scholarly journals ANALYSIS AND SYNTHESIS OF ENHANCED ANT COLONY OPTIMIZATION WITH THE TRADITIONAL ANT COLONY OPTIMIZATION TO SOLVE TRAVELLING SALES PERSON PROBLEM

2011 ◽  
Vol 1 (1) ◽  
pp. 88-92
Author(s):  
Pallavi Arora ◽  
Harjeet Kaur ◽  
Prateek Agrawal
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Traditional Approach ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Analysis And Synthesis ◽  
Heuristic Technique ◽  
Travelling Salesperson Problem

Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.

Ant Colony Optimization and Multiple Knapsack Problem

2007 ◽  
pp. 498-509 ◽  
Author(s):  
S. Fidanova
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Multiple Knapsack Problem ◽  
Constraint Problem ◽  
Multiple Knapsack ◽  
Ant Colony Optimization Algorithms

The ant colony optimization algorithms and their applications on the multiple knapsack problem (MKP) are introduced. The MKP is a hard combinatorial optimization problem with wide application. Problems from different industrial fields can be interpreted as a knapsack problem including financial and other management. The MKP is represented by a graph, and solutions are represented by paths through the graph. Two pheromone models are compared: pheromone on nodes and pheromone on arcs of the graph. The MKP is a constraint problem which provides possibilities to use varied heuristic information. The purpose of the chapter is to compare a variety of heuristic and pheromone models and different variants of ACO algorithms on MKP.

scholarly journals Ant colony optimization based founder sequence reconstruction

2018 ◽  
Vol 33 (2) ◽  
Author(s):  
Anh Vu Thi Ngoc ◽  
Dinh Phuc Thai ◽  
Hoang Duc Nguyen ◽  
Thanh Hai Dang ◽  
Dong Do Duc
Keyword(s):  
Ant Colony Optimization ◽  
Evolutionary Biology ◽  
Optimization Problem ◽  
Gene Sequence ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Benchmark Datasets ◽  
Sequence Reconstruction ◽  
Test Sets ◽  
Founder Gene

Reconstruction of founder (ancestor) genes for a given population is an important problem in evolutionary biology. It involves finding a set of genes that can combine together to form genes of all individuals in that population. Such reconstruction can be modeled as a combinatorial optimization problem, in which we have to find a set of founder (gene) sequences so that the individuals in a given population can be generated by the smallest number of recombination on these founder sequences. In this paper we propose a novel ant colony optimization algorithm (ACO) based method, equipped with some important improvements, for the founder gene sequence reconstruction problem. The proposed method yields excellent performance when validating on 108 test sets from three benchmark datasets. Comparing with the best by far method for founder sequence reconstruction, our proposed method performs better in 45 test sets, equally well in 44 and worse only in 19 sets. These experimental results demonstrate the efficacy and perspective of our proposed method.

A Rank Based ACO Approach for Optimal Resource Allocation and Scheduling in FMS Modeled with Labelled Petri Net

2021 ◽  
Author(s):  
Amira Jablaoui ◽  
Hichem Kmimech ◽  
Layth Sliman ◽  
Lotfi Nabli
Keyword(s):  
Resource Allocation ◽  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Petri Net ◽  
Flexible Manufacturing ◽  
Optimization Problem ◽  
Flexible Manufacturing System ◽  
Combinatorial Optimization Problem ◽  
Optimal Resource Allocation ◽  
Optimal Resource

In this article, we study the NP-Hard combinatorial optimization problem of the minimum initial marking (MIM) computation in labeled Petri net (L-PN) while considering a sequence of labels to minimize the resource consumption in a flexible manufacturing system (FMS), and we propose an approach based on the ant colony optimization (ACO) precisely the extension Rank-based ACO to optimal resource allocation and scheduling in FMS. The ACO meta-heuristic is inspired by the behavior of ants in foraging based on pheromones deposit. The numerical results show that the proposed algorithm obtained much better results than previous studies.

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.

Optimization of Torch Movements of Welding and Cutting Using Ant Colony Method

2009 ◽  
Vol 25 (03) ◽  
pp. 136-141
Author(s):  
Yasuhisa Okumoto
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Heuristic Method ◽  
Computation Time ◽  
Optimization Method ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Hull Structure ◽  
Optimum Path ◽  
The Traveling Salesman Problem

Though many methods are applied to solve the combinatorial optimization problem, there are many cases in which the solution cannot be solved in practical computation time, even if the computer becomes more advanced. Recently the "ant colony optimization method (ACO)" has been proposed as one of the meta-heuristic method. This research tried the ACO in ship production field. Firstly, the ACO was applied and verified for the traveling salesman problem (TSP) to obtain the shortest path in many cities, as a representative combinatorial optimization problem. Next, based on the result, the ACO was applied to the problem in search of the optimum torch movement of a welding robot for the assembly of ship hull structure, and of a NC plasma cutting machine of steel plate. As a result, it was confirmed that the ACO is effective to solve the optimum path of machines.

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