Last-Position Elimination-Based Fireworks Algorithm for Function Optimization

2020 ◽  
pp. 43-55
Author(s):  
JunQi Zhang ◽  
JianQing Chen ◽  
WeiZhi Li
Keyword(s):  
Global Optimum ◽  
Fixed Number ◽  
Function Optimization ◽  
Experimental Results ◽  
The Other ◽  
Local Optima ◽  
Fireworks Algorithm ◽  
Catch Up ◽  
Selection Operator ◽  
Initial Process

Fireworks algorithm (FWA) searches the global optimum by the cooperation between the firework with the best fitness named as core firework (CF) and the other non-CFs. Loser-out tournament-based fireworks algorithm (LoTFWA) uses competition as a new manner of interaction. If the fitness of a firework cannot catch up with the best one, it is considered a loser and will be reinitialized. However, its independent selection operator may prevent non-CFs from aggregating to CF in the late search phase if they fall into different local optima. This chapter proposes a last-position, elimination-based fireworks algorithm which allocates more fireworks in the initial process to search. Then for every fixed number of generations, the firework with the worst fitness is eliminated and its sparks is reallocated to other fireworks. In the final stage of search, only CF survives with all the budget of sparks and thus the aggregation of non-CFs to CF is ensured. Experimental results performed show that the proposed algorithm significantly outperforms most of the state-of-the-art FWA variants.

scholarly journals Konsep Generalized Gradient Pada Optimisasi Nonsmooth

2018 ◽  
Vol 1 (2) ◽  
pp. 168-174
Author(s):  
Esther Nababan ◽  
Pengarapen Bangun
Keyword(s):  
Lipschitz Condition ◽  
Real Function ◽  
Smooth Function ◽  
Function Optimization ◽  
Minimum Point ◽  
Necessary Condition ◽  
Literature Study ◽  
Generalized Gradient ◽  
Optimal Point ◽  
The Real

Tulisan ini merupakan kajian literature mengenai konsep generalized pada optimisasi fungsi nonsmooth. Generalized gradient  ¶f merupakan salah satu pengganti derivative atau turunan fungsi. Generalized gradient dikembangkan dari kenyataan bahwa suatu fungsi tak halus yang memenuhi kondisi Lipschitz disekitar suatu titik x pasti mempunyai turunan berarah di titik x dalam arah v, yaitu f o(x;v).Generalized gradient dari fungsi real  f  di suatu titik x didefinisikan sebagai¶f(x) : = {xÎ Rn : f o(x;v)³áv,xñ untuk semua v di Rn }. Secara geometri,  fungsi nonsmooth dapat dibuktikan mempunyai titik optimal apabila fungsi tersebut konveks. Dengan analisa konveksitas ditunjukkan bahwa syarat perlu untuk titik x* merupakan titik peminimum dari  fungsi tak halus f(x) adalah 0ζ f(x).   This paper was a literature study of generalized concepts in non-smooth function optimization. Generalized gradient ¶f is a substitute for derivatives or derivatives functions. Generalized gradient is developed from the fact that a subtle function that fulfills the Lipschitz condition around a point x must have a derivative directed at point x in the direction v, i.e. fo (x; v). Generalized gradient of the real function f at a point x is defined as f (x): = {xÎ Rn: fo (x; v) ³áv, xñ for all v in Rn}. Geometrically, the nonsmooth function can be proven to have an optimal point if the function is convex. Convinced analysis showed that the necessary condition for point x* was the minimum point of the subtle function f (x) is 0ζ f (x). 

A Low Complexity Adaptive Scheme for Channel Estimation

2013 ◽  
Vol 34 (8) ◽  
pp. 1980-1985
Author(s):  
Tian-ming Ma ◽  
Yu-song Shi ◽  
Feng-rong Li ◽  
Ying-guan Wang
Keyword(s):  
Channel Estimation ◽  
Low Complexity ◽  
Adaptive Scheme

Differentiation Coherence Algorithm for Steady Power Flow Control

2015 ◽  
Vol 9 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Yang Liu-Lin ◽  
Hang Nai-Shan
Keyword(s):  
Flow Control ◽  
Extreme Point ◽  
Power Flow ◽  
Reactive Power ◽  
Small Deviation ◽  
Function Optimization ◽  
Flow Mode ◽  
Power Flow Control ◽  
Active And Reactive Power ◽  
Variable Inequality

This paper researched steady power flow control with variable inequality constraints. Since the inverse function of power flow equation is hard to obtain, differentiation coherence algorithm was proposed for variable inequality which is tightly constrained. By this method, tightly constrained variable inequality for variables adjustment relationships was analyzed. The variable constrained sensitivity which reflects variable coherence was obtained to archive accurate extreme equation for function optimization. The hybrid power flow mode of node power with branch power was structured. It also structured the minimum variable model correction equation with convergence and robot being same as conventional power flow. In fundamental analysis, the effect of extreme point was verified by small deviation from constrained extreme equation, and the constrained sensitivity was made for active and reactive power. It pointed out possible deviation by using simplified non-constrained sensitivity to deal with the optimization problem of active and reactive power. The control solutions for power flow for optimal control have been discussed as well. The examples of power flow control and voltage management have shown that the algorithm is simple and concentrated and shows the effect of differential coherence method for extreme point analysis.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

Sign in / Sign up

Export Citation Format

Share Document