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人工智能

市場調查報告書

商品編碼

1490922

群體智慧市場規模- 按模型（蟻群最佳化、粒子群最佳化）、按功能（最佳化、集群、調度、路由）、按應用（機器人、無人機、人類集群）、按最終用戶和預測，2024 - 2032 年

Swarm Intelligence Market Size - By Model (Ant Colony Optimization, Particle Swarm Optimization), By Capability (Optimization, Clustering, Scheduling, Routing), By Application (Robotics, Drones, Human Swarming), By End Users & Forecast, 2024 - 2032

出版日期: 2024年03月22日 | 出版商:

Global Market Insights Inc. | 英文 240 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

預計從 2024 年到 2032 年，群體智慧市場規模將以超過 38.5% 的複合年成長率成長。群體智慧演算法透過模仿自然群體中觀察到的集體行為來提供有效的問題解決能力。此外，人工智慧（AI）和機器學習（ML）技術的進步正在增強群體智慧系統的效能和可擴展性。

自動駕駛汽車、機器人和最佳化任務的日益普及也增加了市場吸引力。政府和私人組織不斷增加的合作研究工作和投資正在進一步促進群體智慧解決方案的開發和商業化。例如，2024 年4 月，微軟推出了Phi-3-mini，這是其第一個小語言模型(SLM)，旨在透過具有成本效益的人工智慧選項擴大其客戶群，同時重申其對變革性科技以徹底改變工作和社會的承諾。

整個產業分為模型、能力、應用、最終用戶和區域。

根據模型，由於演算法的簡單性、效率和可擴展性，粒子群最佳化 (PSO) 領域的群體智慧市場將在 2024 年至 2032 年間以穩健的複合年成長率擴展。 PSO 演算法透過模擬鳥群或魚群的行為，在最佳化任務中表現出色。它們還能夠快速收斂到最佳解決方案並適應動態環境，從而增加對工程、金融和資料分析等領域各種應用的吸引力。

在應用方面，由於多機器人系統對分散控制和協調的需求不斷成長，到 2032 年，機器人領域的群體智慧產業將大幅成長。群體智慧演算法使機器人能夠表現出集體行為，從而提高其在搜索和救援、監視和探索等任務中的效率。群體機器人協作演算法研發的重大進步將進一步推動其採用，使其成為下一代機器人系統的關鍵技術。

從地區來看，由於研發投資不斷增加，特別是在中國、日本和韓國等國家，亞太地區群體智慧市場將從 2024 年到 2032 年顯著成長。蓬勃發展的科技產業以及人工智慧和機器人解決方案的日益普及正在推動對群體智慧技術的需求。政府推出的促進創新創業的措施也將刺激區域市場的擴張。

產業生態系統分析
供應商格局
- 技術提供者
- 系統整合商
- 終端用戶
利潤率分析
技術與創新格局
專利分析
重要新聞和舉措
監管環境
衝擊力
- 成長動力
  - 提高群體智慧在解決巨量資料問題的適用性
  - 運輸和物流領域擴大採用群體智慧
  - 自治系統的發展
  - 工業4.0的崛起
  - 技術進步
- 產業陷阱與挑戰
  - 開發和部署成本高
  - 認知和理解有限
成長潛力分析
波特的分析
PESTEL分析

第 4 章：競爭格局

介紹
公司市佔率分析
競爭定位矩陣
戰略展望矩陣

第 5 章：市場估計與預測：依型號，2021 - 2032

主要趨勢
蟻群最佳化
粒子群最佳化
其他

第 6 章：市場估計與預測：依能力分類，2021 - 2032 年

主要趨勢
最佳化
聚類
調度
路由

第 7 章：市場估計與預測：依應用分類，2021 - 2032

主要趨勢
機器人技術
無人機
人類蜂擁而至

第 8 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

主要趨勢
運輸與物流
- 最佳化
- 聚類
- 調度
- 路由
機器人與自動化
- 最佳化
- 聚類
- 調度
- 路由
衛生保健
- 最佳化
- 聚類
- 調度
- 路由
零售與電子商務
- 最佳化
- 聚類
- 調度
- 路由
其他
- 最佳化
- 聚類
- 調度
- 路由

第 9 章：市場估計與預測：按地區，2021 - 2032

主要趨勢
北美洲
- 美國
- 加拿大
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 俄羅斯
- 北歐人
- 歐洲其他地區
亞太地區
- 中國
- 印度
- 日本
- 韓國
- 澳新銀行
- 新加坡
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地區
MEA
- 阿拉伯聯合大公國
- 南非
- 沙烏地阿拉伯
- MEA 的其餘部分

第 10 章：公司簡介

Agilox Services GmbH
Apium Swarm Robotics
Axon Enterprise, Inc
Berkeley Marine Robotics Inc.
Boston Dynamics
Continental AG
ConvergentAI, Inc
DoBots
Enswarm
Hydromea
Kim Technologies
PowerBlox
Robert Bosch GmbH
Sentien Robotics
SSI Schafer - Fritz Schafer
SwarmFarm Robotics
Swarm Technology
Swisslog Holding AG
Unanimous AI
Valutico

簡介目錄

Product Code: 8689

Swarm Intelligence Market size is projected to expand at over 38.5% CAGR from 2024 to 2032. The increasing complexity of problems in various industries, such as logistics, finance, and healthcare is driving the demand for numerous innovative solutions. Swarm intelligence algorithms offer efficient problem-solving capabilities by mimicking collective behavior observed in natural swarms. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) technologies are enhancing the performance and scalability of swarm intelligence systems.

