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.

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.

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 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 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.

scholarly journals Ising Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann Machine

2020 ◽  
Author(s):  
Saavan Patel ◽  
Lili Chen ◽  
Philip Canoza ◽  
Sayeef Salahuddin
Keyword(s):  
Optimization Problems ◽  
Restricted Boltzmann Machine ◽  
Model Optimization ◽  
Boltzmann Machine ◽  
Stochastic Sampling ◽  
Combinatorial Optimization Problems ◽  
Field Programmable ◽  
Max Cut Problem ◽  
Asymptotic Scaling ◽  
Better Than

Abstract In this work we demonstrate usage of the Restricted Boltzmann Machine (RBM) as a stochastic neural network capable of solving NP-Hard Combinatorial Optimization problems efficiently. By mapping the RBM onto a reconfigurable Field Programmable Gate Array (FPGA), we can effectively hardware accelerate the RBM's stochastic sampling algorithm. We benchmark the RBM against the DWave 2000Q Quantum Adiabatic Computer and the Optical Coherent Ising Machine on two such optimization problems: the MAX-CUT problem and the Sherrington-Kirkpatrick (SK) spin glass. The hardware accelerated RBM shows asymptotic scaling either similar or better than these other accelerators. This leads to 107x and 105x time to solution improvement compared to the DWave 2000Q on the MAX-CUT and SK problems respectively, along with a 150x and 1000x improvement compared to the Coherent Ising Machine annealer on those problems. By utilizing commodity hardware running at room temperature, the RBM shows potential for immediate and scalable use.

An Improved Method of Artificial Bee Colony Algorithm

2011 ◽  
Vol 101-102 ◽  
pp. 315-319 ◽  
Author(s):  
Xin Jie Wu ◽  
Duo Hao ◽  
Chao Xu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Simulation Experiment ◽  
Optimization Problems ◽  
Local Extremum ◽  
Global Search ◽  
Improved Method ◽  
Object Function ◽  
Bee Colony ◽  
Better Than

The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.

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