GLOBAL OPTIMIZATION OF HYSTERETIC DAMPERS FOR ELASTIC-PLASTIC MDOF STRUCTURES VIA HYBRID APPROACH OF REAL-CODED GENETIC ALGORITHM AND LOCAL SEARCH

An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. First, a sorting grouping selection method is given with the advantage of easy realization and not needing to calculate the fitness value. Secondly, a heuristic normal distribution crossover (HNDX) operator is proposed. It can guarantee the cross-generated offsprings to locate closer to the better one among the two parents and the crossover direction to be very close to the optimal crossover direction or to be consistent with the optimal crossover direction. In this way, HNDX can ensure that there is a great chance of generating better offsprings. Thirdly, since the GA in the existing literature has many iterations, the same individuals are likely to appear in the population, thereby making the diversity of the population worse. In IRCGA, substitution operation is added after the crossover operation so that the population does not have the same individuals, and the diversity of the population is rich, thereby helping avoid premature convergence. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, this paper proposes a combinational mutation method, which makes the mutation operation take into account both local search and global search. The computational results with nine examples show that the IRCGA has fast convergence speed. As an example application, the optimization model of the steering mechanism of vehicles is formulated and the IRCGA is used to optimize the parameters of the steering trapezoidal mechanism of three vehicle types, with better results than the other methods used.

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A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

Computational Intelligence and Neuroscience ◽

10.1155/2016/2565809 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Leilei Cao ◽

Lihong Xu ◽

Erik D. Goodman

Keyword(s):

Genetic Algorithm ◽

Global Optimization ◽

Local Search ◽

Evolutionary Algorithm ◽

Optimization Problems ◽

Its Region ◽

Bat Algorithm ◽

Greedy Strategy ◽

Search Mechanism ◽

Genotype Space

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

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A Genetic Algorithm Based Hybrid Approach for Reliability-Redundancy Optimization Problem of a Series System with Multiple-Choice

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2017.2.3-016 ◽

2017 ◽

Vol 2 (3) ◽

pp. 185-212 ◽

Cited By ~ 10

Author(s):

Asoke Kumar Bhunia ◽

Avijit Duary ◽

Laxminarayan Sahoo

Keyword(s):

Genetic Algorithm ◽

System Reliability ◽

Hybrid Algorithm ◽

Optimization Problems ◽

Hybrid Approach ◽

Multiple Choice ◽

Function Technique ◽

Series System ◽

Special Cases ◽

Real Coded Genetic Algorithm

The goal of this paper is to introduce an application of hybrid algorithm in reliability optimization problems for a series system with parallel redundancy and multiple choice constraints to maximize the system reliability subject to system budget and also to minimize the system cost subject to minimum level of system reliability. Both the problems are solved by using penalty function technique for dealing with the constraints and hybrid algorithm. In this algorithm, the well-known real coded Genetic Algorithm is combined with Self-Organizing Migrating Algorithm. As special cases, both the problems are formulated and solved considering single component without redundancy. Finally, the proposed approach is illustrated by some numerical examples and the computational results are discussed.

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Integrated Real-Coded Genetic Algorithm and Particle Swarm Optimization for Solving Constrained Global Optimization Problems

Communications in Computer and Information Science - Advances in Information Technology and Education ◽

10.1007/978-3-642-22418-8_72 ◽

2011 ◽

pp. 511-522

Author(s):

Jui-Yu Wu

Keyword(s):

Genetic Algorithm ◽

Global Optimization ◽

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Constrained Global Optimization ◽

Swarm Optimization ◽

Real Coded Genetic Algorithm

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Real-coded genetic algorithm with uniform random local search

Applied Mathematics and Computation ◽

10.1016/j.amc.2013.11.097 ◽

2014 ◽

Vol 228 ◽

pp. 589-597 ◽

Cited By ~ 4

Author(s):

B.A. Sawyerr ◽

A.O. Adewumi ◽

M.M. Ali

Keyword(s):

Genetic Algorithm ◽

Local Search ◽

Real Coded Genetic Algorithm

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Benchmarking RCGAu on the Noiseless BBOB Testbed

The Scientific World JOURNAL ◽

10.1155/2015/734957 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Babatunde A. Sawyerr ◽

Aderemi O. Adewumi ◽

M. Montaz Ali

Keyword(s):

