An Novel Estimation of Distribution Algorithm for TSP

2013 ◽  
Vol 373-375 ◽  
pp. 1089-1092
Author(s):  
Fa Hong Yu ◽  
Wei Zhi Liao ◽  
Mei Jia Chen
Keyword(s):  
Optimization Problem ◽  
Population Diversity ◽  
Combinatorial Optimization Problem ◽  
Estimation Of Distribution Algorithm ◽  
Traveling Salesman ◽  
Estimation Of Distribution Algorithms ◽  
Np Hard ◽  
Estimation Of Distribution ◽  
Np Hard Problem ◽  
Distribution Algorithms

Estimation of distribution algorithms (EDAs) is a method for solving NP-hard problem. But it is hard to find global optimization quickly for some problems, especially for traveling salesman problem (TSP) that is a classical NP-hard combinatorial optimization problem. To solve TSP effectively, a novel estimation of distribution algorithm (NEDA ) is provided, which can solve the conflict between population diversity and algorithm convergence. The experimental results show that the performance of NEDA is effective.

An Improved Estimation of Distribution Algorithm Based on the Entropy Increment Theorem

2013 ◽  
Vol 385-386 ◽  
pp. 1675-1678 ◽  
Author(s):  
Yang Yi ◽  
Mei Jia Chen ◽  
Fa Hong Yu
Keyword(s):  
High Dimension ◽  
Selective Pressure ◽  
Annealing Process ◽  
Population Diversity ◽  
Premature Convergence ◽  
Estimation Of Distribution Algorithms ◽  
Competitive Mechanism ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
The Cost

To systematically harmonize the conflict between selective pressure and population diversity in estimation of distribution algorithms, an improved estimation of distribution algorithms based on the entropy increment theorem (IEDAEI) is proposed in this paper. IEDAEI conforms to the entropy increment theorem in simulating the competitive mechanism between energy and entropy in annealing process, in which population diversity is measured by the entropy increment theorem. By solving some typical high-dimension problems with multiple local optimizations, satisfactory results are achieved. The results show that this algorithm has preferable capability to avoid the premature convergence effectively and reduce the cost in search to some extent.

Space Complexity of Estimation of Distribution Algorithms

2005 ◽  
Vol 13 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Yong Gao ◽  
Joseph Culberson
Keyword(s):  
Genetic Algorithm ◽  
Bayesian Network ◽  
Optimization Problem ◽  
Space Complexity ◽  
Estimation Of Distribution Algorithms ◽  
Additive Functions ◽  
Problem Size ◽  
Interaction Structure ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

2005 ◽  
Vol 13 (1) ◽  
pp. 43-66 ◽  
Author(s):  
J. M. Peña ◽  
J. A. Lozano ◽  
P. Larrañaga
Keyword(s):  
Bayesian Networks ◽  
Unsupervised Learning ◽  
Genetic Drift ◽  
Optimization Problems ◽  
Estimation Of Distribution Algorithm ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Global Optima ◽  
Effectiveness And Efficiency ◽  
Distribution Algorithms

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

scholarly journals Minimization of the Total Traveling Distance and Maximum Distance by Using a Transformed-Based Encoding EDA to Solve the Multiple Traveling Salesmen Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
S. H. Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Maximum Distance ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Hard Problems ◽  
Distribution Algorithms

Estimation of distribution algorithms (EDAs) have been used to solve numerous hard problems. However, their use with in-group optimization problems has not been discussed extensively in the literature. A well-known in-group optimization problem is the multiple traveling salesmen problem (mTSP), which involves simultaneous assignment and sequencing procedures and are shown in different forms. This paper presents a new algorithm, namedEDAMLA, which is based on self-guided genetic algorithm with a minimum loading assignment (MLA) rule. This strategy uses the transformed-based encoding approach instead of direct encoding. The solution space of the proposed method is onlyn!. We compare the proposed algorithm against the optimal direct encoding technique, the two-part encoding genetic algorithm, and, in experiments on 34 TSP instances drawn from the TSPLIB, find that its solution space isn!n-1m-1. The scale of the experiments exceeded that presented in prior studies. The results show that the proposed algorithm was superior to the two-part encoding genetic algorithm in terms of minimizing the total traveling distance. Notably, the proposed algorithm did not cause a longer traveling distance when the number of salesmen was increased from 3 to 10. The results suggest that EDA researchers should employ the MLA rule instead of direct encoding in their proposed algorithms.

