標(biāo)題: Titlebook: Advances in Swarm Intelligence; 15th International C Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The A [打印本頁(yè)] 作者: 密度 時(shí)間: 2025-3-21 17:30
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(kāi)度
書目名稱Advances in Swarm Intelligence網(wǎng)絡(luò)公開(kāi)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: 翅膀拍動(dòng) 時(shí)間: 2025-3-21 23:48
Modellkonzeption und Hypothesen,and oscillation problems caused by the fixed step-size. The reproduction step improves the non-elite solutions based on non-elite reverse learning and incorporates a chaotic disturbance mechanism to enhance the convergence speed and effectively reduce the possibility of the population falling into l作者: Palliation 時(shí)間: 2025-3-22 00:39 作者: 上坡 時(shí)間: 2025-3-22 07:07
Proposal of?a?Memory-Based Ensemble Particle Swarm Optimizernerations during the evolution of the particles. To solve this weakness, the article presents a PSO approach based on a sliding memory that stores these generations and applies them in the dynamic selection of algorithms, making a choice even more efficient. We compare the proposal with other partic作者: 鴿子 時(shí)間: 2025-3-22 10:10 作者: 譏笑 時(shí)間: 2025-3-22 14:30 作者: 泥沼 時(shí)間: 2025-3-22 19:40
0302-9743 ces in Swarm Intelligence, ICSI 2024, held in Xining, China, during August 23–26, 2024...The 74 revised full papers presented in these proceedings were carefully reviewed and selected from 156 submissions. The papers are organized in the following topical sections:..Part I - Particle swarm optimizat作者: invade 時(shí)間: 2025-3-22 23:09
Betrachtung des Untersuchungsgegenstandes, integration involves updating the inertia weight and incorporating a local search to refine search directions, termed PSO-IWLS (PSO with Inertia Weight Local Search). The PSO-IWLS has demonstrated superior performance compared to state-of-the-art approaches on large-scale benchmarks, excelling in both computation time and solution quality.作者: 無(wú)效 時(shí)間: 2025-3-23 05:13 作者: floaters 時(shí)間: 2025-3-23 05:46 作者: 單片眼鏡 時(shí)間: 2025-3-23 12:34
Strategisches Kompetenz-Managementctively, this paper designs a modified variable velocity strategy particle swarm optimization algorithm. The algorithm incorporates whale encircling and flipping, along with an inertia weight updating strategy for random perturbation, known as WETVVS-MOPSO. The results show that WETVVS-MOPSO significantly outperforms its competitors.作者: Extricate 時(shí)間: 2025-3-23 14:07 作者: 時(shí)間等 時(shí)間: 2025-3-23 19:32 作者: 以煙熏消毒 時(shí)間: 2025-3-24 02:14 作者: Focus-Words 時(shí)間: 2025-3-24 02:51
Gründungsintention von Akademikernof the algorithm on 8 benchmark functions. Experimental results demonstrate that our improved fusion strategy has superior comprehensive performance over advanced optimization algorithms such as the Artificial Rabbit optimization, implying evident superiority and application potential of our strategy.作者: refraction 時(shí)間: 2025-3-24 09:16 作者: 不滿分子 時(shí)間: 2025-3-24 14:23
Multi-strategy Enhanced Particle Swarm Optimization Algorithm for?Elevator Group Schedulingloyed to search the nearby solution spaces to further avoid local optimum. Simulation results have demonstrated that the proposed algorithm can achieve a shorter passenger waiting time than traditional particle swarm optimization.作者: 態(tài)度暖昧 時(shí)間: 2025-3-24 17:58
Convolutional Neural Network Architecture Design Using an Improved Surrogate-Assisted Particle Swarmerify the proposed algorithm and compare it with some mainstream network structures and processes. Experimental results show that the classification accuracy of the proposed algorithm is equivalent to or even better than similar algorithms and consumes fewer computing resources.作者: 跟隨 時(shí)間: 2025-3-24 19:27 作者: 遠(yuǎn)足 時(shí)間: 2025-3-25 01:43
Multi-strategy Integration Model Based on?Black-Winged Kite Algorithm and?Artificial Rabbit Optimizaof the algorithm on 8 benchmark functions. Experimental results demonstrate that our improved fusion strategy has superior comprehensive performance over advanced optimization algorithms such as the Artificial Rabbit optimization, implying evident superiority and application potential of our strategy.作者: 賄賂 時(shí)間: 2025-3-25 06:00 作者: Maximize 時(shí)間: 2025-3-25 08:23
Gründungsintention von Akademikernoptima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.作者: Heresy 時(shí)間: 2025-3-25 12:14
Implikationen und Limitationen, The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.作者: NOVA 時(shí)間: 2025-3-25 18:25 作者: EVADE 時(shí)間: 2025-3-25 22:23
A Tri-Swarm Particle Swarm Optimization Considering the Cooperation and the Fitness Valuest fitness value were respectively divided into ERS, EIS and CS. The results on seven unimodal benchmark functions demonstrated the superiority of the proposed variant compared with other five variants.作者: 催眠 時(shí)間: 2025-3-26 01:20
