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Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.


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Proceedings ArticleDOI
01 May 2017
TL;DR: In this article, a modified EO algorithm combining exact mathematical model on CC stage and a heuristic algorithm on HR stage is proposed to solve the scheduling problem with less computational effort, and has been successfully applied to a real plant.
Abstract: Making an integrated schedule on both continues casting (CC) and hot rolling (HR) process in steel-making lines is a difficult problem since the balance of material flow, the continuity of production, the hot charge rate as well as the coupling between successive stages should be taken into consideration together. Therefore, few approaches have been published to solve the multistage integrated scheduling problem and applied to the real systems in practice. In this study, a novel modified Extremal Optimization (EO) algorithm combining exact mathematical model on CC stage and a heuristic algorithm on HR stage is proposed to solve the scheduling problem with less computational effort, and has been successfully applied to a real plant. The industrial application results show that compared with manual scheduling, considerable improvement in hot charge rate is achieved, leading to large energy-saving for the steel maker.

2 citations

07 Nov 2012
TL;DR: Results indicate that sandpile model can be applicable to image segmentation and Extremal Optimization algorithm is a general-purpose local search heuristic that is based on SOC.
Abstract: The sandpile model is a paradigm of self organizing critically (SOC) concept that is inspired by a physics-based intuition for optimization. In this paradigm, physical properties of sandpiles such as avalanche promise improved convergence and lower computations. Extremal Optimization (EO) algorithm is a general-purpose local search heuristic that is based on SOC. Here, application of sandpile model to image segmentation is proposed. In the proposed model, oversegmented images are submitted to the algorithm. Inspired by sandpile model, similar segments then merge, and by using the energy function in Markov random fields (MRF), EO adjusts the labels of pixels. Results indicate that sandpile model can be applicable to image segmentation.

2 citations

Dissertation
01 Jan 1978
TL;DR: This thesis was produced using the GATE text editing system on the Computati: bnal and.
Abstract: F II / Best Copy Available ACKNOWLEDGEMENTS This thesis owes its birth to Austin Tate, its growth to Robert Ross, and its completion to Brian Boffey. Particular thanks are due to them. I am grateful for access to computing services at Edinburgh University and the Edinburgh Regional Computing Centre. Further computing facilities were provided by the Liverpool _-University Computer Laboratory. This thesis was produced using the GATE text editing system on the Computati: bnal and. Statistical Science departmental computer system at Liverpool University. Many of the diagrams and tables were prepared by Miss«, M. Ross. Other secretarial services were provided by Liverpool University. CONTENTS Chapter 1 Introduction 1 1.1 Motivation and structure 1 1.2 The 1#-dimensional trim-loss problem 3 1.3 The 2-dimensional trim-loss problem 4

2 citations

01 Jan 2007
TL;DR: The results suggest that genetic algorithms can both enrich and benefit from probabilistic modeling, reinforcement learning, ant colony optimization or other similar algorithms using values to encode preferences for parameter assignments.
Abstract: This paper generalizes our research on parameter interdependencies in reinforcement learning algorithms for optimization problem solving. This generalization expands the work to a larger class of search algorithms that use explicit search statistics to form feasible solutions. Our results suggest that genetic algorithms can both enrich and benefit from probabilistic modeling, reinforcement learning, ant colony optimization or other similar algorithms using values to encode preferences for parameter assignments. The approach is shown to be effective on both the Asymmetric Traveling Salesman and the Quadratic Assignment Problems.

1 citations

Book ChapterDOI
01 Jan 2016
TL;DR: The ant colony algorithm is used for solving the travelling salesman problem of the real set of data and getting the optimal results on graphs and the results show that this algorithm can efficiently find the optimal path of the hundred cities with minimum time and cost.
Abstract: Ant colony optimisation is a population-based advanced approach for finding the solution of difficult problems with the help of a bioinspired approach from the behaviour of natural ants. The ant colony algorithm is a propelled optimisation method which is utilised to take care of combinatorial optimisation problems. The significant features of this algorithm are the utilisation of a mixture of preinformation and postinformation for organizing great solutions. The ant colony algorithm is used in this paper for solving the travelling salesman problem of the real set of data and getting the optimal results on graphs. This algorithm is an meta-heuristic algorithm in which we used the 2-opt local search method for tour construction and roulette wheel selection method for selection of nodes while constructing the route. The results show that this algorithm can efficiently find the optimal path of the hundred cities with minimum time and cost.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
202213
20217
20209
201922
201815