Enhanced Multi-Agent Normal Sampling Technique for Global Optimization

2008 ◽  
Author(s):  
Alireza Saremi ◽  
Amir H. Birjandi ◽  
G. Gary Wang ◽  
Tarek ElMekkawy ◽  
Eric Bibeau
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Sampling Technique ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Multi Agent ◽  
Mean Function ◽  
Specific Number

This paper describes an enhanced version of a new global optimization method, Multi-Agent Normal Sampling Technique (MANST) described in reference [1]. Each agent in MANST includes a number of points that sample around the mean point with a certain standard deviation. In each step the point with the minimum value in the agent is chosen as the center point for the next step normal sampling. Then the chosen points of all agents are compared to each other and agents receive a certain share of the resources for the next step according to their lowest mean function value at the current step. 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 and does not require equations or knowledge about the objective function. The unique feature of this method in comparison with other global optimization methods is its dynamic normal distribution search. This work presents our recent research in enhancing MANST to handle variable boundaries and constraints. Moreover, a lean group sampling approach is implemented to prevent sampling in the same region for different agents. The overall capability and efficiency of the MANST has been improved as a result in the newer version. The enhanced MANST is highly competitive with other stochastic methods such as Genetic Algorithm (GA). In most of the test cases, the performance of the MANST is significantly higher than the Matlab™ GA Toolbox.

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.

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.

CAN GLOBAL OPTIMIZATION TECHNIQUE COMPENSATE FOR MARKER SKIN MOVEMENT IN RAT KINEMATICS?

2014 ◽  
Vol 14 (05) ◽  
pp. 1450065 ◽  
Author(s):  
FILIPA JOÃO ◽  
ANTÓNIO VELOSO ◽  
SANDRA AMADO ◽  
PAULO ARMADA-DA-SILVA ◽  
ANA C. MAURÍCIO
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Optimization Method ◽  
Anatomical Landmarks ◽  
Global Optimization Method ◽  
Skin Deformation ◽  
Angular Displacements ◽  
The Right

The motion of the skeletal estimated from skin attached marker-based motion capture(MOCAP) systems is known to be affected by significant bias caused by anatomical landmarks mislocation but especially by soft tissue artifacts (such as skin deformation and sliding, inertial effects and muscle contraction). As a consequence, the error associated with this bias can propagate to joint kinematics and kinetics data, particularly in small rodents. The purpose of this study was to perform a segmental kinematic analysis of the rat hindlimb during locomotion, using both global optimization as well as segmental optimization methods. Eight rats were evaluated for natural overground walking and motion of the right hindlimb was captured with an optoeletronic system while the animals walked in the track. Three-dimensional (3D) biomechanical analyses were carried out and hip, knee and ankle joint angular displacements and velocities were calculated. Comparison between both methods demonstrated that the magnitude of the kinematic error due to skin movement increases in the segmental optimization when compared with the global optimization method. The kinematic results assessed with the global optimization method matches more closely to the joint angles and ranges of motion calculated from bone-derived kinematics, being the knee and hip joints with more significant differences.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

Study on Optimal Design of a Folding-Type Hatch Cover Considering Material Selection

2021 ◽  
pp. 1-11
Author(s):  
Gerry Liston Putra ◽  
Mitsuru Kitamura ◽  
Akihiro Takezawa
Keyword(s):  
Production Systems ◽  
Material Selection ◽  
Optimization Technique ◽  
Plate Thickness ◽  
Optimization Methods ◽  
Optimization Method ◽  
Production Costs ◽  
Plate Material ◽  
Material Type ◽  
Material Cost

