An Obstacle Avoidance Strategy to Ant Colony Optimization Algorithm for Classification in Event Logs

Vijaykumar, V.; Radhakrishnan, Chandrasekar; Srinivasan, T.

doi:10.1109/iccis.2006.252326

Cited by 2 publications

(5 citation statements)

References 19 publications

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“…Based on the value of path-to-path (P2P) and term-to-term (T2T) relationship, ADC may be determined (Vijaykumar et al 2006). In P2P relationship, two adjacent paths form a pair of paths P ij must be disjoint in nature and not converging, whereas in T2T relationship T ij that shows the similarity between successive terms (Vijaykumar et al 2006).…”

Section: Csa-aco Optimisation Techniquementioning

confidence: 99%

“…First, an ant starts with initial condition (Vijaykumar et al 2006); based on the value of ADC, we form three categories of rules as follows.…”

Section: Csa-aco Optimisation Techniquementioning

confidence: 99%

“…Then the CSA, depending on the swarm affinity, protects the convergence and maintains the diversity by producing new replicas. The CSA-ACO optimisation algorithm is given in Algorithm 2 (Vijaykumar et al 2006). Step 1.…”

Section: Csa-aco Optimisation Techniquementioning

confidence: 99%

“…But APF method may fall into local minima and may not avoid the similar obstacles (Dorigo & Di Caro 1999). Ant colony optimisation (ACO) is inspired by the ability of ants to determine the shortest path between their nest and destination, avoiding obstacles without considering the local minima problem (Vijaykumar et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Das

Subudhi

Pati

2016

Journal of Marine Engineering & Technology

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This paper presents the cooperative control of a team of autonomous underwater vehicles (AUVs) in the presence of obstacles under environmental disturbance. A leader-follower formation control based on optimisation algorithms using communication topology is designed to navigate towards the target in the presence of obstacles. CLONAL selection optimisation algorithm may be employed as team controller by providing the optimal position to hierarchical control strategy for each AUV incorporating learning, memory and affinity. But unfortunately CLONAL selection optimisation algorithm fails to avoid obstacles because some of the lymphocytes become memory cells of the system. The above prematurity may be overcome by using artificial potential fields and ant colony optimisation technique combined with CLONAL selection optimisation algorithm. The efficacy of the proposed optimisation is verified through MATLAB simulation and the result confirmed the robustness and proficiency of proposed technique over CLONAL selection optimisation algorithm.

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Section: Csa-aco Optimisation Techniquementioning

confidence: 99%

“…First, an ant starts with initial condition (Vijaykumar et al 2006); based on the value of ADC, we form three categories of rules as follows.…”

Section: Csa-aco Optimisation Techniquementioning

confidence: 99%

Section: Csa-aco Optimisation Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Das

Subudhi

Pati

2016

Journal of Marine Engineering & Technology

View full text Add to dashboard Cite

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“…An iterative process of calculating rules followed by removal of the training cases satisfying the rule to assign them their classes was followed. In [1], the authors found out that the formation of rules took an unusually longer time in certain cases. They have termed the phenomena as the presence of obstacles meaning that there is a high degree of change between the formations of successive rules -termed as Average Degree of Change or a large amount of time is taken for the rule formation.…”

Section: Introduction and Motivations For Our Workmentioning

confidence: 97%

Evaluating an Obstacle Avoidance Strategy to Ant Colony Optimization Algorithm for Classification in Event Logs

Radhakrishnan

Suresh

Ponnambalam

2006

2006 International Conference on Advanced Computing and Communications

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Classification using Ant Colony Optimization(ACO) algorithm provides a very good technique for users to understand the data obtained from event log files, which can further help in building a system profile and determining whether intrusions have taken place in the system. To evaluate the Obstacle Avoidance Strategy, the parameters used are along the lines of simplicity of rules formed, number of terms present in the rules and also the predictive accuracy of the test data on the training set using the rules obtained. We have tried to analyze changes in the rule formation process for different thresholds, and for different times within the process of generating rules. We show through our evaluation that the Obstacle Avoidance Strategy to ACO performs better than the popular Ant-Miner algorithm by building simple rules with an improved predictive accuracy.

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An Obstacle Avoidance Strategy to Ant Colony Optimization Algorithm for Classification in Event Logs

Cited by 2 publications

References 19 publications

Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Evaluating an Obstacle Avoidance Strategy to Ant Colony Optimization Algorithm for Classification in Event Logs

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