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Swarm Intelligence and Bio-Inspired Computation

by Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, Mehmet Karamanoglu

May 2013

Intermediate to advanced

450 pages

13h 16m

English

Elsevier

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1.1 Introduction1.2 Current Issues in Bio-Inspired Computing1.3 Search for the Magic Formulas for Optimization1.4 Characteristics of Metaheuristics1.5 Swarm-Intelligence-Based Algorithms1.6 Open Problems and Further Research TopicsReferences
2.1 Introduction2.2 Optimization Problems2.3 Swarm Intelligence–Based Optimization Algorithms2.4 Numerical Examples2.5 Summary and ConclusionsReferences
3.1 Introduction3.2 Metaheuristic Algorithms3.3 Lévy Flights in Global Optimization3.4 Metaheuristic Algorithms Based on Lévy Probability Distribution: Is It a Good Idea?3.5 Discussion3.6 ConclusionsReferences

4.1 Introduction4.2 Optimization Problems and Their Complexity4.3 Memetic Self-Adaptive Firefly Algorithm4.4 Case Study: Graph 3-Coloring4.5 ConclusionsReferences
5.1 Introduction5.2 Ant Colony’s Labor Division Behavior and its Modeling Description5.3 Modeling and Simulation of Ant Colony’s Labor Division with Multitask5.4 Modeling and Simulation of Ant Colony’s Labor Division with Multistate5.5 Modeling and Simulation of Ant Colony’s Labor Division with Multiconstraint5.6 Concluding RemarksAcknowledgmentReferences
6.1 Introduction6.2 Convergence Analysis6.3 Performance Illustration6.4 Application in Hidden Markov Models6.5 ConclusionsReferences
7.1 Introduction7.2 Swarm Algorithms7.3 Main Concerns to Handle Discrete Problems7.4 Applications to Discrete Problems7.5 Discussion7.6 Concluding Remarks and Future ResearchReferences
8.1 Introduction8.2 A Collection of Test Functions for GO8.3 ConclusionsReferences
9.1 Introduction9.2 Bat Algorithm9.3 Binary Bat Algorithm9.4 Optimum-Path Forest Classifier9.5 Binary Bat Algorithm9.6 Experimental Results9.7 ConclusionsReferences
10.1 Introduction10.2 Unsupervised Intelligent Composition10.3 Supervised Intelligent Composition10.4 Interactive Intelligent Composition10.5 ConclusionsReferences
11.1 Introduction11.2 Cuckoo Search Algorithm11.3 Modifications and Developments11.4 Applications11.5 ConclusionReferences
12.1 Introduction12.2 Semantic Web12.3 Constituent Models12.4 Neuro-Fuzzy System for the Web Content Filtering: Application12.5 ConclusionsReferences
13.1 Introduction13.2 Evolutionary Discrete Firefly Algorithm13.3 A New DFA for the TSP13.4 Result and Discussion13.5 ConclusionAcknowledgmentReferences
14.1 Introduction14.2 Modeling to Generate Alternatives14.3 FA for Function Optimization14.4 FA-Based Concurrent Coevolutionary Computational Algorithm for MGA14.5 Computational Testing of the FA Used for MGA14.6 An SO Approach for Stochastic MGA14.7 Case Study of Stochastic MGA for the Expansion of Waste Management Facilities14.8 ConclusionsReferences
15.1 Introduction15.2 Krill Herd Algorithm15.3 Implementation and Numerical Experiments15.4 Conclusions and Future ResearchReferences
16.1 Introduction16.2 Primary APOA16.3 Standard APOA16.4 ConclusionAcknowledgmentReferences
17.1 Introduction17.2 Literature Review17.3 Optimization Model17.4 Solution Procedure by Genetic Algorithm17.5 Results and Analysis17.6 ConclusionReferences
18.1 Introduction18.2 Challenges in Data Mining18.3 Bio-Inspired Optimization Metaheuristics18.4 The Convergence18.5 ConclusionReferences
19.1 Introduction19.2 The Improved PSO Algorithm19.3 Stochastic Optimization of Multiechelon Supply Chain Model19.4 ConclusionAcknowledgmentReferences

Content preview from Swarm Intelligence and Bio-Inspired Computation

Discrete Firefly Algorithm for Traveling Salesman Problem

A New Movement Scheme

Gilang Kusuma Jati¹, Ruli Manurung¹ and Suyanto², ¹Faculty of Computer Science, Universitas Indonesia, Kampus UI, Depok, Jawa Barat, Indonesia, ²Faculty of Informatics, Telkom School of Technology, Jl. Telekomunikasi No. 1, Terusan Buah Batu, Bandung, Jawa Barat, Indonesia

13.1 Introduction

The traveling salesman problem (TSP) is one of the most studied problems and has received growing attention in artificial intelligence, computational mathematics, and optimization theory in recent years. The traveling salesman problem (TSP) is one of the most studied problems and has received growing attention in artificial intelligence, computational mathematics, and ...

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Publisher Resources

ISBN: 9780124051638

Swarm Intelligence and Bio-Inspired Computation

by Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, Mehmet Karamanoglu

Discrete Firefly Algorithm for Traveling Salesman Problem

A New Movement Scheme

13.1 Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

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Discrete Firefly Algorithm for Traveling Salesman Problem

A New Movement Scheme

13.1 Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

You might also like

Bio-inspired Algorithms for Engineering

Swarm Intelligence Algorithms (Two Volume Set)

Agent-Based Modeling and Simulation with Swarm

Distributed Computing Through Combinatorial Topology

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.