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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.


Papers
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Journal Article
TL;DR: An extremal genetic algorithm to deal with the QoS multicast routing with characteristics of the networks, a suit of special encode, crossover and mutation is used to guarantee the feasibility of the solution.
Abstract: Based on genetic algorithm and the extremal optimization idea,this paper propose an extremal genetic algorithm to deal with the QoS multicast routing.The non-equilibrium of extremal optimization can avoid to local minimums and accelerate convergence.According to the characteristics of the networks,a suit of special encode,crossover and mutation is used to guarantee the feasibility of the solution.Simulation results indicate that this algorithm has better performances on both speed and precision.

1 citations

Book ChapterDOI
16 May 2017
TL;DR: This paper adopts a different, purely theoretical approach, which is based on characterizing the search space into subspaces and analyzing the worst-case performance of a MOCO algorithm in terms of the expected number of calls to the underlying constraint solver, and applies this approach to two important constraint-based M OCO algorithms.
Abstract: In a multi-objective combinatorial optimization (MOCO) problem, multiple objectives must be optimized simultaneously. In past years, several constraint-based algorithms have been proposed for finding Pareto-optimal solutions to MOCO problems that rely on repeated calls to a constraint solver. Understanding the properties of these algorithms and analyzing their performance is an important problem. Previous work has focused on empirical evaluations on benchmark instances. Such evaluations, while important, have their limitations. Our paper adopts a different, purely theoretical approach, which is based on characterizing the search space into subspaces and analyzing the worst-case performance of a MOCO algorithm in terms of the expected number of calls to the underlying constraint solver. We apply the approach to two important constraint-based MOCO algorithms. Our analysis reveals a deep connection between the search mechanism of a constraint solver and the exploration of the search space of a MOCO problem.

1 citations

Book ChapterDOI
23 Oct 2011
TL;DR: The results of experiments indicated that the proposed algorithm can effectively speed up the convergence and lead to a preferable solution, balancing very well search efficiency of time-frequency atoms and computer memory for storing the over-complete dictionary.
Abstract: Sparse signal decomposition can get sparse representation of signal. Given that the sparse decomposition has a large number of calculations and is almost impossible to meet the request of real time. A novel multi-swarm co-operative particle swarm optimization (PSO) algorithm to implement matching pursuit was developed, where multi-swarm was adopted to maintain the diversity of population, and the exploration ability of particle swarm optimization was elegantly combined with the exploitation of extremal optimization (EO) to prevent premature convergence. This method could reduce very time-consuming inner product times and improve decomposition accuracy in signal sparse decomposition, thereby, balancing very well search efficiency of time-frequency atoms and computer memory for storing the over-complete dictionary. The results of experiments indicated that the proposed algorithm can effectively speed up the convergence and lead to a preferable solution.

1 citations


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