Fuzzy Simulated Evolution Algorithm

2015 ◽  
pp. 27-38
Keyword(s):  
Simulated Evolution ◽  
Evolution Algorithm

A Fuzzy Simulated Evolution Algorithm for Hard Problems

2014 ◽  
pp. 892-912
Author(s):  
Michael Mutingi
Keyword(s):  
Set Theory ◽  
Large Scale ◽  
Cell Formation ◽  
Computational Experiments ◽  
Benchmark Problems ◽  
Problem Complexity ◽  
Simulated Evolution ◽  
Solution Methods ◽  
Hard Problems ◽  
Evolution Algorithm

As problem complexity continues to increase in industry, developing efficient solution methods for solving hard problems, such as heterogeneous vehicle routing and integrated cell formation problems, is imperative. The focus of this chapter is to develop from the classical simulated evolution algorithm, a Fuzzy Simulated Evolution Algorithm (FSEA) that incorporates the concepts of fuzzy set theory, evolution, and constructive perturbation. The aim is to improve the search efficiency of the algorithm by enhancing the major phases of the algorithm through initialization, evaluation, selection, and reconstruction. Illustrative examples are provided to demonstrate the candidate application areas and to show the strength of the algorithm. Computational experiments are conducted based on benchmark problems in the literature. Results from the computational experiments demonstrate the strength of the algorithm. It is anticipated that the application of the FSEA metaheuristic can be extended to other hard large scale problems.

Fuzzy simulated evolution algorithm for VLSI cell placement

2003 ◽  
Vol 44 (2) ◽  
pp. 227-247 ◽  
Author(s):  
Habib Youssef ◽  
Sadiq M Sait ◽  
Hussain Ali
Keyword(s):  
Simulated Evolution ◽  
Evolution Algorithm ◽  
Cell Placement

Method of Assembly Sequence Planning Based on Simulated Evolution Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 1171-1175
Author(s):  
Jing Zhang ◽  
Yun Sheng Yang ◽  
Shao Wei Feng
Keyword(s):  
Energy Function ◽  
Graph Model ◽  
Typical Case ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Simulated Evolution ◽  
Sequence Planning ◽  
Assembly Cost ◽  
Assembly Sequences ◽  
Evolution Algorithm

It is important for assemble industry to generate an optimized assembly sequences. In order to solve this problem, firstly an oriented mating graph model is proposed with the related mating matrix. Therefore an improved simulated evolution algorithm is proposed, which can compute an energy function of the assembly cost associated with the assembly sequence deduced by the graph model. The energy function can be optimized iteratively and perturbed occasionally by the simulated evolution until no further change in the energy occurs. Finally, the validity of the method is proved by a typical case.

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