Choosing the best parameters for method of deformed stars in n-dimensional space

2021 ◽  
pp. 24-28
Author(s):  
Maryna Antonevych ◽  
Anna Didyk ◽  
Nataliia Tmienova ◽  
Vitaliy Snytyuk
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Classical Method ◽  
Dimensional Space ◽  
New Method ◽  
Evolutionary Strategy ◽  
Point Groups ◽  
Best Parameters

This paper is devoted to the problem of optimization of a function in -dimensional space, which, in general case, is polyextreme and undifferentiated. The new method of deformed stars in n-dimensional space was proposed. It is built on the ideas and principles of the evolutionary paradigm. Method of deformed stars is based on the assumption of using potential solutions groups. There by it allows to increase the rate of the accuracy and the convergence of the achieved result. Populations of potential solutions are used to optimize the multivariable function. In contrast to the classical method of deformed stars, we obtained a method that solves problems in -dimensional space, where the population of solutions consists of 3-, 4-, and 5-point groups. The advantages of the developed method over genetic algorithm, differential evolution and evolutionary strategy as the most typical evolutionary algorithms are shown. Also, experiments were performed to investigate the best configuration of method of deformed stars parameters.

Improving the optimal solution of GoYang network – using genetic algorithm and differential evolution

2019 ◽  
Vol 20 (1) ◽  
pp. 95-102 ◽  
Author(s):  
S. N. Poojitha ◽  
Gagandeep Singh ◽  
V. Jothiprakash
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Benchmark Problem ◽  
Future Studies ◽  
Loop Network ◽  
Optimum Solution

Abstract In the present study, an attempt is made to search for better solutions for the Hanoi network, BakRyan network, and GoYang network (GYN) through evolutionary algorithms (EAs) such as genetic algorithm (GA) and differential evolution (DE), which are initially validated using the two-loop network. A detailed note on the classification of available benchmark-problems is reported. The major aim of the study is to improve the optimal solution of GYN, which can emerge as a standard benchmark-problem for future studies. On applying the developed EA models, an improved optimal cost compared with the literature is obtained for GYN. From the results, it is found that DE outshines GA by its better convergence capability and robustness in attaining an optimum solution.

scholarly journals On the Use of Compact Approaches in Evolution Strategies

2014 ◽  
Vol 3 (4) ◽  
pp. 13-23
Author(s):  
Anderson Sergio ◽  
Sidartha Carvalho ◽  
Marco Rego
Keyword(s):  
Genetic Algorithm ◽  
Probability Distribution ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Evolution Strategies ◽  
Processing Power ◽  
Compact Genetic Algorithm ◽  
Efficient Alternative ◽  
Computing Environments

Compact evolutionary algorithms have proven to be an efficient alternative for solving optimization problems in computing environments with low processing power. In this kind of solution, a probability distribution simulates the behavior of a population, thus looking for memory savings. Several compact algorithms have been proposed, including the compact genetic algorithm and compact differential evolution. This work aims to investigate the use of compact approaches in other important evolutionary algorithms: evolution strategies. This paper proposes two different approaches for compact versions of evolution strategies. Experiments were performed and the results analyzed. The results showed that, depending on the nature of problem, the use of the compact version of Evolution Strategies can be rewarding.

Flight control clearance of the Cessna Citation X using evolutionary algorithms

2016 ◽  
Vol 231 (3) ◽  
pp. 510-532
Author(s):  
Yamina Boughari ◽  
Ruxandra Mihaela Botez ◽  
Georges Ghazi ◽  
Florian Theel
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Objective Function ◽  
Linear Model ◽  
Differential Evolution Algorithm ◽  
Good Level ◽  
Flying Qualities ◽  
Flight Envelope ◽  
Evolution Algorithm

In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft’s mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H∞ method and two different metaheuristic algorithms; in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H∞ weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft’s flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was concluded that very good performance of the business Cessna Citation X aircraft was achieved in this research.

CHAOS SYNTHESIS BY MEANS OF EVOLUTIONARY ALGORITHMS

2008 ◽  
Vol 18 (04) ◽  
pp. 911-942 ◽  
Author(s):  
IVAN ZELINKA ◽  
GUANRONG CHEN ◽  
SERGEJ CELIKOVSKY
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Differential Evolution ◽  
Cost Function ◽  
Case Studies ◽  
Chaotic System ◽  
Chaotic Systems ◽  
Reliability And Robustness ◽  
Eleven Case

This paper introduces the notion of chaos synthesis by means of evolutionary algorithms and develops a new method for chaotic systems synthesis. This method is similar to genetic programming and grammatical evolution and is being applied along with three evolutionary algorithms: differential evolution, self-organizing migration and genetic algorithm. The aim of this investigation is to synthesize new and "simple" chaotic systems based on some elements contained in a prechosen existing chaotic system and a properly defined cost function. The investigation consists of eleven case studies: the aforementioned three evolutionary algorithms in eleven versions. For all algorithms, 100 simulations of chaos synthesis were repeated and then averaged to guarantee the reliability and robustness of the proposed method. The most significant results were carefully selected, visualized and commented in this report.

Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms

2019 ◽  
Vol 234 (5) ◽  
pp. 1472-1479 ◽  
Author(s):  
Tey Jing Yuen ◽  
Rahizar Ramli
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Time Frame ◽  
Motor Power ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective

A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%.

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