Titlebook: Advances in Swarm Intelligence; First International Ying Tan,Yuhui Shi,Kay Chen Tan Conference proceedings 2010 The Editor(s) (if applicab

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书目名称Advances in Swarm Intelligence影响因子(影响力)




书目名称Advances in Swarm Intelligence影响因子(影响力)学科排名




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书目名称Advances in Swarm Intelligence网络公开度学科排名




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书目名称Advances in Swarm Intelligence被引频次学科排名




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书目名称Advances in Swarm Intelligence年度引用学科排名




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书目名称Advances in Swarm Intelligence读者反馈学科排名

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0302-9743 viewed by at least three reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 185 high-quality papers were selected for publicat978-3-642-13494-4978-3-642-13495-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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明智的人

Study on the Local Search Ability of Particle Swarm Optimizational optimum, even for unimodal functions. It is necessary to study the local search ability of PSO. The interval compression method and the probabilistic characteristic of the searching interval of particles are used to analyze the local search ability of PSO in this paper. The conclusion can be obta

Acumen

The Performance Measurement of a Canonical Particle Swarm Optimizer with Diversive Curiosityive curiosity (CPSO/DC). A crucial idea here is to introduce diversive curiosity into the CPSO to comprehensively manage the trade-off between exploitation and exploration for alleviating stagnation. To demonstrate the effectiveness of the proposed method, computer experiments on a suite of five-dim

entail

Mechanism and Convergence of Bee-Swarm Genetic Algorithmlobal search, and another for local search. Only best one can crossover. The genetic operators include order crossover operator, adaptive mutation operator and restrain operator. The simulated annealing is also introduced to help local optimization. The method sufficiently takes the advantage of gen

有发明天才

On the Farther Analysis of Performance of the Artificial Searching Swarm Algorithmous algorithms. For farther understanding the running principle of ASSA, this work discusses the functions of three behavior rules which decide the moves of searching swarm. Some typical functions are selected to do the simulation tests. The function simulation tests showed that the three behavior r

健谈的人

Orthogonality and Optimality in Non-Pheromone Mediated Foragingtructed which contains the location(s) of an item or of items in the search area. Collection is the task in which an item is picked up and carried back to a central known location. We theoretically examine these tasks, generating minimal conditions for each one to be accomplished. We then build a sw

HAUNT

An Adaptive Staged PSO Based on Particles’ Search Capabilitiesearch processes of the standard PSO (SPSO) and the linear decreasing inertia weight PSO (LDWPSO) are analyzed based on our previous definition of exploitation. Second, three stages of the search process in PSO are defined. Each stage has its own search preference, which is represented by the exploit

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