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.

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.

Intelligent selective disassembly using the ant colony algorithm

2003 ◽  
Vol 17 (4) ◽  
pp. 325-333 ◽  
Author(s):  
J.F. WANG ◽  
J.H. LIU ◽  
S.Q. LI ◽  
Y.F. ZHONG
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Solution Space ◽  
Ant Colony ◽  
Environmentally Conscious ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Selective Disassembly ◽  
Disassembly Matrix

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

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.

Ant Colony Algorithms for Steiner Trees

2011 ◽  
pp. 1551-1575 ◽  
Author(s):  
Gurdip Singh ◽  
Sanjoy Das ◽  
Shekhar V. Gosavi ◽  
Sandeep Pujar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Steiner Trees ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Graph Theoretic ◽  
Ant Colony Algorithms ◽  
Complex Optimization

This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the proposed algorithm is also described, which is applied to the specific problem of data-centric routing in wireless sensor networks. This research illustrates how tree based graph theoretic computations can be accomplished by means of purely local ant interaction. The authors hope that this work will demonstrate how innovative ways to carry out ant interactions can be used to design effective ant colony algorithms for complex optimization problems.

Ant Colony Optimization of Multilayer Shielding for Mixed Neutron and Gamma Radiations: A Preliminary Study

2017 ◽  
Author(s):  
Muhammad Arif Bin Sazali ◽  
Nahrul Khair Alang Md Rashid ◽  
Khaidzir Hamzah
Keyword(s):  
Ant Colony Optimization ◽  
Gamma Rays ◽  
Radiation Spectrum ◽  
Optimization Problems ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Final Layer ◽  
Preliminary Study ◽  
Gamma Radiations ◽  
Combination Of Material

Mixed neutron and gamma radiations require different shielding materials as their interaction with materials is different. Composites were developed in order to combine the shielding capabilities of different materials. However, their homogeneity is difficult to be assured which can lead to pinholes where radiation can penetrate. To avoid this problem, several materials arranged in layers can be used to shield against mixed radiations. Since the multilayer shielding can be made from any material in many configurations, the ant colony optimization (ACO) is a promising method because it deals with combinatorial optimization problems. The candidate materials are HDPE, boron, cadmium, gadolinium, tungsten, bismuth, and iron. Preliminary MCNP simulations were done to observe the effect of arrangements, thicknesses, and types of materials on the radiation spectrum. It was found that: (1) the final layer should be made of high density material, (2) an increase beyond certain thicknesses did not result in a significant increase in attenuation, and (3) there should be an optimum combination of material that can effectively shield against both neutrons and gamma rays.

scholarly journals Application of Rough Ant Colony Algorithm in Adolescent Psychology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tao Cong ◽  
Lin Jiang ◽  
Qihang Sun ◽  
Yang Li
Keyword(s):  
Big Data ◽  
Juvenile Delinquency ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Hot Spot ◽  
Rapid Development ◽  
Ant Colony ◽  
Good Effect ◽  
Adolescent Psychology ◽  
Combinatorial Optimization Problems

With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm, referred to as RoughAC, which first uses the concept of upper and lower approximate sets in rough sets to determine the degree of membership. In addition, in the ant colony algorithm, we use the membership value to update the pheromone. Experiments show that the algorithm can not only solve the premature convergence problem caused by stagnation near the local optimal solution but also solve the continuous domain and combinatorial optimization problems and achieve better classification results. Moreover, the algorithm has a good effect on predicting classification and can provide guidance for predicting the tendency of juvenile delinquency.

Study of Parameters Configuration in Ant Colony Algorithm

2014 ◽  
Vol 678 ◽  
pp. 51-54
Author(s):  
Yan Rong Cui
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Optimal Parameter ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Parameter Combination ◽  
The Impact

It has obtained a better result to use ant colony algorithm to solve complex combinatorial optimization problems, but different value of the parameters in ant colony algorithm affects the performance of the algorithm. This paper studies the configuration of parameters in ant colony algorithm, and analyses the impact of the key parameters of the algorithm, and obtains the optimal parameter combination of using ant colony algorithm to solve TSP problems by using EIL51TSP data to simulate.

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