Taboo evolutionary programming: a new method of global optimization

2006 ◽  
Vol 462 (2076) ◽  
pp. 3613-3627 ◽  
Author(s):  
Mingjun Ji ◽  
Jacek Klinowski
Keyword(s):  
Global Optimization ◽  
Global Minimum ◽  
Single Point ◽  
Evolutionary Programming ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Benchmark Problems ◽  
Single Point Mutation ◽  
Central Idea ◽  
Real World Problems

We introduce taboo evolutionary programming, a very efficient global optimization method which combines features of single-point mutation evolutionary programming (SPMEP) and taboo search. As demonstrated by solving 18 benchmark problems, the algorithm is not trapped in local minima and quickly approaches the global minimum. The results are superior to those from SPMEP, fast evolutionary programming and generalized evolutionary programming. The method is easily applicable to real-world problems, and the central idea may be introduced into other algorithms.

scholarly journals Design of dimensionally stable composites using efficient global optimization method

2016 ◽  
Vol 233 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Levent Aydin ◽  
Olgun Aydin ◽  
H Seçil Artem ◽  
Ali Mert
Keyword(s):  
Global Optimization ◽  
Communication Systems ◽  
Optimization Problems ◽  
Material Design ◽  
Optimization Method ◽  
Pattern Search ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Moisture Expansion ◽  
Design And Optimization

Dimensionally stable material design is an important issue for space structures such as space laser communication systems, telescopes, and satellites. Suitably designed composite materials for this purpose can meet the functional and structural requirements. In this paper, it is aimed to design the dimensionally stable laminated composites by using efficient global optimization method. For this purpose, the composite plate optimization problems have been solved for high stiffness and low coefficients of thermal and moisture expansion. Some of the results based on efficient global optimization solution have been verified by genetic algorithm, simulated annealing, and generalized pattern search solutions from the previous studies. The proposed optimization algorithm is also validated experimentally. After completing the design and optimization process, failure analysis of the optimized composites has been performed based on Tsai–Hill, Tsai–Wu, Hoffman, and Hashin–Rotem criteria.

scholarly journals MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Search Algorithm ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

<div> <div> <div> <p>Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods. </p> </div> </div> </div>

scholarly journals MolFinder: An Evolutionary Algorithm for the Global Optimization of Molecular Properties and the Extensive Exploration of Chemical Space Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Chemical Space ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

Abstract Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.

A New Global Optimization Method for Simultaneous Computation on Expensive Black-Box Functions

2003 ◽  
Author(s):  
Liqun Wang ◽  
Songqing Shan ◽  
G. Gary Wang
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Black Box ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Global Optimization Methods

The presence of black-box functions in engineering design, which are usually computation-intensive, demands efficient global optimization methods. This work proposes a new global optimization method for black-box functions. The global optimization method is based on a novel mode-pursuing sampling (MPS) method which systematically generates more sample points in the neighborhood of the function mode while statistically covers the entire search space. Quadratic regression is performed to detect the region containing the global optimum. The sampling and detection process iterates until the global optimum is obtained. Through intensive testing, this method is found to be effective, efficient, robust, and applicable to both continuous and discontinuous functions. It supports simultaneous computation and applies to both unconstrained and constrained optimization problems. Because it does not call any existing global optimization tool, it can be used as a standalone global optimization method for inexpensive problems as well. Limitation of the method is also identified and discussed.

Multi Agent Normal Sampling Technique (MANST) for Global Optimization

2007 ◽  
Author(s):  
Alireza Saremi ◽  
Nasr Al-Hinai ◽  
G. Gary Wang ◽  
Tarek ElMekkawy
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Sampling Technique ◽  
Optimization Method ◽  
Global Optimum ◽  
Systematic Sampling ◽  
Benchmark Problems ◽  
Candidate Solution ◽  
Multi Agent ◽  
Specific Number

The current work discusses a novel global optimization method called the Multi-Agent Normal Sampling Technique (MANST). MANST is based on systematic sampling of points around agents; each agent in MANST represents a candidate solution of the problem. All agents compete with each other for a larger share of available resources. The performance of all agents is periodically evaluated and a specific number of agents who show no promising achievements are deleted; new agents are generated in the proximity of those promising agents. This process continues until the agents converge to the global optimum. MANST is a standalone global optimization technique. It is benchmarked with six well-known test cases and the results are then compared with those obtained from Matlab™ 7.1 GA Toolbox. The test results showed that MANST outperformed Matlab™ 7.1 GA Toolbox for the benchmark problems in terms of accuracy, number of function evaluations, and CPU time.

scholarly journals A multiple-data-based efficient global optimization algorithm and its parallel implementation for automotive body design

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879434 ◽  
Author(s):  
Bing Xu ◽  
Yong Cai
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Graphics Processing Unit ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Processing Unit ◽  
Global Optimization Algorithm ◽  
Multiple Data ◽  
Practical Engineering ◽  
Graphics Processing

The purpose of this article is to improve the convergence efficiency of the traditional efficient global optimization method. Furthermore, we try a graphics processing unit–based parallel computing method to improve the computing efficiency of the efficient global optimization method for both mathematical and practical engineering problems. First, we propose a multiple-data-based efficient global optimization algorithm instead of the multiple-surrogates-based efficient global optimization algorithm. Second, a novel graphics processing unit–based general-purpose computing technology is adopted to accelerate the solution efficiency of our multiple-data-based efficient global optimization algorithm. Third, a hybrid parallel computing approach using the OpenMP and compute unified device architecture is adopted to further improve the solution efficiency of forward problems in practical application. This is accomplished by integrating the graphics processing unit–based finite element method numerical analysis system into the optimization software. The numerical results show that for the same problem, the optimal result of the multiple-data-based efficient global optimization algorithm is consistently better than the multiple-surrogates-based efficient global optimization algorithm with the same optimization iterations. In addition, the graphics processing unit–based parallel simulation system helps in the reduction of the calculation time for practical engineering problems. The multiple-data-based efficient global optimization method performs stably in both high-order mathematical functions and large-scale nonlinear practical engineering optimization problems. An added benefit is that the computational time and accuracy are no longer obstacles.

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