scholarly journals A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
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.

Memetic Algorithm with Constrained Local Search for Large-Scale Global Optimization

2017 ◽  
Vol 26 (2) ◽  
pp. 287-300
Author(s):  
Shikha Mehta
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Search Mechanism ◽  
Global Optima ◽  
Nature Inspired Algorithms ◽  
Single Objective

AbstractNature-inspired algorithms are seen as potential tools to solve large-scale global optimization problems. Memetic algorithms (MAs) are nature-inspired techniques based on evolutionary computation. MAs are considered as modified genetic algorithms integrated with a local search mechanism. Conventional MAs perform well for small dimensions; however, their performance starts declining with the increase in dimensions. It is popularly known as the “curse of dimensionality” problem. In order to solve this problem, MA with constrained local search (MACLS) is proposed for single-objective optimization problems. MACLS restricts the local search to be performed after every generation. Controlled local search enhances the optimization capability of the MA. MACLS has been evaluated with respect to GS-MPSO (the latest modification of MA) and MLCC, EPUS-PSO, JDEdynNP-F, MTS, DewSAcc, DMS-PSO, LSEDA-gl, UEP, ALPSEA, classical DE (differential evolution), and real-coded CHC algorithms that participated in the Congress on Evolutionary Computation 2008 competition. The results establish that MACLS significantly outperforms these algorithms in attaining global optima for unimodal and multimodal single-objective optimization problems for small as well as large dimensions.

scholarly journals GUIDED HYBRID GENETIC ALGORITHM FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

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.

A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

2018 ◽  
Vol 51 (2) ◽  
pp. 265-285 ◽  
Author(s):  
Abdulbaset Saad ◽  
Zuomin Dong ◽  
Brad Buckham ◽  
Curran Crawford ◽  
Adel Younis ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Black Box ◽  
Computationally Expensive

scholarly journals Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gai-Ge Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hong Duan
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Greedy Strategy ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Low Probability ◽  
Elitism Scheme ◽  
Reliability And Robustness

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

scholarly journals Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiquan Wang ◽  
Zhiwen Cheng ◽  
Okan K. Ersoy ◽  
Panli Zhang ◽  
Weiting Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Global Search ◽  
Single Mutation ◽  
Constrained Optimization Problems ◽  
Fitness Value ◽  
Steering Mechanism ◽  
Real Coded Genetic Algorithm

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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