scholarly journals Application of Rough Ant Colony Algorithm in Adolescent Psychology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tao Cong ◽  
Lin Jiang ◽  
Qihang Sun ◽  
Yang Li
Keyword(s):  
Big Data ◽  
Juvenile Delinquency ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Hot Spot ◽  
Rapid Development ◽  
Ant Colony ◽  
Good Effect ◽  
Adolescent Psychology ◽  
Combinatorial Optimization Problems

With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm, referred to as RoughAC, which first uses the concept of upper and lower approximate sets in rough sets to determine the degree of membership. In addition, in the ant colony algorithm, we use the membership value to update the pheromone. Experiments show that the algorithm can not only solve the premature convergence problem caused by stagnation near the local optimal solution but also solve the continuous domain and combinatorial optimization problems and achieve better classification results. Moreover, the algorithm has a good effect on predicting classification and can provide guidance for predicting the tendency of juvenile delinquency.

Study of Parameters Configuration in Ant Colony Algorithm

2014 ◽  
Vol 678 ◽  
pp. 51-54
Author(s):  
Yan Rong Cui
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Optimal Parameter ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Parameter Combination ◽  
The Impact

It has obtained a better result to use ant colony algorithm to solve complex combinatorial optimization problems, but different value of the parameters in ant colony algorithm affects the performance of the algorithm. This paper studies the configuration of parameters in ant colony algorithm, and analyses the impact of the key parameters of the algorithm, and obtains the optimal parameter combination of using ant colony algorithm to solve TSP problems by using EIL51TSP data to simulate.

Approximate Inference Algorithm Based on Adaptive Ant Colony and Artificial Fish Swarm Algorithm for Credal Networks

2011 ◽  
Vol 219-220 ◽  
pp. 1504-1508
Author(s):  
Ying Qu ◽  
Pang Zhou
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Directed Acyclic Graphs ◽  
Ant Colony ◽  
Approximate Inference ◽  
Artificial Fish Swarm Algorithm ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

This paper presents a new algorithm for approximate inference in credal networks (that is, models based on directed acyclic graphs and interval-valued probabilities). Approximate inference in credal networks can be considered as multistage decision in this paper. It is looked as combinatorial optimization problems that obtaining the extreme posteriors from the combinations of various vertices in credal networks. Based on this, the paper combines two intelligence swarm algorithms (ant colony algorithm and artificial fish swarm algorithm) to obtain interval posterior probabilities of query variable for the states of given evidence variables.

Intelligent selective disassembly using the ant colony algorithm

2003 ◽  
Vol 17 (4) ◽  
pp. 325-333 ◽  
Author(s):  
J.F. WANG ◽  
J.H. LIU ◽  
S.Q. LI ◽  
Y.F. ZHONG
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Solution Space ◽  
Ant Colony ◽  
Environmentally Conscious ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Selective Disassembly ◽  
Disassembly Matrix

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

scholarly journals A Hybrid Ant Colony Algorithm for Quadratic Assignment Problem

2013 ◽  
Vol 7 (1) ◽  
pp. 51-54 ◽  
Author(s):  
Guo Hong
Keyword(s):  
Ant Colony Optimization ◽  
Assignment Problem ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Optimal Solutions ◽  
Combinatorial Optimization Problems

Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization problems. Ant colony optimization (ACO) is one of new bionic optimization algorithms and it has some characteristics such as parallel, positive feedback and better performances. ACO has achieved in solving quadratic assignment problems. However, its solution quality and its computation performance need be improved for a large scale QAP. In this paper, a hybrid ant colony optimization (HACO) has been proposed based on ACO and particle swarm optimization (PSO) for a large scale QAP. PSO algorithm is combined with ACO algorithm to improve the quality of optimal solutions. Simulation experiments on QAP standard test data show that optimal solutions of HACO are better than those of ACO for QAP.

Ship Pipe Routing Design Using the ACO with Iterative Pheromone Updating

2007 ◽  
Vol 23 (01) ◽  
pp. 36-45 ◽  
Author(s):  
Fan Xiaoning ◽  
Lin Yan ◽  
Ji Zhuoshang
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Stochastic Search ◽  
Ant Colony ◽  
Pipe Material ◽  
Combinatorial Optimization Problems ◽  
Routing Design ◽  
Pipe Routing

Ship pipe routing design (SPRD) that belongs to non-deterministic polynomial (NP)-hard problem concerns minimizing the cost of pipe material while satisfying constraints and avoiding obstacles. Currently, this total solution mainly depends on human experts. The stochastic search algorithms suitable for computer technology provide the opportunity to automate and optimize it. The Ant Colony Optimization (ACO) is an effective metaheuristic and stochastic search technique to solve combinatorial optimization problems by using principle of pheromone information. Based on ACO, the method of ant colony algorithm with iterative pheromone updating is first proposed to solve ship pipeline routing in three-dimensional space. Simulation results show that the new updating approach of pheromone is feasible and effective. Mean-while, the performance and computer processing time of the proposed algorithm outperform the original ACO used to generate SPRD solutions.

scholarly journals Dynamic ant colony algorithm based on cw saving Value

2018 ◽  
Vol 246 ◽  
pp. 03015
Author(s):  
Jiang-Gu Yao ◽  
Jian Gao
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Combinatorial Optimization Problems

As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.

Ant Colony Optimization Algorithm in Intrusion Detection and Positive

2013 ◽  
Vol 443 ◽  
pp. 541-545
Author(s):  
Qian Zou ◽  
Hua Jun Wang ◽  
Wei Huang ◽  
Jin Pan
Keyword(s):  
Combinatorial Optimization ◽  
Intrusion Detection ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Combinatorial Optimization Problems

Ant colony algorithm is an effective algorithm to solve combinatorial optimization problems, it has many good features, and there are also some disadvantages. In this paper, through research on ant colony optimization algorithm, apply it in intrusion detection. Then it gives an improved ant colony optimization algorithm. Tests show that the algorithm improves the efficiency of intrusion detection, reduces false positives of intrusion detection.

An Adaptive Ant Colony Algorithm Improved and Simulation

2011 ◽  
Vol 87 ◽  
pp. 209-212
Author(s):  
Yue Shun He ◽  
Xiang Li
Keyword(s):  
Large Scale ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Time ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Algorithm Performance ◽  
Combinatorial Optimization Problems ◽  
Accelerate Convergence ◽  
Scale Optimization

Ant colony algorithm is a new evolutionary algorithm, Ant colony algorithm is widely used to solve combinatorial optimization problems, But the ant colony algorithm has slow convergence speed and prone to stagnation phenomenon. This paper presents an evolution strategy based on adaptive selection and dynamic adjustment to improve ant colony algorithm, the simulation results show that the algorithm performance significantly improved, this method can not only accelerate convergence rate, and save search time, but also can overcome premature stagnation of behavior, and to find a better solution. This is very favorable for solving large-scale optimization problem.

scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

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