A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem

2019 ◽  
Vol 8 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Syeda Shabnam Hasan ◽  
Rashida Rahman ◽  
Khurshida Akther Jahan ◽  
Shaheda Islam ◽  
Arshiful Islam Shadman ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Fuzzy Controller ◽  
Bat Algorithm ◽  
Function Optimization ◽  
Size Parameter ◽  
Step Size ◽  
Existing Problems ◽  
Candidate Solution ◽  
And Control ◽  
Better Than

This article introduces Fuzzy Inspired Bat Algorithm (FIBA), which is an improved variant of the original Bat algorithm. The novelty of FIBA lies in the integration of a fuzzy controller with the basic Bat algorithm that tries to bring balance between the degree of explorations and exploitations during the mutation operation. Another novelty of FIBA is the introduction of a step size parameter, maintained separately for every candidate solution, to customize and control the mix of explorative and exploitative operations around each candidate solution. FIBA is tested on a standard benchmark set that includes 10 complex, scalable, high dimensional functions. The results on benchmark functions reveal that FIBA can perform sufficiently well, and often better than the original Bat algorithm and another recently proposed improved Bat variant. Such improvements on the experimental results imply that the fuzzy technique adopted by FIBA might be effective on other existing problems as well, and hence demand further research and investigation.

Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 304-308
Author(s):  
Lu Gang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Extreme Points ◽  
Function Optimization ◽  
Experimental Result ◽  
Genetic Optimization ◽  
Binary Coding ◽  
Coding Method ◽  
Peak Function ◽  
Better Than

In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

2020 ◽  
Vol 11 (3) ◽  
pp. 41-57
Author(s):  
Pandian Vasant ◽  
Fahad Parvez Mahdi ◽  
Jose Antonio Marmolejo-Saucedo ◽  
Igor Litvinchev ◽  
Roman Rodriguez Aguilar ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Real World Problem ◽  
Valve Point Effect ◽  
Power Generation Systems ◽  
Better Than

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

Design of Self-Regulating Fuzzy Control System for Vacuum Sintering Furnace

2009 ◽  
Vol 16-19 ◽  
pp. 140-144 ◽  
Author(s):  
Qi Gao Feng ◽  
Han Ping Mao ◽  
Xue Ming Zhang
Keyword(s):  
Pid Controller ◽  
Fuzzy Controller ◽  
Heating Temperature ◽  
Sintering Process ◽  
Control Effect ◽  
Vacuum Sintering ◽  
Large Delay ◽  
Application Fields ◽  
And Control ◽  
Better Than

Although the traditional PID controller is widely used in many practical application fields, it’s unsuitable for the control of the complex plant. The powder metallurgy sintering process had such characteristics as nonlinearity and large delay, etc., and it’s difficult for the conventional PID controller to meet the control requirements. According to these traits, we used a parameter self-adjusting fuzzy controller to control the heating temperature of the vacuum sintering furnace. In practice it shows that every quality index and control effect of this scheme is better than that of traditional PID controller.

scholarly journals A high-fidelity realization of the Euclid code comparison N-body simulation with Abacus

2019 ◽  
Vol 485 (3) ◽  
pp. 3370-3377 ◽  
Author(s):  
Lehman H Garrison ◽  
Daniel J Eisenstein ◽  
Philip A Pinto
Keyword(s):  
Power Spectrum ◽  
High Performance ◽  
High Fidelity ◽  
Step Size ◽  
Time Step ◽  
Code Comparison ◽  
Force Accuracy ◽  
Integration Errors ◽  
Better Than ◽  
Good Agreement

Abstract We present a high-fidelity realization of the cosmological N-body simulation from the Schneider et al. code comparison project. The simulation was performed with our AbacusN-body code, which offers high-force accuracy, high performance, and minimal particle integration errors. The simulation consists of 20483 particles in a $500\ h^{-1}\, \mathrm{Mpc}$ box for a particle mass of $1.2\times 10^9\ h^{-1}\, \mathrm{M}_\odot$ with $10\ h^{-1}\, \mathrm{kpc}$ spline softening. Abacus executed 1052 global time-steps to z = 0 in 107 h on one dual-Xeon, dual-GPU node, for a mean rate of 23 million particles per second per step. We find Abacus is in good agreement with Ramses and Pkdgrav3 and less so with Gadget3. We validate our choice of time-step by halving the step size and find sub-percent differences in the power spectrum and 2PCF at nearly all measured scales, with ${\lt }0.3{{\ \rm per\ cent}}$ errors at $k\lt 10\ \mathrm{Mpc}^{-1}\, h$. On large scales, Abacus reproduces linear theory better than 0.01 per cent. Simulation snapshots are available at http://nbody.rc.fas.harvard.edu/public/S2016.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

