A Hybrid Fuzzy Simplex Genetic Algorithm

2004 ◽  
Vol 126 (6) ◽  
pp. 969-974 ◽  
Author(s):  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Local Minimum ◽  
Hybrid Genetic Algorithm ◽  
Simplex Algorithm ◽  
Standard Test ◽  
Test Problems ◽  
Design Problems ◽  
Engineering Design Problems

This paper presents a novel hybrid genetic algorithm that has the ability of the genetic algorithms to avoid being trapped at local minimum while accelerating the speed of local search by using the fuzzy simplex algorithm. The new algorithm is labeled the hybrid fuzzy simplex genetic algorithm (HFSGA). Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The HFSGA generally results in a faster convergence toward extremum.

A Hybrid Fuzzy Simplex Genetic Algorithm

2000 ◽  
Author(s):  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Simplex Algorithm ◽  
Standard Test ◽  
Initial Guess ◽  
Test Problems ◽  
Programming Technique ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Novel Algorithm

Abstract Nelder and Mead Simplex (NMS) algorithm is an effective nonlinear programming technique. Trabia and Lu (1999) recently presented a novel algorithm, Fuzzy Simplex (FS), which improved the efficiency of Nelder and Mead Simplex by using fuzzy logic to determine the orientation and size of the simplex. While Fuzzy Simplex algorithm can be successfully used to search a wide variety of functions, it suffers, as other simplex algorithms, from its dependence on the initial guess and the original simplex size. This paper addresses this problem by combining the Fuzzy Simplex with Genetic Algorithm (GA) in a hybrid algorithm. Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The Hybrid GA Fuzzy Simplex algorithm generally results in a faster convergence.

scholarly journals Construction Cost Estimation Using a Case-Based Reasoning Hybrid Genetic Algorithm Based on Local Search Method

2020 ◽  
Vol 12 (19) ◽  
pp. 7920
Author(s):  
Sangsun Jung ◽  
Jae-Ho Pyeon ◽  
Hyun-Soo Lee ◽  
Moonseo Park ◽  
Inseok Yoon ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Cost Estimation ◽  
Hybrid Genetic Algorithm ◽  
Construction Cost ◽  
Case Based Reasoning ◽  
Construction Costs ◽  
Initial Stage ◽  
Case Based

Estimates of project costs in the early stages of a construction project have a significant impact on the operator’s decision-making in essential matters, such as the site’s decision or the construction period. However, it is not easy to carry out the initial stage with confidence, because information such as design books and specifications is not available. In previous studies, case-based reasoning (CBR) is used to estimate initial construction costs, and genetic algorithms are used to calculate the weight of the retrieve phase in CBR’s process. However, it is difficult to draw a better solution than the current one, because existing genetic algorithms use random numbers. To overcome these limitations, we reflect correlation numbers in the genetic algorithms by using the method of local search. Then, we determine the weights using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using a hybrid genetic algorithm. Then, the model was verified with construction cost data that were not used for the development of the model. As a result, it was found that the hybrid genetic algorithm and case-based reasoning applied with the local search performed better than the existing solution. The detail mean error value was found to be 3.52%, 6.15%, and 0.33% higher for each case than the previous one.

Hybrid genetic algorithm for engineering design problems

2016 ◽  
Vol 20 (1) ◽  
pp. 263-275 ◽  
Author(s):  
Xuesong Yan ◽  
Hanmin Liu ◽  
Zhixin Zhu ◽  
Qinghua Wu
Keyword(s):  
Genetic Algorithm ◽  
Engineering Design ◽  
Hybrid Genetic Algorithm ◽  
Design Problems ◽  
Engineering Design Problems

A Fuzzy Adaptive Simplex Search Optimization Algorithm

1999 ◽  
Vol 123 (2) ◽  
pp. 216-225 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Xiao Bin Lu
Keyword(s):  
Fuzzy Logic ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Simplex Algorithm ◽  
Test Problems ◽  
Design Problems ◽  
Simplex Search ◽  
Search Optimization ◽  
Engineering Design Problems ◽  
Minimization Algorithms

Most optimization algorithms use empirically-chosen fixed parameters as a part of their search strategy. This paper proposes to replace these fixed parameters by adaptive ones to make the search more responsive to changes in the problem by incorporating fuzzy logic in optimization algorithms. The proposed ideas are used to develop a new adaptive form of the simplex search algorithm whose objective is to minimize a function of n variables. The new algorithm is labeled Fuzzy Simplex. The search starts by generating a simplex with n+1 vertices. The algorithm then repeatedly replaces the point with the highest function value by a new point. This process has three components: reflecting the point with the highest function value, expanding, and contracting the simplex. These operations use fuzzy logic controllers whose inputs incorporate the relative weights of the function values at the simplex points. Standard minimization test problems are used to evaluate the efficiency of the algorithm. The Fuzzy Simplex algorithm generally results in a faster convergence. Robustness and sensitivity of the algorithm are also considered. The Fuzzy Simplex algorithm is also applied successfully to several engineering design problems. The results of the Fuzzy Simplex algorithm compare favorably with other available minimization algorithms.

A hybrid genetic algorithm with local search

2001 ◽  
Vol 59 (1-2) ◽  
pp. 107-120 ◽  
Author(s):  
G Vivó-Truyols ◽  
J.R Torres-Lapasió ◽  
A Garrido-Frenich ◽  
M.C Garcı́a-Alvarez-Coque
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm

A hybrid genetic algorithm for multiobjective problems with activity analysis-based local search

2009 ◽  
Vol 193 (1) ◽  
pp. 195-203 ◽  
Author(s):  
Gerald Whittaker ◽  
Remegio Confesor ◽  
Stephen M. Griffith ◽  
Rolf Färe ◽  
Shawna Grosskopf ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Activity Analysis ◽  
Multiobjective Problems

A Fuzzy Adaptive Simplex Search Optimization Algorithm

1999 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Xiao Bin Lu
Keyword(s):  
Fuzzy Logic ◽  
Optimization Algorithms ◽  
Test Problems ◽  
Objective Functions ◽  
Fuzzy Logic Controllers ◽  
Design Problems ◽  
Simplex Search ◽  
Search Optimization ◽  
Engineering Design Problems ◽  
Fuzzy Adaptive

Abstract Optimization algorithms usually use fixed parameters that are empirically chosen to reach the minimum for various objective functions. This paper shows how to incorporate fuzzy logic in optimization algorithms to make the search adaptive to various objective functions. This idea is applied to produce a new algorithm for minimization of a function of n variables using an adaptive form of the simplex method. The search starts by generating a simplex with n+1 vertices. The algorithm replaces the point with the highest function value by a new point. This process comprises reflecting the point with the highest function value in addition to expanding or contracting the simplex using fuzzy logic controllers whose inputs incorporate the relative weights of the function values at the simplex points. The efficiency of the algorithm is studied using a set of standard minimization test problems. This algorithm generally results in a faster convergence toward the minimum. The algorithm is also applied successfully to two engineering design problems.

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