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

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.

Download Full-text

An Novel Estimation of Distribution Algorithm for TSP

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.373-375.1089 ◽

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.

Download Full-text

Constrained optimization problem solving using estimation of distribution algorithms

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) ◽

10.1109/cec.2004.1330870 ◽

2004 ◽

Cited By ~ 5

Author(s):

P.A. Simionescu ◽

D.G. Beale ◽

G.V. Dozier

Keyword(s):

Problem Solving ◽

Constrained Optimization ◽

Optimization Problem ◽

Estimation Of Distribution Algorithms ◽

Constrained Optimization Problem ◽

Estimation Of Distribution ◽

Distribution Algorithms ◽

Optimization Problem Solving

Download Full-text

Space Complexity of Estimation of Distribution Algorithms

Evolutionary Computation ◽

10.1162/1063656053583423 ◽

2005 ◽

Vol 13 (1) ◽

pp. 125-143 ◽

Cited By ~ 25

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.

Download Full-text

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

Mathematical Problems in Engineering ◽

10.1155/2015/640231 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 3

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.

Download Full-text