A Hybrid Genetic Algorithm for Mixed-Discrete Design Optimization

2004 ◽  
Vol 127 (6) ◽  
pp. 1100-1112 ◽  
Author(s):  
Singiresu S. Rao ◽  
Ying Xiong
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Mechanical Design ◽  
Random Search ◽  
Hybrid Genetic Algorithm ◽  
Global Optimum ◽  
Feasible Region ◽  
Search Methods ◽  
Nonlinear Design ◽  
Optimum Solution

A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.

scholarly journals Optimal source localization problem based on TOA measurements

2017 ◽  
Vol 14 (1) ◽  
pp. 161-176
Author(s):  
Maja Rosic ◽  
Mirjana Simic ◽  
Predrag Pejovic ◽  
Milan Bjelica
Keyword(s):  
Genetic Algorithm ◽  
Lower Bound ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Nonlinear Least Squares ◽  
Global Optimum ◽  
Time Of Arrival ◽  
Newton Raphson ◽  
Optimum Solution

Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.

Development of Multi-Stage and Hybrid Real-Coded Genetic Algorithms With Active Constraints

2002 ◽  
Author(s):  
Yasunari Mimura ◽  
Shinobu Yoshimura ◽  
Tomoyuki Hiroyasu ◽  
Mitsunori Miki
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Initial Point ◽  
Global Optimum ◽  
Feasible Region ◽  
Active Constraints ◽  
Multi Stage ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Optimum Solution

In this study, we propose multi-stage and hybrid real-coded genetic algorithm. In the proposed algorithm, there are two stages. In the first stage, Real-coded Genetic Algorithm with Active Constraints (RGAAC) is applied to find a solution that is close to the global optimum. In RGAAC, indviduals who are out of the feasible region are pulled back into feasible region. Therefore, the effective search can be carried out even in the constraints problems. In the second stage, Feasible Region Limiting Method (FRLM) is applied to obtain an optimum solution. FRLM uses the solution that is derived in the first stager as an initial point. In this study, RGAAC is applied to solve the truss structure problems. Through these problems, the effectiveness and the searching mechanism of RGAAC is discussed. The, the proposed algorithm is also applied to 2D problem. In this problem, there are about 1000 design variables. The proposed method can derive the reasonable solution. From these results, it is concluded that the proposed method is effective to solve optimzation problems of large scale structures.

Artificial Life Optimization Algorithm and Applications

2011 ◽  
pp. 423-449
Author(s):  
Bo-Suk Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Artificial Life ◽  
Optimization Problem ◽  
Global Optimum ◽  
Function Optimization ◽  
Global Function ◽  
Test Function ◽  
Optimum Solution ◽  
Artificial World

This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global function optimization problem. In the ALRT, the emergent colony is a fundamental mechanism to search the optimum solution and can be accomplished through the metabolism, movement and reproduction among artificial organisms which appear at the optimum locations in the artificial world. In this case, the optimum locations mean the optimum solutions in the optimization problem. Hence, the ALRT focuses on the searching for the optimum solution in the location of emergent colonies and can achieve more accurate global optimum. The optimization results using different types of test functions are presented to demonstrate the described approach successfully achieves optimum performance. The algorithm is also applied to the test function optimization and optimum design of short journal bearing as a practical application. The optimized results are compared with those of genetic algorithm and successive quadratic programming to identify the optimizing ability.

MODELLING THE DYNAMICS OF AN ADJUSTMENT OF A SEARCH SPACE SIZE IN A GENETIC ALGORITHM

2008 ◽  
Vol 19 (07) ◽  
pp. 1047-1062 ◽  
Author(s):  
ADIL AMIRJANOV
Keyword(s):  
Genetic Algorithm ◽  
Search Strategy ◽  
Equations Of Motion ◽  
Search Space ◽  
Global Optimum ◽  
Statistical Properties ◽  
Feasible Region ◽  
Space Size ◽  
The One ◽  
Average Fitness

One way to improve a search strategy in a Genetic Algorithm (GA) is to reduce the search space towards the feasible region where the global optimum is located. The paper describes the effect of an adjustment of a search space size of GA on the macroscopic statistical properties of population such as the average fitness and the variance fitness of population. The set of equations of motion was derived for the one-max problem that expressed the macroscopic statistical properties of population after an adjustment of a search space size in terms of those prior to the operation.

Finding Diversity in Mechanisms Using a Hybrid Genetic Algorithm

2000 ◽  
Author(s):  
Robert A. O’Neil ◽  
Louis J. Everett
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Global Optimum ◽  
Small Error ◽  
Test Function ◽  
Modified Genetic Algorithm ◽  
Path Synthesis ◽  
Alternative Solutions ◽  
Better Than

Abstract The path synthesis problem for mechanical linkages still presents problems for engineers, although it has been examined for more than two centuries. This research approached the design problem as one of creating a characteristic test function to compare a synthesized output path with a desired output path, and finding a set of linkages that reduce the corresponding error. Since the solution space of this approach is very large with typically a generous number of local minima, it may be possible to find several linkages that each produce a small error. This research investigated the ability to use a modified genetic algorithm to search for a global minima and simultaneously identify several linkage designs that are “almost” as good as the global optimum. Having alternative solutions will allow designers to choose a mechanism that best fits criteria other than path error. The results from using the method on a subclass of linkage problems demonstrate that solutions can be found that “fit” better than those found in the literature. The results also show that a diverse family of acceptable designs can be obtained and that this family includes both “well known” designs and heretofore unknown solutions.

scholarly journals Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Jinn-Tsong Tsai ◽  
Jyh-Horng Chou ◽  
Wen-Hsien Ho
Keyword(s):  
Evolutionary Algorithm ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Latin Square ◽  
Latin Squares ◽  
Feasible Region ◽  
Continuous Variables ◽  
Nonlinear Design ◽  
Discrete Values

An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA) combines Latin squares and quantum-inspired genetic algorithm (QGA). The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature.

Genetic Algorithms as Global Random Search Methods: An Alternative Perspective

1995 ◽  
Vol 3 (1) ◽  
pp. 39-80 ◽  
Author(s):  
Charles C. Peck ◽  
Atam P. Dhawan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Random Search ◽  
Schema Theory ◽  
Global Optimum ◽  
Global Search ◽  
Search Process ◽  
Sampling Distributions ◽  
Alternative Perspective ◽  
Global Random Search

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

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