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 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.

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.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

2012 ◽  
pp. 33-51
Author(s):  
Julio Cesar Ponce Gallegos ◽  
Fatima Sayuri Quezada Aguilera ◽  
José Alberto Hernandez Aguilar ◽  
Christian José Correa Villalón
Keyword(s):  
Waste Management ◽  
Garbage Collection ◽  
Optimization Problems ◽  
Decision Makers ◽  
Ant Colony ◽  
Ant Colony System ◽  
Waste Collection ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Acs Algorithm

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

Bio-Inspired Algorithm for the Shortest Path According to the Maximum Time for Each Trial

2013 ◽  
Vol 717 ◽  
pp. 455-459
Author(s):  
Seung Gwan Lee ◽  
Seung Won Lee
Keyword(s):  
Simulation Experiment ◽  
State Transition ◽  
Optimization Problems ◽  
Ant Colony ◽  
Transition Rule ◽  
Ant Colony System ◽  
Combinatorial Optimization Problems ◽  
Maximum Time ◽  
Acs Algorithm ◽  
Better Than

Ant Colony System (ACS) is a new meta heuristics algorithms to solve hard combinatorial optimization problems. In this paper, we propose hybrid ant colony algotirhm that is searching the second best edge first in the state transition rule and updating the pheromone on edges applying the visited number of edge in the globally best tour. And we evaluate the proposed algorithm according to the maximum time for each trial. The results of a simulation experiment demonstrate that the proposed algorithm is better than, or, at least as good as, that of ACS algorithm in the most sets.

Solution of Travelling Salesman Problem Using Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1206-1212
Author(s):  
Sevda Dayıoğlu Gülcü ◽  
Şaban Gülcü ◽  
Humar Kahramanli
Keyword(s):  
Discrete Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Travelling Salesman ◽  
Ant Colonies ◽  
Discrete Optimization Problems

Recently some studies have been revealed by inspiring from animals which live as colonies in the nature. Ant Colony System is one of these studies. This system is a meta-heuristic method which has been developed based upon food searching characteristics of the ant colonies. Ant Colony System is applied in a lot of discrete optimization problems such as travelling salesman problem. In this study solving the travelling salesman problem using ant colony system is aimed.

An Evaluation of Methods for Estimating the Number of Local Optima in Combinatorial Optimization Problems

2013 ◽  
Vol 21 (4) ◽  
pp. 625-658 ◽  
Author(s):  
Leticia Hernando ◽  
Alexander Mendiburu ◽  
Jose A. Lozano
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Space ◽  
Combinatorial Optimization Problem ◽  
Local Search Algorithms ◽  
Neighborhood Structure ◽  
Local Optima ◽  
Combinatorial Optimization Problems ◽  
Evaluation Of Methods

The solution of many combinatorial optimization problems is carried out by metaheuristics, which generally make use of local search algorithms. These algorithms use some kind of neighborhood structure over the search space. The performance of the algorithms strongly depends on the properties that the neighborhood imposes on the search space. One of these properties is the number of local optima. Given an instance of a combinatorial optimization problem and a neighborhood, the estimation of the number of local optima can help not only to measure the complexity of the instance, but also to choose the most convenient neighborhood to solve it. In this paper we review and evaluate several methods to estimate the number of local optima in combinatorial optimization problems. The methods reviewed not only come from the combinatorial optimization literature, but also from the statistical literature. A thorough evaluation in synthetic as well as real problems is given. We conclude by providing recommendations of methods for several scenarios.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

2013 ◽  
pp. 1809-1827 ◽  
Author(s):  
Julio Cesar Ponce Gallegos ◽  
Fatima Sayuri Quezada Aguilera ◽  
José Alberto Hernandez Aguilar ◽  
Christian José Correa Villalón
Keyword(s):  
Waste Management ◽  
Garbage Collection ◽  
Optimization Problems ◽  
Decision Makers ◽  
Ant Colony ◽  
Ant Colony System ◽  
Waste Collection ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Acs Algorithm

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

scholarly journals Ant Colony Solving Multiple Constraints Problem: Vehicle Route Allocation

2008 ◽  
Vol 3 (4) ◽  
pp. 366 ◽  
Author(s):  
Sorin C. Negulescu ◽  
Claudiu V. Kifor ◽  
Constantin Oprean
Keyword(s):  
Optimization Problems ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Multiple Constraints ◽  
Multi Agent Systems ◽  
Ant System ◽  
Travelling Salesman ◽  
Ant Colonies ◽  
Vehicle Route ◽  
Multi Agent

Ant colonies are successfully used nowadays as multi-agent systems (MAS) to solve difficult optimization problems such as travelling salesman (TSP), quadratic assignment (QAP), vehicle routing (VRP), graph coloring and satisfiability problem. The objective of the research presented in this paper is to adapt an improved version of Ant Colony Optimisation (ACO) algorithm, mainly: the Elitist Ant System (EAS) algorithm in order to solve the Vehicle Route Allocation Problem (VRAP). After a brief introduction in the first section about MAS and their characteristics, the paper presents the rationale within the second section where ACO algorithm and its common extensions are described. In the approach (the third section) are explained the steps that must be followed in order to adapt EAS for solving the VRAP. The resulted algorithm is illustrated in the fourth section. Section five closes the paper presenting the conclusions and intentions.

scholarly journals Applying the big bang-big crunch metaheuristic to large-sized operational problems

2020 ◽  
Vol 10 (3) ◽  
pp. 2484 ◽  
Author(s):  
Yousef K. Qawqzeh ◽  
Ghaith Jaradat ◽  
Ali Al-Yousef ◽  
Anmar Abu-Hamdah ◽  
Ibrahim Almarashdeh ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Job Shop ◽  
Optimization Problems ◽  
Big Bang ◽  
Quadratic Assignment ◽  
Scheduling Problems ◽  
High Quality ◽  
Combinatorial Optimization Problems ◽  
Big Crunch ◽  
The Big Bang

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

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.

Sign in / Sign up

Export Citation Format

Share Document