HYBRID GENETIC — ANT COLONY ALGORITHMS FOR SOLVING AGGREGATE PRODUCTION PLAN

2005 ◽  
Vol 04 (01) ◽  
pp. 103-111 ◽  
Author(s):  
G. MOHAN KUMAR ◽  
A. NOORUL HAQ
Keyword(s):  
Genetic Algorithms ◽  
Long Range ◽  
Ant Colony Algorithm ◽  
Job Shop ◽  
Production Capacity ◽  
Ant Colony ◽  
Planning Problem ◽  
Production Plan ◽  
Ant Colony Optimization Algorithm ◽  
Aggregate Production

It is necessary for the management of any industry to workout an intermediate range plan also known as aggregate production plan, consistently with the long range policies and resources allocated by long range decisions. It is a procedure of translating the expected demand and production capacity of the available facilities into future manufacturing plans for a family of products. It includes decisions on production quantity, work force and inventory to workout a low cost product and timely delivery. Ant colony optimization algorithm finds its extensive application in solving job shop scheduling, assignment problems, transportation problems, etc. Genetic algorithms are proposed to solve the problem, already by the authors. In this paper, an attempt is made to solve an aggregate production-planning problem for obtaining an effective solution using ant colony algorithm. Also a hybrid algorithm that combines genetic algorithm and ant colony algorithm is proposed and its effectiveness over the models developed using genetic algorithms and ant colony algorithm is also analyzed.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

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.

Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm

2021 ◽  
Author(s):  
Mehrdad Ahmadi Kamarposhti ◽  
Ilhami Colak ◽  
Celestine Iwendi ◽  
Shahab S. Band ◽  
Ebuka Ibeke
Keyword(s):  
Power Plant ◽  
Power System ◽  
Ant Colony Algorithm ◽  
Phase Error ◽  
Low Frequency ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Power System Stabilizers ◽  
Three Phase ◽  
Optimal Coordination

Volatility leads to disruption in synchronism between generators of a continuous system. The frequency of the volatility is usually between a few tenths of Hz to several Hz. This volatility is sometimes divided into two types, local and interregional. Local volatility is the low-frequency volatility of a power plant unit or units of a power plant relative to the grid whereas interregional volatility is the volatility of the units of one area relative to the units of another area. The worst kind of low-frequency volatility occurs when the power system in a region has a short three-phase connection to the earth, creating a complete instability of the grid and operating protective systems. One of the ways to improve the dynamic stability and steady-state of the power system is to use power system stabilizers and FACTS devices in the system. In this paper, the stabilization of the power system stabilizers (PSSs) and SSSC is done using the ant colony algorithm. Studies on a four-machine system with the three-phase error were performed in two scenarios and finally compared with the PSO method. The simulation results show that the proposed method produced more accurate performance.

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

2021 ◽  
Author(s):  
Yueping Chen ◽  
Naiqi Shang
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Path Optimization ◽  
Inspection Time ◽  
Free Form ◽  
Coordinate Measuring Machines ◽  
Ant Colony Optimization Algorithm ◽  
Measuring Machine ◽  
Free Form Surfaces

Abstract Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

scholarly journals An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jiang Zhao ◽  
Dingding Cheng ◽  
Chongqing Hao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Selection Strategy ◽  
Planning Problem ◽  
Evaporation Coefficient ◽  
Mobile Vehicle ◽  
Starting Point ◽  
Path Planning Problem ◽  
Improved Ant Colony Algorithm

This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

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