An Improved Estimation of Distribution Algorithms Based on the Minimal Free Energy

2013 ◽  
Vol 373-375 ◽  
pp. 1093-1097
Author(s):  
Fa Hong Yu ◽  
Mei Jia Chen ◽  
Wei Zhi Liao
Keyword(s):  
Free Energy ◽  
Optimization Problems ◽  
Minimum Free Energy ◽  
Population Diversity ◽  
Estimation Of Distribution Algorithms ◽  
Minimal Free Energy ◽  
Competitive Mechanism ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
The Cost

To systematically harmonize the conflict between selective pressure and population diversity in estimation of distribution algorithms, an improved estimation of distribution algorithms based on the minimal free energy (IEDA) is proposed in this paper. IEDA conforms to the principle of minimal free energy in simulating the competitive mechanism between energy and entropy in annealing process, in which population diversity is measured by similarity entropy and the minimum free energy is simulated with an efficient and effective competition by free energy component. Through solving some typical numerical optimization problems, satisfactory results were achieved, which showed that IEDA was a preferable algorithm to avoid the premature convergence effectively and reduce the cost in search to some extent.

scholarly journals Collaborative Learning of Estimation of Distribution Algorithm for RNA secondary structure prediction

2020 ◽  
Vol 14 (1) ◽  
pp. 92-102
Author(s):  
Supawadee Srikamdee ◽  
Prabhas Chongstitvatana
Keyword(s):  
Secondary Structure ◽  
Collaborative Learning ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Estimation Of Distribution Algorithm ◽  
Estimation Of Distribution Algorithms ◽  
Rna Secondary Structure Prediction ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

Estimation of distribution algorithms (EDAs) are successfully applied in the fields of bioinformatics for tasks such as gene structure analysis, protein structure prediction, and RNA secondary structure prediction. This paper proposes a new method, namely collaborative learning of estimation of distribution algorithms, or Co-EDAs, based on an estimation of distribution algorithm for RNA secondary structure prediction using a single RNA sequence as input. The proposed method consists of two EDAs with minimum free energy objective. The Co-EDAs use both good and poor solutions to improve the algorithm’s to search throughout the search space. Using information from poor solutions can indicate which area is unappealing to explore when searching with high-dimensional data. The Co-EDAs method was tested with 750 known RNA structures from RNA STRAND v2.0. That database includes data with more than 14 RNA types. The proposed method was compared to three prediction programs that are based on dynamic programming algorithms called Mfold, RNAfold, and RNAstructure. These programs are available as services on web servers. The results on average show that the Co-EDAs yields approximately 6% better accuracy than those competitors in all metrics.

Optimization of Machining Parameters for Parallel Turnings Using Estimation of Distribution Algorithms

2013 ◽  
Vol 753-755 ◽  
pp. 1192-1195 ◽  
Author(s):  
Shu Tong Xie ◽  
Li Fang Pan
Keyword(s):  
Production Cost ◽  
Optimization Problem ◽  
Optimization Approach ◽  
Estimation Of Distribution Algorithms ◽  
Machining Parameters ◽  
Optimal Solutions ◽  
Unit Production Cost ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Manufacturing Problems

Optimal machining parameters can lead to considerable savings in manufacturing problems. In this paper, to deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in parallel turnings, we propose a novel optimization approach which divides this complicated problem into several sub-problems. Then an estimation of distribution algorithm (EDA) is developed to search the optimal results for each sub-problem. Computer simulations show that the proposed approach is efficient in searching the optimal solutions to reduce significantly the unit production cost.

LZW Chromosome Encoding in Estimation of Distribution Algorithms

2013 ◽  
Vol 4 (4) ◽  
pp. 41-61
Author(s):  
Orawan Watchanupaporn ◽  
Worasait Suwannik
Keyword(s):  
Genetic Algorithm ◽  
Model Building ◽  
Fitness Landscape ◽  
Estimation Of Distribution Algorithm ◽  
Experimental Results ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Lzw Algorithm

Estimation of distribution algorithm (EDA) can solve more complicated problems than its predecessor (Genetic Algorithm). EDA uses various methods to probabilistically model a group of highly fit individuals. Calculating the model in sophisticated EDA is very time consuming. To reduce the model building time, the authors propose compressed chromosome encoding. A chromosome is encoded using a format that can be decompressed by the Lempel-Ziv-Welch (LZW) algorithm. The authors combined LZW encoding with various EDAs and termed the class of algorithms Lempel-Ziv-Welch Estimation of Distribution Algorithms (LZWEDA). Experimental results show that LZWEDA significantly outperforms the original EDA. Finally, the authors analyze how LZW encoding transforms a fitness landscape.

Research on Traveling Salesman Problem Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2901-2904 ◽  
Author(s):  
Xiao Yan Yun
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Problem ◽  
Research Direction ◽  
Traveling Salesman ◽  
Future Research ◽  
Efficiency Enhancement ◽  
Different Types ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) has been an important problem in the field of distribution and logistics and it is clearlyNP-hard combinatorial optimization problem and difficult to solve. This paper gives a review of achievements of different types of Algorithms for the traveling sales man problem and outlines these advantages and limitation for these algorithms, including dynamic program, brand and bound, genetic algorithm and estimation of distribution algorithms. In addition, some of the most powerful efficiency enhancement techniques applied to TSP is discussed and quite a few common conditions of different methods for TSP are summarized. Finally, some future research direction and content are proposed.

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