solutions for the block layout problem with unequal departmental areas arranged in flexible bays. The …

Although tracing linear structures in 2D images and 3D image stacks has received much attention over the years, full automation remains elusive. In this paper, we formulate the delineation problem as one of solving a Quadratic Mixed Integer Program (Q-MIP) in a graph of potential paths, which can be done optimally up to a very small tolerance. We further propose a novel approach to weighting these paths, which results in a Q-MIP solution that accurately matches the ground truth. We demonstrate that our approach outperforms a state-of-the-art technique based on the k-Minimum Spanning Tree formulation on a 2D dataset of aerial images and a 3D dataset of confocal microscopy stacks.

… In this paper, a mixed integer programming model for solving the short term planning problem in surface mining is presented. This model will establish the sequence of mining for a …

… the solution of a mixed integer program. A tolerance range is associated with each measurement and an estimate is chosen to maximize the number of estimated measurements that …

Multilayer\noptical films have been extensively used in optical\ntechnology, but the design of multilayer structures for broadband\napplications is often challenging due to the need to incorporate material\ndispersion. Here, we present an implementation of a memetic algorithm\nbased on mixed integer programming, which is especially suited for\npractical broadband optimization of layered thin-film materials. In\nour implementation, the optimization variables consist of a list of\ndiscrete variables that represents different dielectric materials,\nalong with a list of continuous variables that represents the thicknesses\nof each material. As a set of concrete demonstrations, we optimize\nthe spectra of a radiative cooling device and an incandescent light\nbulb filter. The resulting structures from the optimization can, by\nusing more materials, achieve better performance than their counterparts\nin the literature while using fewer numbers of material layers.

… Another future potential research area is highlighted in this study; that is, scalability of metaheuristic methods for solving optimization problems with dimensions greater than 1000. …

… review of the meta-heuristic optimization methods that have … in collecting the data (meta-heuristic, optimization, algorithm, … out which version of optimization methods performs better in …

Metaheuristic algorithms are an intelligent way of thinking and working developed for resolving diverse issues about optimization. The number of potential solutions for such problems often is too large to be properly analyzed using standard procedures; thus, these algorithms are highly flexible and can be useful in many cases where needed to predict different types of optimizations accurately. Metaheuristics take inspiration from several natural processes like evolution or animal behavior, which allow them to show strength without being specific only towards one area. Some Metaheuristics algorithms are commonly being used like : Genetic Algorithm (GA), Simulated Annealing (SA), Evolutionary Algorithm (EA), Tabu Search (TS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Cuckoo Search Approach (CSA). All of them derives from this initial set of solutions and employ heuristics to get from this set of solutions.. The objective of this paper is to thoroughly analyze different metaheuristic algorithms. Their principles, mechanisms and the area where they are applied and will delve into. This paper provides a qualitative analysis of these algorithmic performances in diverse settings that underscore their strong suits as well as their weaknesses. The discourse also makes mention of some specific examples like how metaheuristic algorithms find utility application in various fields which include but are not limited to engineering or computer science, even economics and healthcare later down the line receive due consideration with an eye towards specific results; showing not only how effective these individual algorithms can be when applied under differing scenarios but also pointing out areas deserving further research efforts be directed onto them.

… optimization tasks is hampered. To close this gap, this paper presents a framework that is tailored to meta-heuristic optimization … Since the subtasks cannot be optimized separately due …

… This paper develops a new metaheuristic optimization algorithm named K-means Optimizer (KO) to solve a wide range of optimization problems from numerical functions to real-design …

Optimization is the process of searching optimal solution of complex problems more efficiently. There is a class of problems which is called NP (nondeterministic polynomial) problem which means that solution of NP problem based on metaheuristic approach may be verified in polynomial time which might take exponential number of steps to find the solution. To solve such problems, we may use special type of algorithm which is called Meta Heuristic (MA’s) algorithm. MA’s are able to provide near optimal solution by introducing randomization technique with deterministic approach. In this paper, we have discussed some meta heuristic algorithms and their applications in research areas.

Abstract Problem Metaheuristics are efficient algorithms designed to address a broad spectrum of optimization challenges and offer satisfactory solutions, even in scenarios of limited processing capability or incomplete information. It has been observed that no single metaheuristic algorithm is universally ideal for all applications. This realization underscores the opportunity for the introduction of new metaheuristic algorithms or enhancements to existing ones. Aim The aim of this work is to propose Quokka swarm optimization (QSO), a novel nature-inspired metaheuristic optimization technique. The QSO simulates the cooperative behavior of quokka animals, which can be used to address optimization issues. Method A group of common unconstrained and constrained test functions is employed to demonstrate the strength of the proposed approach. To test the performance of QSO, 43 popular test functions that are used in the optimization were employed as benchmarks. The solutions have been refining their positions in tandem with the ongoing discovery of the best solution. In addition, QSO can substitute the worst quokka with the best child found so far to improve the solutions. Performance comparisons using the Blue monkey swarm optimization, Gray wolf optimization, Biogeography-based optimizer, Artificial bee colony, Particle swarm optimization, and Gravitational search algorithm were also performed. Results The obtained results showed that QSO is competitive in comparison to the chosen metaheuristic algorithms.

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA’s robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems.

… are called multi-objective optimization problems and may … most important concepts of multi-objective optimization and a … the main applied multi-objective optimization algorithms and …

Algorithms for approximating the nondominated set of multiobjective optimization problems are reviewed. The approaches are categorized into general methods that are applicable under mild assumptions and, thus, to a wide range of problems, and into algorithms that are specifically tailored to structured problems. All in all, this survey covers 52 articles published within the last 41 years, that is, between 1979 and 2020. Summary of Contribution: In many problems in operations research, several conflicting objective functions have to be optimized simultaneously, and one is interested in finding Pareto optimal solutions. Because of the high complexity of finding Pareto optimal solutions and their usually very large number, however, the exact solution of such multiobjective problems is often very difficult, which motivates the study of approximation algorithms for multiobjective optimization problems. This research area uses techniques and methods from algorithmics and computing in order to efficiently determine approximate solutions to many well-known multiobjective problems from operations research. Even though approximation algorithms for multiobjective optimization problems have been investigated for more than 40 years and more than 50 research articles have been published on this topic, this paper provides the first survey of this important area at the intersection of computing and operations research.

… In a multiobjective optimization problem, we aim to find the set of optimal tradeoff solutions known as the Pareto optimal set. Pareto optimality is defined with respect to the concept of …

… In this paper, the biobjective AWS method is extended to multiobjective optimization … method are not suitable for higher dimensional multiobjective optimization. The reason is that the …

Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for DMO, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), nonmonotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature. A test suite of ten instances with different dynamic features is produced from the generator in this paper. Additionally, a few new performance measures are proposed to evaluate algorithms for DMOPs with different characteristics. Six representative multiobjective evolutionary algorithms from the literature are investigated based on the proposed DMO test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for DMOPs.

… This article provides a short introduction to the evolutionary multiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary …

… multiobjective optimization. Over the last two decades, much effort has been dedicated to developing evolutionary multiobjective optimization (… definitions of multiobjective optimization. …

… The main contribution of this paper is to present a single surrogate, multi-objective optimization approach. This approach is the first one, to the best of our knowledge, where a single …

… makes a multiobjective optimization problem (MOP) difficult for multiobjective evolutionary … In this scheme, single objective optimization problems are clustered into several groups. To …