Rising adoption in autonomous vehicles, robotics, and optimization tasks is also increasing the market appeal. Growing collaborative research efforts and investments by governments and private organizations are further facilitating the development and commercialization of swarm intelligence solutions. For instance, in April 2024, Microsoft unveiled Phi-3-mini, the first of its small language models (SLMs)to broaden its customer base with cost-effective AI options while affirming its commitment to transformative technology to revolutionize work and society.

The overall industry is categorized into model, capability, application, end-user, and region.

Based on model, the swarm intelligence market from the particle swarm optimization (PSO) segment will expand at robust CAGR between 2024 and 2032, due to the simplicity, efficiency, and scalability of the algorithm. PSO algorithms excel in optimization tasks by simulating the behavior of bird flocks or fish schools. They also have ability to converge quickly to optimal solutions and adapt to dynamic environments, subsequently increasing their appeal for various applications in fields like engineering, finance, and data analytics.

With respect to application, the swarm intelligence industry from the robotics segment will grow at substantial rate up to 2032, owing to the growing need for decentralized control and coordination in multi-robot systems. Swarm intelligence algorithms enable robots to exhibit collective behavior, enhancing their efficiency in tasks like search and rescue, surveillance, and exploration. Significant advancements in swarm robotics R&D of collaborative algorithms will further drive their adoption, making it a key technology for the next generation of robotic systems.

Regionally, the Asia Pacific swarm intelligence market will depict notable growth from 2024 to 2032, on account of the increasing R&D investments, particularly in countries including China, Japan, and South Korea. The burgeoning tech industry and the growing adoption of AI and robotics solutions are driving the demand for swarm intelligence technologies. The launch of government initiatives to promote innovation and entrepreneurship will also spur the regional market expansion.

Chapter 1 Methodology & Scope

1.1 Research design
- 1.1.1 Research approach
- 1.1.2 Data collection methods
1.2 Base estimates and calculations
- 1.2.1 Base year calculation
- 1.2.2 Key trends for market estimates
1.3 Forecast model
1.4 Primary research & validation
- 1.4.1 Primary sources
- 1.4.2 Data mining sources
1.5 Market definitions

Chapter 2 Executive Summary

2.1 Industry 360 degree synopsis, 2021 - 2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Supplier landscape
- 3.2.1 Technology providers
- 3.2.2 System integrator
- 3.2.3 End users
3.3 Profit margin analysis
3.4 Technology & innovation landscape
3.5 Patent analysis
3.6 Key news & initiatives
3.7 Regulatory landscape
3.8 Impact forces
- 3.8.1 Growth drivers
  - 3.8.1.1 Increasing applicability of swarm intelligence for solving big data problems
  - 3.8.1.2 Rising adoption of swarm intelligence in transportation and logistics
  - 3.8.1.3 Growth of autonomous systems
  - 3.8.1.4 Rise of Industry 4.0
  - 3.8.1.5 Advancement in technology
- 3.8.2 Industry pitfalls & challenges
  - 3.8.2.1 High development and deployment costs
  - 3.8.2.2 Limited awareness and understanding
3.9 Growth potential analysis
3.10 Porter's analysis
3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Model, 2021 - 2032 ($Mn)

5.1 Key trends
5.2 Ant colony optimization
5.3 Particle swarm optimization
5.4 Others

Chapter 6 Market Estimates & Forecast, By Capability, 2021 - 2032 ($Mn)

6.1 Key trends
6.2 Optimization
6.3 Clustering
6.4 Scheduling
6.5 Routing

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2032 ($Mn)

7.1 Key trends
7.2 Robotics
7.3 Drones
7.4 Human swarming

Chapter 8 Market Estimates & Forecast, By End Users, 2021 - 2032 ($Mn)

8.1 Key trends
8.2 Transportation & logistics
- 8.2.1 Optimization
- 8.2.2 Clustering
- 8.2.3 Scheduling
- 8.2.4 Routing
8.3 Robotics & automation
- 8.3.1 Optimization
- 8.3.2 Clustering
- 8.3.3 Scheduling
- 8.3.4 Routing
8.4 Healthcare
- 8.4.1 Optimization
- 8.4.2 Clustering
- 8.4.3 Scheduling
- 8.4.4 Routing
8.5 Retail & E-commerce
- 8.5.1 Optimization
- 8.5.2 Clustering
- 8.5.3 Scheduling
- 8.5.4 Routing
8.6 Others
- 8.6.1 Optimization
- 8.6.2 Clustering
- 8.6.3 Scheduling
- 8.6.4 Routing

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2032 ($Mn)

9.1 Key trends
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
9.3 Europe
- 9.3.1 UK
- 9.3.2 Germany
- 9.3.3 France
- 9.3.4 Italy
- 9.3.5 Spain
- 9.3.6 Russia
- 9.3.7 Nordics
- 9.3.8 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 India
- 9.4.3 Japan
- 9.4.4 South Korea
- 9.4.5 ANZ
- 9.4.6 Singapore
- 9.4.7 Rest of Asia Pacific
9.5 Latin America
- 9.5.1 Brazil
- 9.5.2 Mexico
- 9.5.3 Argentina
- 9.5.4 Rest of Latin America
9.6 MEA
- 9.6.1 UAE
- 9.6.2 South Africa
- 9.6.3 Saudi Arabia
- 9.6.4 Rest of MEA