Abstract Most shipyard companies maintain efficiency in all aspects of their business to survive. One of these aspects is ship production costs and their reduction. This study proposes a solution to this problem using an optimization method. A hatch cover composed of plates and stiffeners was selected as a case study. In this study, the mass and material cost of the hatch cover was optimized as an objective function using the Pareto approach with developed optimization methods. Plate thickness t, stiffener shape s, and plate material type m were selected as the design variables in this study along with some constraints. To estimate the optimal plate thickness, an expression of stress equations was Developed using an optimization technique. Furthermore, stiffener shape and plate material type selection were optimized using a genetic algorithm (GA). The results show that the optimization method is effective to decrease the mass and material cost of a hatch cover. Introduction The demand for new shipbuilding has decreased because of the effect of the economic crisis that hit almost every country in the world. Shipyard companies must think innovatively and creatively to survive under the pressure of this crisis by evaluating various studies and improvising new methods to achieve efficiency. One of the studies that has been performed examines the methods to reduce the fabrication cost of ship structures to stay profitable through the optimization of work hours, workflow production systems, and structural design.

scholarly journals Damage Identification in Bars with a Wave Propagation Approach and a Hybrid Optimization Method

2012 ◽  
Vol 19 (3) ◽  
pp. 301-321 ◽  
Author(s):  
R.A. Tenenbaum ◽  
K.M. Fernandes ◽  
L.T. Stutz ◽  
A.J. Silva Neto
Keyword(s):  
Wave Propagation ◽  
Damage Identification ◽  
Signal To Noise Ratio ◽  
Time History ◽  
Optimization Methods ◽  
Optimization Method ◽  
Stochastic Methods ◽  
Test Case ◽  
Hybrid Technique ◽  
Damage Scenarios

The formulation and solution of the inverse problem of damage identification based on wave propagation approach are presented. Different damage scenarios for a bar are considered. Time history responses, obtained from pulse-echo synthetic experiments, are used to identify damage position, severity and shape. In order to account for noise corrupted data, different levels of signal to noise ratio – varying from 30 to 0 dB – are introduced. In the identification process, different optimization methods are investigated: the deterministic Levenberg-Marquardt; the stochastic Particle Swarm Optimization; and a hybrid technique combining the aforementioned methods. It is shown that the damage identification procedure built on the wave propagation approach was successful, even for highly corrupted noisy data. Test case results are presented and a few comments on the advantages of deterministic and stochastic methods and their combination are also reported. Finally, an experimental validation of the sequential algebraic algorithm, used for modeling the direct problem, is presented.

Hybrid and Adaptive Metamodel Based Global Optimization

2009 ◽  
Author(s):  
J. Gu ◽  
G. Y. Li ◽  
Z. Dong
Keyword(s):  
Global Optimization ◽  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Method ◽  
Global Optimum ◽  
Superior Performance ◽  
Automatic Identification ◽  
Design Problems ◽  
Sample Data ◽  
Data Points

Metamodeling techniques are increasingly used in solving computation intensive design optimization problems today. In this work, the issue of automatic identification of appropriate metamodeling techniques in global optimization is addressed. A generic, new hybrid metamodel based global optimization method, particularly suitable for design problems involving computation intensive, black-box analyses and simulations, is introduced. The method employs three representative metamodels concurrently in the search process and selects sample data points adaptively according to the values calculated using the three metamodels to improve the accuracy of modeling. The global optimum is identified when the metamodels become reasonably accurate. The new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization problem involving vehicle crash simulation, to demonstrate the superior performance of the new algorithm over existing search methods. Present limitations of the proposed method are also discussed.

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.

scholarly journals SADEA-II: A generalized method for efficient global optimization of antenna design

2016 ◽  
Vol 4 (2) ◽  
pp. 86-97 ◽  
Author(s):  
Bo Liu ◽  
Slawomir Koziel ◽  
Nazar Ali
Keyword(s):  
Data Mining ◽  
Global Optimization ◽  
Design Optimization ◽  
Surrogate Model ◽  
Simulation Models ◽  
Optimization Methods ◽  
Optimization Method ◽  
Antenna Design ◽  
Efficient Global Optimization ◽  
Optimization Framework

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

Sign in / Sign up

Export Citation Format

Share Document