標(biāo)題: Titlebook: Advances in Swarm Intelligence; First International Ying Tan,Yuhui Shi,Kay Chen Tan Conference proceedings 2010 The Editor(s) (if applicab [打印本頁] 作者: 小故障 時(shí)間: 2025-3-21 16:23
書目名稱Advances in Swarm Intelligence影響因子(影響力)
書目名稱Advances in Swarm Intelligence影響因子(影響力)學(xué)科排名
書目名稱Advances in Swarm Intelligence網(wǎng)絡(luò)公開度
書目名稱Advances in Swarm Intelligence網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Swarm Intelligence被引頻次
書目名稱Advances in Swarm Intelligence被引頻次學(xué)科排名
書目名稱Advances in Swarm Intelligence年度引用
書目名稱Advances in Swarm Intelligence年度引用學(xué)科排名
書目名稱Advances in Swarm Intelligence讀者反饋
書目名稱Advances in Swarm Intelligence讀者反饋學(xué)科排名
作者: Abjure 時(shí)間: 2025-3-21 22:43
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 作者: CURL 時(shí)間: 2025-3-22 03:56 作者: 明智的人 時(shí)間: 2025-3-22 05:48
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作者: Acumen 時(shí)間: 2025-3-22 09:52
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作者: entail 時(shí)間: 2025-3-22 13:53
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作者: 有發(fā)明天才 時(shí)間: 2025-3-22 17:04
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作者: 健談的人 時(shí)間: 2025-3-23 01:06
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作者: HAUNT 時(shí)間: 2025-3-23 03:29
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作者: 邊緣帶來墨水 時(shí)間: 2025-3-23 06:42 作者: 襲擊 時(shí)間: 2025-3-23 10:40 作者: STEER 時(shí)間: 2025-3-23 16:19 作者: STANT 時(shí)間: 2025-3-23 20:15
Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimizationswarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evolutionary process. By testing on 4 benchmark function作者: 新字 時(shí)間: 2025-3-24 01:21 作者: 虛構(gòu)的東西 時(shí)間: 2025-3-24 03:23
An Improved Probability Particle Swarm Optimization Algorithmwo normal distributions are used to describe the distributions of particle positions, respectively. One is the normal distribution with the global best position as mean value and the difference between the current fitness and the global best fitness as standard deviation while another is the distrib作者: 葡萄糖 時(shí)間: 2025-3-24 07:14 作者: aesthetic 時(shí)間: 2025-3-24 14:07 作者: 小木槌 時(shí)間: 2025-3-24 15:41 作者: detach 時(shí)間: 2025-3-24 19:37
Improved Quantum Particle Swarm Optimization by Bloch Spheree Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a large extent. In this paper, the Bloch Sphere encodin作者: MAIM 時(shí)間: 2025-3-25 02:52 作者: mitten 時(shí)間: 2025-3-25 05:56 作者: AIL 時(shí)間: 2025-3-25 08:57
https://doi.org/10.1007/978-3-642-69779-1locate multiple optima. In the proposed algorithm, the algorithm inspired from natural ecosystem form niches automatically without any prespecified problem dependent parameters. Experiment results demonstrated that the proposed niching method is superior to the classic niching methods which are with or without niching parameters.作者: 巨碩 時(shí)間: 2025-3-25 12:12
Tidal Marshes as Outwelling/Pulsing Systemshematical modeling tool applicable to discrete event systems in order to represent its states evolution. Lyapunov stability theory provides the required tools needed to aboard the stability problem for the predator-prey system treated as a discrete event system modeled with timed petri nets. By prov作者: NOVA 時(shí)間: 2025-3-25 19:20 作者: TAIN 時(shí)間: 2025-3-26 00:00
https://doi.org/10.1007/978-981-16-1063-9ive 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作者: 撕裂皮肉 時(shí)間: 2025-3-26 03:15 作者: 出處 時(shí)間: 2025-3-26 06:49
https://doi.org/10.1007/978-981-16-1063-9ous 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作者: 產(chǎn)生 時(shí)間: 2025-3-26 11:57 作者: 柔聲地說 時(shí)間: 2025-3-26 14:19 作者: TRACE 時(shí)間: 2025-3-26 18:12 作者: essential-fats 時(shí)間: 2025-3-26 22:01
Paolo Cattorini,Roberto Mordacciization (PSO) to construct a two-population PSO model called PSOPB, composed of the host and the parasites population. In this model, the two populations exchange particles according to the fitness sorted in a certain number of iterations. In order to embody the law of "survival of the fittest" in b作者: bronchiole 時(shí)間: 2025-3-27 04:18 作者: Tractable 時(shí)間: 2025-3-27 06:21 作者: 滔滔不絕地講 時(shí)間: 2025-3-27 10:39
https://doi.org/10.1007/978-94-015-8344-2sed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presented to furthermore improve the performance of PSO. Expe作者: Cryptic 時(shí)間: 2025-3-27 16:14 作者: Archipelago 時(shí)間: 2025-3-27 18:20 作者: Resign 時(shí)間: 2025-3-28 01:06
Biomechanics Modeling and Concepts,d are not continuously available for computation, achieving a better make-span is a key issue. The existing algorithm SSAC has proved to be a good trade-off between availability and responsiveness while maintaining a good performance in the average response time of multiclass tasks. But the makespan作者: stratum-corneum 時(shí)間: 2025-3-28 02:22 作者: JEER 時(shí)間: 2025-3-28 08:12
