標(biāo)題: Titlebook: Biogeography-Based Optimization: Algorithms and Applications; Yujun Zheng,Xueqin Lu,Shengyong Chen Book 2019 Springer Nature Singapore Pte [打印本頁(yè)] 作者: antibody 時(shí)間: 2025-3-21 19:27
書(shū)目名稱Biogeography-Based Optimization: Algorithms and Applications影響因子(影響力)
書(shū)目名稱Biogeography-Based Optimization: Algorithms and Applications影響因子(影響力)學(xué)科排名
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書(shū)目名稱Biogeography-Based Optimization: Algorithms and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Biogeography-Based Optimization: Algorithms and Applications被引頻次
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書(shū)目名稱Biogeography-Based Optimization: Algorithms and Applications讀者反饋
書(shū)目名稱Biogeography-Based Optimization: Algorithms and Applications讀者反饋學(xué)科排名
作者: 行乞 時(shí)間: 2025-3-21 20:29 作者: 6Applepolish 時(shí)間: 2025-3-22 03:52
Application of Biogeography-Based Optimization in Transportation,s can be modeled as combinatorial optimization problems. Nowadays, with the development of transportation systems, most of such problems are high-dimensional and/or NP-hard. In recent years, we have adapted BBO algorithm to a variety of transportation problems and achieved good results.作者: cumber 時(shí)間: 2025-3-22 07:38
Application of Biogeography-Based Optimization in Job Scheduling,his chapter, we adapt BBO to solve a set of scheduling problems including flow-shop scheduling, job-shop scheduling, maintenance job scheduling, and university course timetabling. The experimental results demonstrate the effectiveness and efficiency of BBO for the problems.作者: Outwit 時(shí)間: 2025-3-22 09:40 作者: HEAVY 時(shí)間: 2025-3-22 15:22
Biogeography-Based Optimization in Machine Learning,ted by structural design and parameter selection. This chapter introduces how to use BBO and its variants for optimizing structures and parameters of ANNs. The results show that BBO is a powerful method for enhancing the performance of many machine learning models.作者: 易于 時(shí)間: 2025-3-22 17:15
Kant’s Theory of Natural Scienceulation, which often has a great influence on the performance of the algorithms. This chapter first introduces some typical types of population topologies and then describes the methods for improving BBO with local topologies.作者: 表否定 時(shí)間: 2025-3-23 01:01 作者: compassion 時(shí)間: 2025-3-23 05:21
,Apperception: B-Deduction § 16,s can be modeled as combinatorial optimization problems. Nowadays, with the development of transportation systems, most of such problems are high-dimensional and/or NP-hard. In recent years, we have adapted BBO algorithm to a variety of transportation problems and achieved good results.作者: Interregnum 時(shí)間: 2025-3-23 07:24 作者: 臭了生氣 時(shí)間: 2025-3-23 10:48
Synthesis, Intuition and Mathematics,, we use BBO and its improved versions to a set of optimization problems in image processing, including image compression, salient object detection, and image segmentation. The results demonstrate the effectiveness of BBO in optimization problems in image processing.作者: 鍍金 時(shí)間: 2025-3-23 16:34
Summary: The Catalogue of Design Features,ted by structural design and parameter selection. This chapter introduces how to use BBO and its variants for optimizing structures and parameters of ANNs. The results show that BBO is a powerful method for enhancing the performance of many machine learning models.作者: 復(fù)習(xí) 時(shí)間: 2025-3-23 19:31
https://doi.org/10.1007/978-981-13-2586-1Biogeography-based optimization; Evolutionary Computation; Meta-Heuristic Algorithms; bio-inspired comp作者: 乏味 時(shí)間: 2025-3-24 02:01 作者: flaunt 時(shí)間: 2025-3-24 03:51 作者: Negligible 時(shí)間: 2025-3-24 08:29 作者: 暗指 時(shí)間: 2025-3-24 13:03 作者: 等待 時(shí)間: 2025-3-24 16:09
Are Transcendental Deductions Impossible?O) is a heuristic inspired by biogeography for optimization problems, where each solution is analogous to a habitat with an immigration rate and an emigration rate. BBO evolves a population of solutions by continuously migrating features probably from good solutions to poor solutions. This chapter i作者: 共和國(guó) 時(shí)間: 2025-3-24 20:38 作者: myopia 時(shí)間: 2025-3-25 01:11
,Kant’s Theory of Natural Science,s two novel migration operators, named local migration and global migration, which borrow ideas from the migration models of ecogeography to enrich information sharing among the solutions. This chapter introduces the EBO algorithm in detail and shows its significant improvement over the basic BBO an作者: 殘廢的火焰 時(shí)間: 2025-3-25 07:21 作者: AGOG 時(shí)間: 2025-3-25 08:47
