scholarly journals ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rafid Sagban ◽  
Ku Ruhana Ku-Mahamud ◽  
Muhamad Shahbani Abu Bakar
Keyword(s):  
Ant Colony Optimization ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Optimization Algorithms

A statistical machine learning indicator,ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

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.

scholarly journals Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Abdulqader M. Mohsen
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Mutation Operator ◽  
Combinatorial Optimization Problems ◽  
Wide Range

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

scholarly journals An Early Exploratory Method to Avoid Local Minima in Ant Colony System

1970 ◽  
Vol 10 (1) ◽  
pp. 33-41
Author(s):  
Thanet Satukitchai ◽  
Kietikul Jearanaitanakij
Keyword(s):  
Job Shop ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Search Space ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Ant Colony System ◽  
Ant System ◽  
Local Optima ◽  
Combinatorial Optimization Problems

Ant Colony Optimization (ACO) is a famous technique for solving the Travelling Salesman Problem (TSP.) The first implementation of ACO is Ant System. Itcan be used to solve different combinatorial optimization problems, e.g., TSP, job-shop scheduling, quadratic assignment. However, one of its disadvantages is that it can be easily trapped into local optima. Although there is an attempt by Ant Colony System (ACS) to improve the local optima by introducing local pheromone updating rule, the chance of being trapped into local optima still persists. This paper presents an extension of ACS algorithm by modifying the construction solution phase of the algorithm, the phase that ants move and build their tours, to reduce the duplication of tours produced by ants. This modification forces ants to select unique path which has never been visited by other ants in the current iteration. As a result, the modified ACS can explore more search space than the conventional ACS. The experimental results on five standard benchmarks from TSPLIB show improvements on both the quality and the number of optimal solutions founded.

scholarly journals The Hybrid ColorAnt-RT Algorithms and an Application to Register Allocation

2015 ◽  
Vol 18 (55) ◽  
pp. 81
Author(s):  
Mauro Mulati, ◽  
Carla Lintzmayer ◽  
Anderson Da Silva
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Register Allocation ◽  
Ant Colony ◽  
Frequency Assignment ◽  
It Use ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Optimization Algorithms

Ant Colony Optimization is a metaheuristic used to create heuristic algorithms to find good solutions for combinatorial optimization problems. This metaheuristic is inspired on the effective behavior present in some species of ants of exploring the environment to find and transport food to the nest. Several works have proposed using Ant Colony Optimization algorithms to solve problems such as vehicle routing, frequency assignment, scheduling and graph coloring. The graph coloring problem essentially consists in finding a number k of colors to assign to the vertices of a graph, so that there are no two adjacent vertices with the same color. This paper presents the hybrid ColorAnt-RT algorithms, a class of algorithms for graph coloring problems which is based on the Ant Colony Optimization metaheuristic and uses Tabu Search as local search. The experiments with ColorAnt-RT algorithms indicate that changing the way to reinforce the pheromone trail results in better results. In fact, the results with ColorAnt-RT show that it is a promising option in finding good approximations of k. The good results obtained by ColorAnt-RT motivated it use on a register allocation based on Ant Colony Optimization, called CARTRA. As a result, this paper also presents CARTRA, an algorithm that extends a classic graph coloring register allocator to use the graph coloring algorithm ColorAnt-RT. CARTRA minimizes the amount of spills, thereby improving the quality of the generated code.

ROUTING IN OPTICAL MULTISTAGE NETWORKS WITH LIMITED CROSSTALK USING ANT COLONY OPTIMIZATION

2005 ◽  
Vol 16 (02) ◽  
pp. 301-320 ◽  
Author(s):  
AJAY K. KATANGUR ◽  
SOMASHEKER AKKALADEVI ◽  
YI PAN ◽  
MARTIN D. FRASER
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Optimization Problems ◽  
Interconnection Network ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Scheduling Problems ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Number Of Passes

Ant Colony Optimization (ACO) techniques can be successfully implemented to solve many combinatorial optimization problems. After the traveling salesman problem was successfully solved using the ACO technique, other researchers have concentrated on solving other problems like the quadratic assignment and the job-shop scheduling problems using the same technique. In this paper we use the ACO technique to route messages through an N × N Optical Multistage Interconnection Network (OMIN) allowing upto ' C ' limited crosstalk's (conflicts between messages within a switch) where ' C ' is a technology driven parameter and is always less than log 2 N . Messages with switch conflicts satisfying the crosstalk constraint are allowed to pass in the same group, but if there is any link conflict, then messages have to be routed in a different group. The focus is to minimize the number of passes required for routing allowing upto ' C ' limited crosstalks in an N × N optical network. This routing problem is an NP-hard problem. In this paper we show how the ACO technique can be applied to the routing problem and compare the performance of the ACO technique to that of the degree-descending algorithm using simulation techniques. Finally the lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using the two algorithms discussed. The results obtained show that the ACO technique performs better than the degree-descending algorithm and is quite close to optimal algorithms to the problem.

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