Circle Chaotic Search-Based Butterfly Optimization Algorithmoptima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.作者: 縮影 時(shí)間: 2025-3-26 05:48
Improved Kepler Optimization Algorithm Based on?Mixed Strategy The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.作者: 勉勵(lì) 時(shí)間: 2025-3-26 08:36 作者: lattice 時(shí)間: 2025-3-26 14:42 作者: 狂熱文化 時(shí)間: 2025-3-26 20:14
Cooperative Search and Rescue Target Assignment Based on Improved Ant Colony Algorithmand makes full use of the global search ability of ant colony algorithm to explore the optimal solution. The simulation results show that this method can quickly and effectively provide the target assignment scheme of search and rescue resources, maximize the survival probability, and improve the efficiency of search and rescue at sea.作者: 拋射物 時(shí)間: 2025-3-26 22:10 作者: pessimism 時(shí)間: 2025-3-27 01:09
Conference proceedings 2024 intelligence computing; Differential evolution; Evolutionary algorithms; Multi-agent reinforcement learning & Multi-objective optimization...Part II - Route planning problem; Machine learning; Detection and prediction; Classification; Edge computing; Modeling and optimization & Analysis of review..作者: 呼吸 時(shí)間: 2025-3-27 08:26 作者: Cpap155 時(shí)間: 2025-3-27 10:14 作者: 發(fā)出眩目光芒 時(shí)間: 2025-3-27 15:11 作者: 可以任性 時(shí)間: 2025-3-27 20:35
Conference proceedings 2024rm Intelligence, ICSI 2024, held in Xining, China, during August 23–26, 2024...The 74 revised full papers presented in these proceedings were carefully reviewed and selected from 156 submissions. The papers are organized in the following topical sections:..Part I - Particle swarm optimization; Swarm作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-3-28 00:13 作者: granite 時(shí)間: 2025-3-28 04:08
Strategisches Kompetenz-Management, the SBPSO was applied to a number of, mostly single-objective, optimization problems. Recently, the SBPSO was adapted to solve multi-objective optimization problems (MOPs). The resulting multi-guide SBPSO (MGSBPSO) showed excellent performance on multi-objective portfolio optimization problems and作者: 陶瓷 時(shí)間: 2025-3-28 06:49 作者: ENACT 時(shí)間: 2025-3-28 14:04 作者: 自愛(ài) 時(shí)間: 2025-3-28 17:42 作者: Accessible 時(shí)間: 2025-3-28 20:03 作者: 帳單 時(shí)間: 2025-3-29 02:30 作者: peritonitis 時(shí)間: 2025-3-29 04:35 作者: phytochemicals 時(shí)間: 2025-3-29 09:19
Gründungsintention von Akademikern and finish the rescue task. A target assignment model of multiple resources cooperative search and rescue at sea is established, and a target assignment solution based on improved ant colony algorithm is proposed. This method optimizes the target selection mechanism of search and rescue resources, 作者: labile 時(shí)間: 2025-3-29 14:48 作者: evasive 時(shí)間: 2025-3-29 16:48 作者: conservative 時(shí)間: 2025-3-29 23:43 作者: BARB 時(shí)間: 2025-3-30 03:22
Modellkonzeption und Hypothesen, and higher memory consumption. To address these problems, a bilayer nested bacterial foraging algorithm incorporating a dynamic disturbance learning strategy (BiddBFO) is proposed. The novel algorithm incorporates three update strategies: i) The piecewise linear chaotic map (PWLCM) was used to init作者: diabetes 時(shí)間: 2025-3-30 05:54 作者: 連累 時(shí)間: 2025-3-30 11:29 作者: 思想流動(dòng) 時(shí)間: 2025-3-30 13:39 作者: Volatile-Oils 時(shí)間: 2025-3-30 16:31
Gründungsintention von Akademikernmization (ARO). This fusion draws from the strengths of both algorithms, offering a powerful tool for addressing complex problems. We have employed a master-slave model strategy, introducing a master-slave structure during the optimization process to enhance search efficiency and optimize algorithm 作者: 歸功于 時(shí)間: 2025-3-31 00:42
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/167321.jpg作者: Estimable 時(shí)間: 2025-3-31 01:44
https://doi.org/10.1007/978-981-97-7181-3swarm intelligence; swarm intelligence optimization algorithm; ACO; PSO; ABC; GA; FWA; hybrid optimization 作者: 觀察 時(shí)間: 2025-3-31 06:38
978-981-97-7180-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: ESPY 時(shí)間: 2025-3-31 12:52 作者: Fillet,Filet 時(shí)間: 2025-3-31 14:53 作者: 推測(cè) 時(shí)間: 2025-3-31 20:53
A Tri-Swarm Particle Swarm Optimization Considering the Cooperation and the Fitness Valueooperation within the animal groups, the whole population of PSO was divided into three sub-swarms according to their responsibility: i) the exploration swarm (ERS) to explore, ii) the exploitation swarm (EIS) to exploit, iii) the convergence swarm (CS) to converge. And according to the fitness valu作者: 生來(lái) 時(shí)間: 2025-3-31 22:56 作者: 我正派 時(shí)間: 2025-4-1 02:58
Multi-strategy Enhanced Particle Swarm Optimization Algorithm for?Elevator Group Schedulingsue of local optimum. In order to improve the capability of finding the global optimum, a multi-strategy enhanced particle swarm optimization algorithm has been proposed for elevator group scheduling in this work. For the initialization of particle position, Tent map is used to generate a diverse po