,Apperception: B-Deduction § 16,s can be modeled as combinatorial optimization problems. Nowadays, with the development of transportation systems, most of such problems are high-dimensional and/or NP-hard. In recent years, we have adapted BBO algorithm to a variety of transportation problems and achieved good results.作者: instill 時(shí)間: 2025-3-25 13:17 作者: arabesque 時(shí)間: 2025-3-25 18:31 作者: 負(fù)擔(dān) 時(shí)間: 2025-3-25 21:21
Summary: The Catalogue of Design Features,ted by structural design and parameter selection. This chapter introduces how to use BBO and its variants for optimizing structures and parameters of ANNs. The results show that BBO is a powerful method for enhancing the performance of many machine learning models.作者: 愚蠢人 時(shí)間: 2025-3-26 02:15 作者: photophobia 時(shí)間: 2025-3-26 05:28 作者: 業(yè)余愛(ài)好者 時(shí)間: 2025-3-26 11:47
ribed with the help of pseudo-codes and flowcharts. The readers will learn not only the basic concepts of BBO but also how to apply and adapt the algorithms to the engineering optimization problems they actually encounter..978-981-13-4794-8978-981-13-2586-1作者: FIS 時(shí)間: 2025-3-26 14:18 作者: Exterior 時(shí)間: 2025-3-26 17:08
Biogeography-Based Optimization,O) is a heuristic inspired by biogeography for optimization problems, where each solution is analogous to a habitat with an immigration rate and an emigration rate. BBO evolves a population of solutions by continuously migrating features probably from good solutions to poor solutions. This chapter i作者: FANG 時(shí)間: 2025-3-26 23:52 作者: 脫離 時(shí)間: 2025-3-27 03:41
Ecogeography-Based Optimization: Enhanced by Ecogeographic Barriers and Differentiations,s two novel migration operators, named local migration and global migration, which borrow ideas from the migration models of ecogeography to enrich information sharing among the solutions. This chapter introduces the EBO algorithm in detail and shows its significant improvement over the basic BBO an作者: Gratulate 時(shí)間: 2025-3-27 06:42 作者: 朝圣者 時(shí)間: 2025-3-27 13:08
Application of Biogeography-Based Optimization in Transportation,s can be modeled as combinatorial optimization problems. Nowadays, with the development of transportation systems, most of such problems are high-dimensional and/or NP-hard. In recent years, we have adapted BBO algorithm to a variety of transportation problems and achieved good results.作者: 纖細(xì) 時(shí)間: 2025-3-27 14:51 作者: Vaginismus 時(shí)間: 2025-3-27 19:32
Application of Biogeography-Based Optimization in Image Processing,, we use BBO and its improved versions to a set of optimization problems in image processing, including image compression, salient object detection, and image segmentation. The results demonstrate the effectiveness of BBO in optimization problems in image processing.作者: 長(zhǎng)處 時(shí)間: 2025-3-27 22:12
Biogeography-Based Optimization in Machine Learning,ted by structural design and parameter selection. This chapter introduces how to use BBO and its variants for optimizing structures and parameters of ANNs. The results show that BBO is a powerful method for enhancing the performance of many machine learning models.作者: cunning 時(shí)間: 2025-3-28 02:04 作者: 注入 時(shí)間: 2025-3-28 10:00
Book 2019n. The algorithms and applications are organized in a step-by-step manner and clearly described with the help of pseudo-codes and flowcharts. The readers will learn not only the basic concepts of BBO but also how to apply and adapt the algorithms to the engineering optimization problems they actually encounter..作者: 可觸知 時(shí)間: 2025-3-28 13:57 作者: 消息靈通 時(shí)間: 2025-3-28 15:50
,Apperception: B-Deduction § 16, but its global exploration ability is relatively poor. Thus, those hybrid BBO algorithms often introduce effective global exploration mechanisms of other heuristic algorithms, so as to better balance the global and local search. This chapter describes some typical hybrid BBO algorithms.作者: 觀點(diǎn) 時(shí)間: 2025-3-28 18:47
of BBO to a variety of domains.Illustrates important mechan.This book introduces readers to the background, general framework, main operators, and other basic characteristics of biogeography-based optimization (BBO), which is an emerging branch of bio-inspired computation. In particular, the book p作者: 新義 時(shí)間: 2025-3-29 01:52
