scholarly journals A New Global Optimization Algorithm for Solving Generalized Geometric Programming

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
San-Yang Liu ◽  
Chun-Feng Wang ◽  
Li-Xia Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Geometric Programming ◽  
Optimal Solution ◽  
Global Optimization Algorithm ◽  
Global Optimal Solution ◽  
Linearization Technique ◽  
Generalized Geometric Programming ◽  
Global Optimal ◽  
Pruning Technique

A global optimization algorithm for solving generalized geometric programming (GGP) problem is developed based on a new linearization technique. Furthermore, in order to improve the convergence speed of this algorithm, a new pruning technique is proposed, which can be used to cut away a large part of the current investigated region in which the global optimal solution does not exist. Convergence of this algorithm is proved, and some experiments are reported to show the feasibility of the proposed algorithm.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

A novel adaptive butterfly optimization algorithm

2018 ◽  
Vol 07 (04) ◽  
pp. 1850026 ◽  
Author(s):  
Bhupinder Singh ◽  
Priyanka Anand
Keyword(s):  
Optimization Algorithm ◽  
Sensory Modality ◽  
Optimal Solution ◽  
Bench Mark ◽  
Optimal Solutions ◽  
Global Optimal Solution ◽  
Simulation Process ◽  
Bee Colony ◽  
Global Optimal ◽  
Loss Of Diversity

Butterfly optimization algorithm (BOA) is an interesting bio-inspired algorithm that uses a nature inspired simulation model, based on the food foraging behavior of butterflies. A common problem with BOA is that in early stages of simulation process, it may converge to sub-optimal solutions due to the loss of diversity in its population. The sensory modality is the critical parameter which is responsible for searching new solutions in the nearby regions. In this work, an adaptive butterfly optimization algorithm is proposed in which a novel phenomenon of changing the sensory modality of BOA is employed during the optimization process in order to achieve better results in comparison to traditional BOA. The proposed Adaptive butterfly optimization algorithm (ABOA) is tested against seventeen standard bench mark functions. Its performance is then compared against existing standard optimization algorithms, namely artificial bee colony, firefly algorithm and standard butterfly optimization algorithm. The results indicate that the proposed adaptive BOA with improved parameter calculation mechanism produces superior results in terms of convergence and achievement of the global optimal solution efficiently.

scholarly journals Global Optimization for Sum of Linear Ratios Problem Using New Pruning Technique

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Hongwei Jiao ◽  
Qigao Feng ◽  
Peiping Shen ◽  
Yunrui Guo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Global Minimum ◽  
Linearization Method ◽  
Linear Constraints ◽  
Feasible Region ◽  
Global Optimization Algorithm ◽  
Equivalent Problem ◽  
Successive Refinement ◽  
Pruning Technique

A global optimization algorithm is proposed for solving sum of general linear ratios problem (P) using new pruning technique. Firstly, an equivalent problem (P1) of the (P) is derived by exploiting the characteristics of linear constraints. Then, by utilizing linearization method the relaxation linear programming (RLP) of the (P1) can be constructed and the proposed algorithm is convergent to the global minimum of the (P) through the successive refinement of the linear relaxation of feasible region and solutions of a series of (RLP). Then, a new pruning technique is proposed, this technique offers a possibility to cut away a large part of the current investigated feasible region by the optimization algorithm, which can be utilized as an accelerating device for global optimization of problem (P). Finally, the numerical experiments are given to illustrate the feasibility of the proposed algorithm.

Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression

2012 ◽  
Vol 614-615 ◽  
pp. 409-413 ◽  
Author(s):  
Zhi Biao Shi ◽  
Ying Miao
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Global Optimal Solution ◽  
Fruit Fly Optimization ◽  
Global Optimal

In order to solve the blindness of the parameter selection in the Support Vector Regression (SVR) algorithm, we use the Fruit Fly Optimization Algorithm (FOA) to optimize the parameters in SVR, and then propose the optimization algorithm on the parameters in SVR based on FOA to fitting and simulate the experimental data of the turbine’s failures. This algorithm could optimize the parameters in SVR automatically, and achieve ideal global optimal solution. By comparing with the commonly used methods such as Support Vector Regression and Radial Basis Function neural network, it can be shown that the forecast results of FOA_SVR more accurate and the forecast speed is the fastest.

scholarly journals Global Optimization for the Sum of Concave-Convex Ratios Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
XueGang Zhou ◽  
JiHui Yang
Keyword(s):  
Convex Programming ◽  
Convex Set ◽  
Nonconvex Programming ◽  
Compact Convex ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Equivalent Problem ◽  
Linearization Technique ◽  
Compact Convex Set ◽  
Global Optimal

This paper presents a branch and bound algorithm for globally solving the sum of concave-convex ratios problem (P) over a compact convex set. Firstly, the problem (P) is converted to an equivalent problem (P1). Then, the initial nonconvex programming problem is reduced to a sequence of convex programming problems by utilizing linearization technique. The proposed algorithm is convergent to a global optimal solution by means of the subsequent solutions of a series of convex programming problems. Some examples are given to illustrate the feasibility of the proposed algorithm.

scholarly journals A Global Optimization Algorithm for Signomial Geometric Programming Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xue-Ping Hou ◽  
Pei-Ping Shen ◽  
Yong-Qiang Chen
Keyword(s):  
Linear Programming ◽  
Global Optimization ◽  
Programming Problem ◽  
Optimization Algorithm ◽  
Geometric Programming ◽  
Nonconvex Programming ◽  
Algebraic Manipulation ◽  
Global Optimization Algorithm ◽  
Linear Programming Problems ◽  
Signomial Geometric Programming

This paper presents a global optimization algorithm for solving the signomial geometric programming (SGP) problem. In the algorithm, by the straight forward algebraic manipulation of terms and by utilizing a transformation of variables, the initial nonconvex programming problem (SGP) is first converted into an equivalent monotonic optimization problem and then is reduced to a sequence of linear programming problems, based on the linearizing technique. To improve the computational efficiency of the algorithm, two range reduction operations are combined in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (SGP) by means of the subsequent solutions of a series of relaxation linear programming problems. And finally, the numerical results are reported to vindicate the feasibility and effectiveness of the proposed method.

A New BP Algorithm and its Application

2013 ◽  
Vol 811 ◽  
pp. 413-416
Author(s):  
Fang Liu ◽  
Yue Guang Li
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Network Structure ◽  
Binary Code ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Global Optimal Solution ◽  
The Best Approximation ◽  
Global Optimal ◽  
Simulation Results

In this paper, based on the combination of Genetic algorithm and BP algorithm, a new algorithm is proposed in this paper. The BP operator is embedded in the genetic operation in the algorithm, the algorithm effectively assimilates the global optimization of genetic algorithm and fast convergence of BP algorithm, and it encodes the construction and the weights hybrid with real code and binary code, achieving the same step optimization of structure and weights. The simulation results show that, the new algorithm can quickly converge to the global optimal solution, but also can obtain the best approximation of weights in the network structure.

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