Topic
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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01 Jun 2008
TL;DR: A new model based on particle swarm optimization to detect network community is proposed and experimental results indicate this model can effectively find web communities of network structure without any domain information.
Abstract: Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number in the network, the extremal optimization algorithm proposed by Duch J. is extremely sensitive to the initial solution, easy to fall into the local optimum. This article proposes a new model based on particle swarm optimization to detect network community, and with different scale network chart, Zachary, Krebs and dolphins network architecture to test the algorithm, the experimental results indicate this model can effectively find web communities of network structure without any domain information.
32 citations
06 Dec 2006
TL;DR: A new effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented, an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures.
Abstract: UDK 004.421:621.396.67 IFAC 5.8.3;2.8.3 Original scientific paper In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). Preliminary analyses are here presented with respect to the other optimization techniques dealing with a classical optimization problem. The optimized design of a printed reflectarray antenna is finally reported with numerical results.
31 citations
16 Dec 2010
TL;DR: The test results indicate that the ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.
Abstract: This work presents Ant Colony Optimization (ACO), which was initially developed to be a meta-heuristic for combinatorial optimization, for solving the cardinality constraints Markowitz mean-variance portfolio model (nonlinear mixed quadratic programming problem). To our knowledge, an efficient algorithmic solution for this problem has not been proposed until now. Using heuristic algorithms in this case is imperative. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.
31 citations
14 Sep 2007
TL;DR: The experimental results demonstrate that the proposed quantum ant colony optimization algorithm (QACO) is valid and outperforms the discrete binary particle swarm optimization algorithm and QEA in terms of the optimization ability.
Abstract: Ant colony optimization (ACO) is a technique for mainly optimizing the discrete optimization problem. Based on transforming the discrete binary optimization problem as a "best path" problem solved using the ant colony metaphor, a novel quantum ant colony optimization (QACO) algorithm is proposed to tackle it. Different from other ACO algorithms, Q-bit and quantum rotation gate adopted in quantum-inspired evolutionary algorithm (QEA) are introduced into QACO to represent and update the pheromone respectively. Considering the traditional rotation angle updating strategy used in QEA is improper for QACO as their updating mechanisms are different, we propose a new strategy to determine the rotation angle of QACO. The experimental results demonstrate that the proposed QACO is valid and outperforms the discrete binary particle swarm optimization algorithm and QEA in terms of the optimization ability.
31 citations
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01 Jan 2011
TL;DR: This paper addresses the Cloud Resource Management Problem with an efficient Biased Random-Key Genetic Algorithm and indicates that the performance of this approach outperforms the approaches proposed in the literature.
Abstract: Evolutionary Computation in Combinatorial Optimization , Evolutionary Computation in Combinatorial Optimization , دانشگاه تهران
31 citations