https://doi.org/10.1007/978-3-319-15096-3e Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a large extent. In this paper, the Bloch Sphere encodin作者: 深陷 時(shí)間: 2025-3-28 12:55
S. M. Niaz Arifin,Gregory R. Madeyproved particle swarm optimization (PSO) algorithm. To enhance the exploitation ability of PSO, a stochastic iterated local search is incorporated. To improve the exploration ability of PSO, a population update method is applied to replace non-promising particles. In addition, a solution pool that s作者: Audiometry 時(shí)間: 2025-3-28 15:05 作者: BARK 時(shí)間: 2025-3-28 21:38 作者: affect 時(shí)間: 2025-3-28 22:58 作者: 保留 時(shí)間: 2025-3-29 03:54 作者: Condyle 時(shí)間: 2025-3-29 10:51
Conference proceedings 2010010) held in Beijing, the capital of China, during June 12-15, 2010. ICSI 2010 was the ?rst gathering in the world for researchers working on all aspects of swarm intelligence, and providedan academic forum for the participants to disseminate theirnewresearch?ndingsanddiscussemergingareasofresearch.作者: 有組織 時(shí)間: 2025-3-29 14:20 作者: ULCER 時(shí)間: 2025-3-29 15:36
https://doi.org/10.1007/978-981-16-1063-9ves of searching swarm. Some typical functions are selected to do the simulation tests. The function simulation tests showed that the three behavior rules are indispensability and endow the ASSA with powerful global optimization ability together.作者: 流動(dòng)性 時(shí)間: 2025-3-29 22:55
Towards a Rational Bioenergy Policy Concept,icle swarm optimization. Thus a new particle swarm optimization algorithm is proposed. Numerical experiments show that its computing time is short and its global search capability is powerful as well as its computing accuracy is high in compared with the basic PSO.作者: PARA 時(shí)間: 2025-3-30 00:44 作者: impaction 時(shí)間: 2025-3-30 06:12
On the Farther Analysis of Performance of the Artificial Searching Swarm Algorithmves of searching swarm. Some typical functions are selected to do the simulation tests. The function simulation tests showed that the three behavior rules are indispensability and endow the ASSA with powerful global optimization ability together.作者: 放逐某人 時(shí)間: 2025-3-30 12:11 作者: Gorilla 時(shí)間: 2025-3-30 13:09
Gender-Hierarchy Particle Swarm Optimizer Based on Punishmenttimal solution. Especially, a novel recognition approach, called general recognition, is presented to furthermore improve the performance of PSO. Experimental results show that the proposed algorithm shows better behaviors as compared to the standard PSO, tribes-based PSO and GH-PSO with tribes.作者: 救護(hù)車 時(shí)間: 2025-3-30 16:50
Tidal Marshes as Outwelling/Pulsing Systemsing boundedness one confirms a dominant oscillating behavior of both populations dynamics performance. However, the oscillating frequency results to be unknown. This inconvenience is overcome by considering a specific recurrence equation, in the max-plus algebra.作者: Anemia 時(shí)間: 2025-3-30 23:00 作者: 去才蔑視 時(shí)間: 2025-3-31 02:47 作者: 兒童 時(shí)間: 2025-3-31 08:59 作者: 大范圍流行 時(shí)間: 2025-3-31 12:56
Biomechanics Modeling and Concepts, may be influenced due to load imbalance. In this paper we proposed approach try to further optimize this scheduling strategy by using quantum-behaved particle swarm optimization. And compared with SSAC and MINMIN in the simulation experiment; results indicate that our proposed technique is a better solution for reducing the makespan considerably.作者: 失眠癥 時(shí)間: 2025-3-31 14:48
Simulating Human Social Behaviorsjobs in each group and the sequence of groups. Three different lower bounds are developed to evaluate the performance of the proposed PSO algorithm. Limited numerical results show that the proposed PSO algorithm performs well for all test problems.作者: 獨(dú)白 時(shí)間: 2025-3-31 18:57 作者: Pastry 時(shí)間: 2025-3-31 23:22
Paolo Cattorini,Roberto Mordaccing is realized through a statistical mapping, between the parameter set and the KNOB, learned by a radial basis function neural network (RBFNN) simulation model. In this way, KNOB provides an easy way to tune PSO directly by its parameter setting. A simple application of KNOB to promote is presented to verify the mechanism of KNOB.作者: SNEER 時(shí)間: 2025-4-1 03:46
https://doi.org/10.1007/978-3-319-15096-3mization problem. Experimental results demonstrate that BQPSO has both stronger global search capability and faster convergence speed, and it is feasible and effective in solving some complex optimization problems.
