Research on path optimization of ant colony algorithm Improved Particle Swarm Optimization and Reverse Learning

In this paper, an improved particle swarm optimization-ant colony algorithm (PSO-ACO) is presented by inserting delete-crossover strategy into it for the shortcoming which PSO-ACO can’t solve the large-scale TSP. The experiments results show that the PSO-ACO has better performance than ant colony algorithm (ACO) on searching the shortest paths, error and robustness for the TSP.

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A New Hybrid Method Based on Improved Particle Swarm Optimization, Ant Colony Algorithm and HMM for Web Information Extraction

International Journal of Simulation Systems Science & Technology ◽

10.5013/ijssst.a.17.45.39 ◽

2016 ◽

Author(s):

Guest Editor Jianping Du

Keyword(s):

Particle Swarm Optimization ◽

Information Extraction ◽

Hybrid Method ◽

Ant Colony Algorithm ◽

Particle Swarm ◽

Ant Colony ◽

Web Information Extraction ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Web Information

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MCM Interconnect Test Scheme Based on Ant Colony Algorithm and Particle Swarm Optimization Algorithm

2009 Third International Conference on Genetic and Evolutionary Computing ◽

10.1109/wgec.2009.114 ◽

2009 ◽

Author(s):

Chen Lei ◽

Zhu Xia

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Ant Colony Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Ant Colony ◽

Test Scheme ◽

Swarm Optimization ◽

Interconnect Test

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A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

Energy Conversion and Management ◽

10.1016/j.enconman.2011.08.004 ◽

2012 ◽

Vol 53 (1) ◽

pp. 75-83 ◽

Cited By ~ 123

Author(s):

Mustafa Servet Kıran ◽

Eren Özceylan ◽

Mesut Gündüz ◽

Turan Paksoy

Keyword(s):

Particle Swarm Optimization ◽

Energy Demand ◽

Ant Colony Algorithm ◽

Hybrid Approach ◽

Particle Swarm ◽

Ant Colony ◽

Swarm Optimization

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Improved DV-Hop Algorithm Based on Ant Colony Algorithm and Particle Swarm Optimization for Wireless Sensor Network Location Problem

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/msota2018/27588 ◽

2019 ◽

Author(s):

Hai YANG

Keyword(s):

Particle Swarm Optimization ◽

Wireless Sensor Network ◽

Sensor Network ◽

Ant Colony Algorithm ◽

Location Problem ◽

Particle Swarm ◽

Ant Colony ◽

Wireless Sensor ◽

Swarm Optimization ◽

Network Location

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Parameter Optimization of Ant Colony Algorithm Based on Particle Swarm Optimization

Information Computing and Automation ◽

10.1142/9789812799524_0321 ◽

2008 ◽

Cited By ~ 1

Author(s):

Yuntao Dai ◽

Liqiang Liu ◽

Shujuan Wang

Keyword(s):

Particle Swarm Optimization ◽

Parameter Optimization ◽

Ant Colony Algorithm ◽

Particle Swarm ◽

Ant Colony ◽

Swarm Optimization

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Review of Task Scheduling Algorithm Research in Cloud Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.3236 ◽

2014 ◽

Vol 926-930 ◽

pp. 3236-3239 ◽

Cited By ~ 5

Author(s):

Mei Geng Huang ◽

Zhi Qi Ou

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Particle Swarm Optimization ◽

Task Scheduling ◽

Ant Colony Algorithm ◽

Particle Swarm ◽

Convergence Speed ◽

Ant Colony ◽

Swarm Optimization ◽

Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

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