Cantitate/Preț
Produs
Total produse
Vezi coșul
Advances in Swarm Intelligence de Ying Tan

Advances in Swarm Intelligence: Lecture Notes in Computer Science, cartea 16012

Editat de Ying Tan, Yuhui Shi
en Limba Engleză Paperback – 2 oct 2025
his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021.
The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (6) 33772 lei  6-8 săpt. +4557 lei  7-13 zile
  Springer – 2 oct 2025 54212 lei  17-23 zile +4557 lei  7-13 zile
  Springer – 2 oct 2025 54719 lei  17-23 zile +4594 lei  7-13 zile
  Springer – 23 iun 2021 33772 lei  6-8 săpt.
  Springer – 23 iun 2021 33793 lei  6-8 săpt.
  Springer – 21 aug 2024 46685 lei  6-8 săpt.
  Springer – 21 aug 2024 46809 lei  6-8 săpt.

Din seria Lecture Notes in Computer Science

Preț: 54212 lei

Preț vechi: 67766 lei
-20%

Puncte Express: 813
Preț estimativ în valută:
9586€ • 10995$ • 8288£

Carte disponibilă

Livrare economică 04-10 aprilie
Livrare express 25-31 martie pentru 5556 lei

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9789819509843
ISBN-10: 981950984X
Pagini: 344
Ilustrații: XVII, 306 p. 92 illus.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.52 kg
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Cuprins

.- Particle Swarm Optimization.
.- Set-Based Particle Swarm Optimization for the Multi-Objective Multi-Dimensional Knapsack Problem.
.- Proposal of a Memory-Based Ensemble Particle Swarm Optimizer.
.- A Tri-swarm Particle Swarm Optimization Considering the Cooperation and the Fitness Value.
.- A Modified Variable Velocity Strategy Particle Swarm Optimization Algorithm for Multi-objective Feature Selection.
.- Multi-Strategy Enhanced Particle Swarm Optimization Algorithm for Elevator Group Scheduling.
.- A Self-Learning Particle Swarm Optimization Algorithm for Dynamic Job Shop Scheduling Problem with New Jobs Insertion.
.- Convolutional Neural Network Architecture Design Using An Improved Surrogate-assisted Particle Swarm Optimization Algorithm.
.- Swarm Intelligence Computing.
.- Cooperative Search and Rescue Target Assignment Based on Improved Ant Colony Algorithm.
.- A Metabolic Pathway Design Method based on surrogate-assisted Fireworks Algorithm.
.- Circle Chaotic Search-Based Butterfly Optimization Algorithm.
.- An Adaptive Bacterial Foraging Optimization Algorithm Based on Chaos-Enhanced Non-Elite Reverse Learning.
.- Enhanced Bacterial Foraging Optimization with Dynamic Disturbance Learning and Bilayer Nested Structure.
.- Improved Kepler Optimization Algorithm Based on Mixed Strategy.
.- Harmony Search with Dynamic Dimensional-reduction Adjustment Strategy for Large-scale Absolute Value Equation.
.- Massive Conscious Neighborhood-based Crow Search Algorithm for the Pseudo-Coloring Problem.
.- Multi-Strategy Integration Model Based on Black-Winged Kite Algorithm and Artificial Rabbit Optimization.
.- Differential Evolution.
.- Fractional Order Differential Evolution to Solve Parameter Estimation Problem of Solar Photovoltaic Models.
.- Enhanced Dingo Optimization Algorithm Based on Differential Evolution and Chaotic Mapping for Engineering Optimization.
.- Hierarchical Adaptive Differential Evolution with Local Search for Extreme Learning Machine.
.- Metaheuristic Algorithms for Enhancing Multicepstral Representation in Voice Spoofing Detection: An Experimental Approach.
.- Evolutionary Algorithms.
.- A Multi-modal Multi-objective Evolutionary Algorithm Based on Multi-criteria Grouping.
.- Constructing Robust and Influential Networks against Cascading Failures via a Multi-objective Evolutionary Algorithm.
.- Fault Reconfiguration of Distribution Networks Using an Enhanced Multimodal Multi-objective Evolutionary Algorithm.
.- Attacking Evolutionary Algorithms via SparseEA.
.- Evolutionary Computation with Distance-based Pretreatment for Multimodal Problems.
.- Multi-Agent Reinforcement Learning.
.- Stock Price Prediction Model Based on Blending Model Improved with Sentiment Factors and Double Q-learning.
.- Stock price prediction mdoel integrating an improved NSGA-III with Random Forest.
.- Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming.
.- Diversity Improved Genetic Algorithm for Weapon Target Assignment.
.- An Investigation of Underground Rescue Scheduling with Multi-Agent Reinforcement Learning.
.- Distributed Advantage-based Weights Reshaping Algorithm with Sparse Reward.
.- Multi-objective Optimization.
.- A Joint Prediction Strategy based on Multiple Feature Points for Dynamic Multi-objective Optimization.
.- An Expensive Multi-objective Optimization Algorithm Based on Regional Density Ratio.
.- Robust Lightweight Neural Network Architecture Search-based on Multi-objective Particle Swarm Optimization.
.- Surrogate-Assisted Multi-Objective Evolutionary Algorithm Guided by Multi-Reference Points.
.- Multi-objective Path planning of Multiple Unmanned Air Vehicles Using the CCMO Algorithm.
.- Multi-UAV Collaborative Detection Based on Reinforcement Learning.