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>

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

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.

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 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.

A Heterogeneous Strategy Genetic Algorithm and its Application in Dynamic Optimization of Structure

2012 ◽  
Vol 204-208 ◽  
pp. 4827-4830
Author(s):  
Le Wei Yan ◽  
Yang Yang Chen
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Numerical Examples ◽  
Random Loads ◽  
Global Optimal

Heterogeneous strategy is used to improve genetic algorithm. It can increase the population diversity and avoid premature while the convergence efficiency is ensured. Furthermore, an expression to estimate the extent of inbreeding and two methods for selecting heterogeneity were given, and, an improved generalized genetic algorithm was presented in this paper. This algorithm is used for multi-parameter dynamic optimization of the structure which is under random loads and has stress constrains. Numerical examples demonstrated that heterogeneous strategy can improve the probability of convergence to global optimal solution and the improved generalized genetic.

scholarly journals On Locally and Globally Optimal Solutions in Scalar Variational Control Problems

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 829 ◽  
Author(s):  
Savin Treanţă
Keyword(s):  
Control Problem ◽  
Optimal Solution ◽  
Control Problems ◽  
Optimal Solutions ◽  
Global Optimal Solution ◽  
New Class ◽  
Minimal Criterion ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Curvilinear Integral

In this paper, optimality conditions are studied for a new class of PDE and PDI-constrained scalar variational control problems governed by path-independent curvilinear integral functionals. More precisely, we formulate and prove a minimal criterion for a local optimal solution of the considered PDE and PDI-constrained variational control problem to be its global optimal solution. The effectiveness of the main result is validated by a two-dimensional non-convex scalar variational control problem.

A Self-Adaptive and Variable Step Length Alopex Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4302-4307
Author(s):  
Dong Li
Keyword(s):  
Optimal Solution ◽  
Step Length ◽  
Global Optimal Solution ◽  
Optimum Algorithm ◽  
Variable Step ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Simulation Results ◽  
Self Adaptive

Alopex is a heuristic and optimum algorithm. A self-adaptive and variable step length Alopex algorithm was raised to exceed local optimal solution and to approximate global optimal solution based on modified Alopex algorithm. To improve modified Alopex approximation precision further and eliminate the follow-on shocks appearance, the reasonable alter of δin was implemented. The simulation results show algorithm optimized is practicable and effective.

scholarly journals Spiral-based chaotic chicken swarm optimization algorithm for parameters identification of photovoltaic models

2021 ◽  
Author(s):  
Miao Li ◽  
Chunquan Li ◽  
Zhengyu Huang ◽  
Jiehui Huang ◽  
Gaige Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Heuristic Methods ◽  
Global Optimal Solution ◽  
Swarm Optimization ◽  
Spiral Trajectory ◽  
Pv Systems ◽  
Reliable Performance ◽  
Global Optimal ◽  
Chicken Swarm Optimization

Abstract Photovoltaic (PV) systems are becoming increasingly significant because they can convert solar energy into electricity. The conversion efficiency is related to the PV models’ parameters, so it is crucial to identify parameters of PV models. Recently, various heuristic methods have been proposed to identify the parameters, but they cannot provide sufficient accurate and reliable performance. To address this problem, this paper proposes a spiral-based chaos chicken swarm optimization algorithm (SCCSO) including three strategies: i) the information-sharing strategy provides the latest information of the roosters for searching global optimal solution, beneficial to improve the exploitation ability; ii) the spiral motion strategy can enable hens and chicks to move towards their corresponding targets with a spiral trajectory, improving the exploration ability; iii) a self-adaptive-based chaotic disturbance mechanism is introduced around the global optimal solution to generate a promising solution for the worst chick at each iteration, thereby improving the convergence speed of the chicken flock. Besides, SCCSO is used for identifying different PV models such as the single diode, the double diode, and PV module models. Comprehensive analysis and experimental results show that SCCSO provides better robustness and accuracy than other advanced heuristic methods.

AN OPTIMIZATION APPROACH TO LOCATING AND STABILIZING UNSTABLE PERIODIC ORBITS OF CHAOTIC SYSTEMS

2002 ◽  
Vol 12 (05) ◽  
pp. 1163-1172 ◽  
Author(s):  
YU-PING TIAN
Keyword(s):  
Periodic Orbits ◽  
Chaotic Systems ◽  
Main Idea ◽  
Optimal Solution ◽  
Optimization Process ◽  
Optimization Approach ◽  
Unstable Periodic Orbits ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal

In this paper, a novel method for locating and stabilizing inherent unstable periodic orbits (UPOs) in chaotic systems is proposed. The main idea of the method is to formulate the UPO locating problem as an optimization issue by using some inherent properties of UPOs of chaotic systems. The global optimal solution of this problem yields the desired UPO. To avoid a local optimal solution, the state of the controlled chaotic system is absorbed into the initial condition of the optimization problem. The ergodicity of chaotic dynamics guarantees that the optimization process does not stay forever at any local optimal solution. When the chaotic orbit approaches the global optimal solution, which is the desired UPO, the controller will stabilize it at the UPO, and the optimization process will cease simultaneously. The method has been developed for both discrete-time and continuous-time systems, and validated for some typical chaotic systems such as the Hénon map and the Duffing oscillator, among others.

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

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