Journal ArticleDOI
Scheduling flow shops using differential evolution algorithm
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TLDR
The novel method requires few control variables, is relatively easy to implement and use, effective, and efficient, which makes it an attractive and widely applicable approach for solving practical engineering problems.About:
This article is published in European Journal of Operational Research.The article was published on 2006-06-01. It has received 324 citations till now. The article focuses on the topics: Meta-optimization & Flow shop scheduling.read more
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Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Journal ArticleDOI
Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
TL;DR: The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models.
Journal ArticleDOI
The distributed permutation flowshop scheduling problem
Bahman Naderi,Rubén Ruiz +1 more
TL;DR: The DPFSP is characterized and six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance are proposed.
Journal ArticleDOI
Differential Evolution: A review of more than two decades of research
TL;DR: The journey of Differential Evolution is shown through its basic aspects like population generation, mutation schemes, crossover schemes, variation in parameters and hybridized variants along with various successful applications of DE.
Journal ArticleDOI
A discrete differential evolution algorithm for the permutation flowshop scheduling problem
TL;DR: In this paper, the authors proposed an iterated greedy algorithm for the permutation flowshop scheduling problem with the makespan criterion and a referenced local search procedure to further improve the solution quality.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Journal ArticleDOI
Tabu Search—Part II
TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more