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Optimization problem

About: Optimization problem is a(n) research topic. Over the lifetime, 96493 publication(s) have been published within this topic receiving 2123990 citation(s). The topic is also known as: optimisation problem. more


Journal ArticleDOI: 10.1126/SCIENCE.220.4598.671
13 May 1983-Science
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods. more

Topics: Optimization problem (61%), Continuous optimization (61%), Extremal optimization (59%) more

38,868 Citations

Open accessBook
01 Jan 2001-
Abstract: From the Publisher: Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study. more

Topics: Evolutionary computation (64%), Evolutionary algorithm (62%), Evolutionary programming (61%) more

11,886 Citations

Journal ArticleDOI: 10.1109/3477.484436
01 Feb 1996-
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS. more

Topics: Metaheuristic (66%), Ant colony optimization algorithms (64%), Extremal optimization (63%) more

10,378 Citations

Open accessJournal ArticleDOI: 10.1109/4235.585893
Abstract: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori "head-to-head" minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems' enforcing of a type of uniformity over all algorithms. more

8,548 Citations

Journal ArticleDOI: 10.1080/10556789908805766
Jos F. Sturm1Institutions (1)
Abstract: SeDuMi is an add-on for MATLAB, which lets you solve optimization problems with linear, quadratic and semidefiniteness constraints. It is possible to have complex valued data and variables in SeDuMi. Moreover, large scale optimization problems are solved efficiently, by exploiting sparsity. This paper describes how to work with this toolbox. more

7,286 Citations

No. of papers in the topic in previous years

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Topic's top 5 most impactful authors

Kalyanmoy Deb

225 papers, 36.6K citations

Carlos A. Coello Coello

140 papers, 3.8K citations

Derrick Wing Kwan Ng

139 papers, 7.5K citations

Robert Schober

98 papers, 5.3K citations

Lorenz T. Biegler

89 papers, 5K citations

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