Ant Colony Algorithm for Single Stage Supply Chain

2014 ◽  
pp. 131-142
Author(s):  
R. Sridharan ◽  
Vinay V. Panicker
Keyword(s):  
Supply Chain ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Allocation Problem ◽  
Single Stage ◽  
Ant Colony ◽  
Fixed Cost ◽  
Modeling And Analysis

Swarm intelligence has emerged as an approach for developing meta-heuristics to solve combinatorial optimization problems. Ant Colony Optimization (ACO) is an example for a swarm-intelligence based meta-heuristic inspired by the social behavior of colonies of ants. In this chapter, an ACO-based heuristic is proposed for solving a distribution-allocation problem in a single-stage of a supply chain. Thus, this work aims at modeling and analysis of the distribution-allocation problem in a single-stage supply chain with a fixed cost for a transportation route. In addition, it provides an insight for researchers in developing heuristics based on ant colony optimization for supply chain related problems.

Ant Colony Algorithm for Two Stage Supply Chain

2014 ◽  
pp. 143-155
Author(s):  
R. Sridharan ◽  
Vinay V. Panicker
Keyword(s):  
Supply Chain ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Transportation Cost ◽  
Allocation Problem ◽  
Ant Colony ◽  
Computational Time ◽  
Fixed Cost ◽  
Two Stage ◽  
Transportation Routes

This chapter focuses on the distribution-allocation problem with fixed cost for transportation routes in a two-stage supply chain. The supply chain considered in this research consists of suppliers, distributors and customers. Each transportation route is associated with a fixed charge (or a fixed cost) and a transportation cost per unit transported. The presence of this fixed cost makes the problem difficult to solve. This motivates the researchers to develop heuristics based on non-traditional optimization techniques that can provide near-optimal solutions in reasonable time. In this research, an ant colony optimization based heuristic is proposed to solve a distribution-allocation problem with fixed cost for transportation routes in a two-stage supply chain. The comparative analysis carried out in this study reveals that the solutions obtained using proposed heuristic are better than those obtained using an existing heuristic in terms of total cost and computational time. In addition, special emphasis is placed in developing heuristics based on ant colony optimization for solving supply chain related problems and identifying opportunities for further research in this area.

scholarly journals Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP)

2019 ◽  
Vol 8 (2) ◽  
pp. 32 ◽  
Author(s):  
Saman M. Almufti ◽  
Ridwan Boya Marqas ◽  
Renas R. Asaad
Keyword(s):  
Comparative Study ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Initial State ◽  
Symmetric Traveling Salesman Problem

Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems.Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimi-zation problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way be-tween its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm (ACO).In this paper, a Comparative study has been done between the previous mentioned algorithms (EHO, U-TACO) in solving Symmetric Traveling Salesman Problem (STSP) which is one of the most well-known NP-Hard problems in the optimization field. The paper pro-vides tables for the results obtained by EHO and U-TACO for various STSP problems from the TSPLIB95.

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.

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.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

scholarly journals High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

2021 ◽  
Author(s):  
Breno A. de Melo Menezes ◽  
Nina Herrmann ◽  
Herbert Kuchen ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Ant Colony Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Programming Model ◽  
Parallel Implementation ◽  
Ant Colony ◽  
Algorithmic Skeletons ◽  
Low Level ◽  
Programming Patterns ◽  
High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

Distributed supply chain management using ant colony optimization

2009 ◽  
Vol 199 (2) ◽  
pp. 349-358 ◽  
Author(s):  
C.A. Silva ◽  
J.M.C. Sousa ◽  
T.A. Runkler ◽  
J.M.G. Sá da Costa
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Chain Management

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.

A two-stage supply chain problem with fixed costs: An ant colony optimization approach

2018 ◽  
Vol 204 ◽  
pp. 214-226 ◽  
Author(s):  
Jiangtao Hong ◽  
Ali Diabat ◽  
Vinay V. Panicker ◽  
Sridharan Rajagopalan
Keyword(s):  
Supply Chain ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Optimization Approach ◽  
Fixed Costs ◽  
Two Stage ◽  
Chain Problem
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