Genetic Algorithm ◽

Local Search ◽

Search Space ◽

Test Functions ◽

Multimodal Functions ◽

Search Mechanism ◽

Random Direction ◽

Real Coded Genetic Algorithm ◽

Search Capability

RCGAu is a hybrid real-coded genetic algorithm with “uniform random direction” search mechanism. Theuniform random directionsearch mechanism enhances the local search capability of RCGA. In this paper, RCGAu was tested on the BBOB-2013 noiseless testbed using restarts till a maximum number of function evaluations (#FEs) of 105×Dare reached, whereDis the dimension of the function search space. RCGAu was able to solve several test functions in the low search dimensions of 2 and 3 to the desired accuracy of 108. Although RCGAu found it difficult in getting a solution with the desired accuracy 108for high conditioning and multimodal functions within the specified maximum #FEs, it was able to solve most of the test functions with dimensions up to 40 with lower precisions.

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An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

Computational Intelligence and Neuroscience ◽

10.1155/2019/4243853 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Jiquan Wang ◽

Mingxin Zhang ◽

Okan K. Ersoy ◽

Kexin Sun ◽

Yusheng Bi

Keyword(s):

Genetic Algorithm ◽

Local Search ◽

Normal Distribution ◽

Optimization Problems ◽

Global Search ◽

Single Mutation ◽

Beam Structure ◽

Great Chance ◽

Real Coded Genetic Algorithm ◽

Cantilevered Beam

A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of participation crossover are the same. Under these circumstances, the crossover operation does not generate new individuals, and therefore does not work. To avoid this problem, the substitution operation is added after the crossover so that there is no duplication of the same individuals in the population. This improves the computational efficiency of MOIRCGA by leading it to quickly converge to the global optimal solution. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, a Combinational Mutation method is proposed with both local search and global search. The experimental results with sixteen examples show that the multi-offspring improved real-coded genetic algorithm (MOIRCGA) has fast convergence speed. As an example, the optimization model of the cantilevered beam structure is formulated, and the proposed MOIRCGA is compared to the RCGA in optimizing the parameters of the cantilevered beam structure. The optimization results show that the function value obtained with the proposed MOIRCGA is superior to that of RCGA.

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GUIDED HYBRID GENETIC ALGORITHM FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

Radio Electronics Computer Science Control ◽

10.15588/1607-3274-2021-2-18 ◽

2021 ◽

pp. 174-188

Author(s):

S. E. Avramenko ◽

T. A. Zheldak ◽

L. S. Koriashkina

Keyword(s):

Genetic Algorithm ◽

Global Optimization ◽

Local Search ◽

Optimization Problems ◽

Hybrid Methods ◽

Hybrid Genetic Algorithm ◽

Solution Process ◽

Nonlinear Function ◽

Search Space ◽

Guided Local Search

Context. One of the leading problems in the world of artificial intelligence is the optimization of complex systems, which is often represented as a nonlinear function that needs to be minimized. Such functions can be multimodal, non-differentiable, and even set as a black box. Building effective methods for solving global optimization problems raises great interest among scientists. Objective. Development of a new hybrid genetic algorithm for solving global optimization problems, which is faster than existing analogues. Methods. One of the crucial challenges for hybrid methods in solving nonlinear global optimization problems is the rational use of local search, as its application is accompanied by quite expensive computational costs. This paper proposes a new GBOHGA hybrid genetic algorithm that reproduces guided local search and combines two successful modifications of genetic algorithms. The first one is BOHGA that establishes a qualitative balance between local and global search. The second one is HGDN that prevents reexploration of the previously explored areas of a search space. In addition, a modified bump-function and an adaptive scheme for determining one of its parameters – the radius of the “deflation” of the objective function in the vicinity of the already found local minimum – were presented to accelerate the algorithm. Results. GBOHGA performance compared to other known stochastic search heuristics on a set of 33 test functions in 5 and 25dimensional spaces. The results of computational experiments indicate the competitiveness of GBOHGA, especially in problems with multimodal functions and a large number of variables. Conclusions. The new GBOHGA hybrid algorithm, developed on the basis of the integration of guided local search ideas and BOHGA and HGDN algorithms, allows to save significant computing resources and speed up the solution process of the global optimization problem. It should be used to solve global optimization problems that arise in engineering design, solving organizational and management problems, especially when the mathematical model of the problem is complex and multidimensional.

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