Comparative studies of the set up of two-dimensional pneumatic arm systems by muscle and rotational actuators

2007 ◽  
Vol 221 (5) ◽  
pp. 743-748 ◽  
Author(s):  
Y-T Wang ◽  
R-H Wong ◽  
J-T Lu
Keyword(s):  
Control Systems ◽  
Fuzzy Controller ◽  
Control Algorithms ◽  
Two Dimensional ◽  
Pneumatic Control ◽  
Learning Mechanism ◽  
Set Up ◽  
And Control ◽  
Self Learning ◽  
System Characteristics

As opposed to traditional pneumatic linear actuators, muscle and rotational actuators are newly developed actuators in rotational and specified applications. In the current paper, these actuators are used to set up two-dimensional pneumatic arms, which are used mainly to simulate the excavator's motion. Fuzzy control algorithms are typically applied in pneumatic control systems owing to their non-linearities and ill-defined mathematical model. The self-organizing fuzzy controller, which includes a self-learning mechanism to modify fuzzy rules, is applied in these two-dimensional pneumatic arm control systems. Via a variety of trajectory tracking experiments, the present paper provides comparisons of system characteristics and control performances.

scholarly journals EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Yi Ren ◽  
Alparslan Emrah Bayrak ◽  
Panos Y. Papalambros
Keyword(s):  
Optimization Problem ◽  
Solution Space ◽  
Efficient Global Optimization ◽  
Vehicle Design ◽  
Control Problems ◽  
Short Term ◽  
Algorithm Efficiency ◽  
Np Complete ◽  
And Control

We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition and received 2391 game plays by 124 anonymous players during the first month from launch. We found that while only a small portion of human players can outperform the algorithm in the long term, players tend to formulate good heuristics early on that can be used to constrain the solution space. Such constraining of the search enhances algorithm efficiency, even for different game settings. These findings indicate that human-assisted computational searches are promising in solving comprehensible yet computationally hard optimal design and control problems, when human players can outperform the algorithm in a short term.

scholarly journals New Cancer Stochastic Models Involving Both Hereditary and Nonhereditary Cancer Cases: A New Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-19
Author(s):  
Wai-Yuan Tan ◽  
Hong Zhou
Keyword(s):  
Cancer Incidence ◽  
Stochastic Models ◽  
Biological Information ◽  
Cancer Genes ◽  
New Approach ◽  
Human Cancers ◽  
Seer Data ◽  
Sampling Procedures ◽  
And Control ◽  
Better Than

To incorporate biologically observed epidemics into multistage models of carcinogenesis, in this paper we have developed new stochastic models for human cancers. We have further incorporated genetic segregation of cancer genes into these models to derive generalized mixture models for cancer incidence. Based on these models we have developed a generalized Bayesian approach to estimate the parameters and to predict cancer incidence via Gibbs sampling procedures. We have applied these models to fit and analyze the SEER data of human eye cancers from NCI/NIH. Our results indicate that the models not only provide a logical avenue to incorporate biological information but also fit the data much better than other models. These models would not only provide more insights into human cancers but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.

scholarly journals THE EFFECTIVENESS OF REALISTIC MATHEMATICS EDUCATION TO IMPROVE STUDENTS’ MULTI-REPRESENTATION ABILITY

2019 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Muhtarom Muhtarom ◽  
Nizaruddin Nizaruddin ◽  
Farida Nursyahidah ◽  
Nurina Happy
Keyword(s):  
Mathematics Education ◽  
Experimental Design ◽  
Learning Process ◽  
Realistic Mathematics Education ◽  
Student Responses ◽  
Realistic Mathematics ◽  
Quasi Experimental ◽  
And Control ◽  
Essay Test ◽  
Better Than

This research aimed to evaluate the effectiveness of Realistic Mathematics Education (RME) to improve students' multi-representation ability. A quasi-experimental design was used in this research. Sixty-four samples from the seventh-grade students of Junior School were randomly selected and divided into two classes: experimental class was treated using RME and control class was treated using conventional learning, with each class consisting of thirty-two students. The essay test was used to measure the multi-representation ability of students and the questionnaire was used to measure students' responses in RME learning. The data from the essay test were analyzed by N-Gain test and t-test in which normality and homogenity test were conducted previously, while the students' learning completeness and student responses were presented descriptive quantitative. The result of the research concluded that the multi-representation ability of students who get RME learning is better than the multi-representation ability in students who get conventional learning. 87.25% of students who get RME learning with the developed device have completed the KKM, and many students are very enthusiastic and interested in RME based learning, thus increasing their learning spirit in a learning process.

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