Clonal and Cauchy-mutation Evolutionary Algorithm for Global Numerical Optimization

2009 ◽  
pp. 217-227
Author(s):  
Jing Guan ◽  
Ming Yang
Keyword(s):  
Evolutionary Algorithm ◽  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Cauchy Mutation

A NOVEL EVOLUTIONARY ALGORITHM ENSEMBLE FOR GLOBAL NUMERICAL OPTIMIZATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350022
Author(s):  
YONGYONG NIU ◽  
ZIXING CAI ◽  
MIN JIN
Keyword(s):  
Evolutionary Algorithm ◽  
Evolutionary Computation ◽  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Covariance Matrix Adaptation ◽  
The Individual ◽  
Benchmark Test Functions ◽  
Adaptation Evolution ◽  
Better Than

In the past few years, evolutionary algorithm ensembles have gradually attracted more and more attention in the community of evolutionary computation. This paper proposes a novel evolutionary algorithm ensemble for global numerical optimization, named NEALE. In order to make a good tradeoff between the exploration and exploitation, NEALE is composed of two constituent algorithms, i.e., the composite differential evolution (CoDE) and the covariance matrix adaptation evolution strategy (CMA-ES). During the evolution, CoDE aims at probing more promising regions and refining the overall quality of the population, while the purposes of CMA-ES are to accelerate the convergence speed and to enhance the accuracy of the solutions. In addition, NEALE encourages the interaction between the constituent algorithms. In NEALE, the interaction is controlled by a predefined generation number and different interaction strategies are designed according to the features of the constituent algorithms. The performance of NEALE has been tested on 25 benchmark test functions developed for the special session on real-parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (IEEE CEC2005). Compared with other state-of-the-art evolutionary algorithms and the individual constituent algorithms, NEALE performs significantly better than them.

scholarly journals Optimal Operation of the Voltage-Doubler Boost Converter through an Evolutionary Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 423
Author(s):  
Cesar Ibarra-Nuño ◽  
Alma Rodríguez ◽  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Juan M. Ramirez ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Numerical Optimization ◽  
Boost Converter ◽  
Optimal Operation ◽  
Input Current ◽  
Voltage Gain ◽  
Duty Cycles ◽  
Switching Functions ◽  
Voltage Doubler ◽  
Current Ripple

This manuscript presents the numerical optimization (through a mathematical model and an evolutionary algorithm) of the voltage-doubler boost converter, also called the series-capacitor boost converter. The circuit is driven by two transistors, each of them activated according to a switching signal. In the former operation, switching signals have an algebraic dependence from each other. This article proposes a new method to operate the converter. The proposed process reduces the input current ripple without changing any converter model parameter, only the driving signals. In the proposed operation, switching signals of transistors are independent of each other, providing an extra degree of freedom, but on the other hand, this produces an infinite number of possible combinations of duty cycles (the main parameter of switching signals) to achieve the desired voltage gain. In other words, this leads to a problem with infinite possible solutions. The proposed method utilizes an evolutionary algorithm to determine the switching functions and, at the same time, to minimize the input current ripple of the converter. A comparison made between the former and the proposed operation shows that the proposed process achieves a lower input current ripple while achieving the desired voltage gain.

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.

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