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Journal ArticleDOI

A simulated annealing approach to the solution of minlp problems

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TLDR
The M-SIMPSA algorithm, which does not require feasible initial points or any problem decomposition, was tested with several functions published in the literature, and results were compared with those obtained with a robust adaptive random search method.
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This article is published in Computers & Chemical Engineering.The article was published on 1997-01-01. It has received 166 citations till now. The article focuses on the topics: Random search & Simulated annealing.

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A real coded genetic algorithm for solving integer and mixed integer optimization problems

TL;DR: The proposed MI-LXPM is a suitably modified and extended version of the real coded genetic algorithm, LXPM, of Deep and Thakur and incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints.
Journal ArticleDOI

Global optimization of mixed‐integer nonlinear problems

TL;DR: Two novel deterministic global optimization algorithms for nonconvex mixed-integer problems (MINLPs) are proposed, using the advances of the αBB algorithm for non Convex NLPs of Adjiman et al.
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Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems

TL;DR: A comparison is made between an algorithm based on Simulated Annealing and two Evolutionary Algorithms: Genetic Al algorithms (GAs) and Evolution Strategies (ESs), which seem a valid approach to the optimization of non-linear problems.
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A test-suite of non-convex constrained optimization problems from the real-world and some baseline results

TL;DR: A set of 57 real-world Constrained Optimization Problems are described and presented as a benchmark suite to validate the COPs and reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.
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An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints

TL;DR: An improved particle swarm optimization (PSO) algorithm was developed for solving non-convex NLP/MINLP problem with equality and/or inequality constraints and demonstrates its advantages in applicability and efficiency.
References
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Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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Equation of state calculations by fast computing machines

TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
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A simplex method for function minimization

TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
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