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生物地理學優化算法與應用

包郵 生物地理學優化算法與應用

作者:鄭宇軍等
出版社:科學出版社出版時間:2019-06-01
開本: 16開 頁數: 228
中 圖 價:¥70.4(5.5折) 定價  ¥128.0 登錄后可看到會員價
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生物地理學優化算法與應用 版權信息

  • ISBN:9787030605283
  • 條形碼:9787030605283 ; 978-7-03-060528-3
  • 裝幀:暫無
  • 冊數:暫無
  • 重量:暫無
  • 所屬分類:>

生物地理學優化算法與應用 本書特色

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 presents the authors’ recent work on improved variants of BBO, hybridization of BBO with other algorthms, and the application of BBO to a variety of domains including transporta-tion, image processing, and neural network learning. The content will help to advance research and application of not only BBO but het whole field of bio-inspired compu-tation. The algorithms and applications are organized in a step-by-step manner and clearly described with the help of pscudo-codes and flowcharts. The readers will learn not only the basic concepts of BBO but also how to apply and adapt the lagorithms to the engineering optimization problems they actually encounter.

生物地理學優化算法與應用 內容簡介

生物地理學優化是生物啟發計算中的一種新興算法。本書介紹了生物地理學優化的背景、基本框架、主要操作和其它基本特征。特別的,本書介紹了作者及其研究團隊對生物地理學優化的兩個重要改進,生物地理學優化算法與其它啟發式算法的融合,以及算法在交通運輸、作業調度、圖像處理、神經網絡訓練等多個領域的應用成果。本書內容對生物地理學優化以及生物啟發計算的研究和應用均有重要的促進作用。

生物地理學優化算法與應用 目錄

Contents1 Optimization Problems and Algorithms 11.1 Introduction 11.2 Optimization Problems 21.2.1 Continuous Optimization Problems 21.2.2 Combinatorial Optimization Problems 51.3 Exact Optimization Algorithms 71.3.1 Gradient-Based Algorithms 71.3.2 Linear Programming Algorithm 81.3.3 Branch-and-Bound 91.3.4 Dynamic Programming 111.4 Heuristic Optimization Algorithms 121.4.1 Genetic Algorithms 121.4.2 Simulated Annealing 141.4.3 Ant Colony Optimization 161.4.4 Particle Swarm Optimization 171.4.5 Differential Evolution 181.4.6 Harmony Search 191.4.7 Fireworks Algorithm 211.4.8 Water Wave Optimization 221.5 Summary 24References 242 Biogeography-Based Optimization 272.1 Introduction 272.2 Background of Biogeography 272.3 The Basic Biogeography-Based Optimization Algorithm 322.3.1 The Migration Operator 322.3.2 The Mutation Operator 332.3.3 The Algorithmic Framework 342.3.4 Comparison with Some Classical Heuristics 352.4 Recent Advances of Biogeography-Based Optimization 362.4.1 Improved Biogeography-Based Optimization Algorithms 362.4.2 Adaption of BBO for Constrained Optimization 402.4.3 Adaption of BBO for Multi-objective Optimization 432.4.4 Adaption of BBO for Combinatorial Optimization 452.5 Summary 47References 473 Localized Biogeography-Based Optimization: Enhanced by Local Topologies 513.1 Introduction 513.2 Population Topology 513.2.1 Global Topology 513.2.2 Local Topologies 533.2.3 Research of Heuristic Algorithms with Local Topologies 563.3 Localized Biogeography-Based Optimization Algorithms 573.3.1 Local-BBO with the Ring Topology 573.3.2 Local-BBO with the Square Topology 583.3.3 Local-BBO with the Random Topology 583.4 Computational Experiments 613.5 Summary 66References 664 Ecogeography-Based Optimization: Enhanced by Ecogeographic Barriers and Differentiations 694.1 Introduction 694.2 Background of Ecogeography 694.3 The Ecogeography-Based Optimization Algorithm 714.3.1 Local Migration and Global Migration 714.3.2 Migration Based on Maturity 724.3.3 The Algorithmic Framework of EBO 724.4 Computational Experiments 734.4.1 Experimental Settings 734.4.2 Impact of the Immaturity Index η 744.4.3 Comparison of the 10-D Functions 744.4.4 Comparison of the 30-D Functions 784.4.5 Comparison of the 50-D Functions 834.4.6 Discussion 834.5 Summary 86References .875 Hybrid Biogeography-Based Optimization Algorithms 895.1 Introduction 895.2 Hybridization with Differential Evolution 895.2.1 The DE/BBO Algorithm 895.2.2 Local-DE/BBO 915.2.3 Self-adaptive DE/BBO 975.3 Hybridization with Harmony Search 1045.3.1 Biogeographic Harmony Search 1045.3.2 Computational Experiments 1055.4 Hybridization with Fireworks Algorithm 1095.4.1 A Hybrid BBO and FWA Algorithm 1095.4.2 Computational Experiments 1105.5 Summary 114References 1146 Application of Biogeography-Based Optimization in Transportation 1176.1 Introduction 1176.2 BBO for General Transportation Planning 1176.2.1 A General Transportation Planning Problem 1176.2.2 BBO Algorithms for the Problem 1196.2.3 Computational Experiments 1196.3 BBO for Emergency Transportation Planning 1236.3.1 An Emergency Transportation Planning Problem 1236.3.2 A BBO Algorithm for the Problem 1246.3.3 Computational Experiments 1256.4 BBO for Emergency Railway Wagon Scheduling 1276.4.1 An Emergency Railway Wagon Scheduling Problem 1286.4.2 A Hybrid BBO/DE Algorithm for the Problem 1316.4.3 Computational Experiments 1346.5 BBO for Emergency Air Transportation 1376.5.1 An Emergency Air Transportation Problem 1376.5.2 BHS and EBO Algorithms for the Problem 1396.5.3 Computational Experiments 1396.6 Summary 140References 1417 Application of Biogeography-Based Optimization in Job Scheduling 1437.1 Introduction 1437.2 BBO for Flow-Shop Scheduling 1437.2.1 Flow-Shop Scheduling Problem 1437.2.2 A BBO Algorithm for FSP 1467.2.3 Computational Experiments 1477.3 BBO for Job-Shop Scheduling 1497.3.1 Job-Shop Scheduling Problem 1497.3.2 An Enhanced BBO Algorithm for the Problem 1517.3.3 Computational Experiments 1537.4 BBO for Maintenance Job Assignment and Scheduling 1567.4.1 A Maintenance Job Assignment and Scheduling Problem 1567.4.2 A Multi-objective BBO Algorithm for the Problem 1587.4.3 Computational Experiments 1607.5 BBO for University Course Timetabling 1637.5.1 A University Course Timetabling Problem 1637.5.2 A Discrete EBO Algorithm for UCTP 1667.5.3 Computational Experiments 1697.6 Summary 173References 1738 Application of Biogeography-Based Optimization in Image Processing 1778.1 Introduction 1778.2 BBO for
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