Conservative and Non-Conservative Evolutionary Computations in Connection with Wolf-Rayet Binaries

1979 ◽  
pp. 483-490
Author(s):  
D. Vanbeveren ◽  
J. P. Grève ◽  
C. Loore ◽  
E. L. Dessel
Keyword(s):  
Evolutionary Computations

scholarly journals Graphics Evolutionary Computations in Higher Order Parametric Bezier Curves

2022 ◽  
Vol 41 (2) ◽  
pp. 595-609
Author(s):  
Monday Eze ◽  
Charles Okunbor ◽  
Deborah Aleburu ◽  
Olubukola Adekola ◽  
Ibrahim Ramon ◽  
...  
Keyword(s):  
Higher Order ◽  
Evolutionary Computations ◽  
Bezier Curves ◽  
Bézier Curves

scholarly journals Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems

2011 ◽  
Vol 15 (5) ◽  
pp. 515-524
Author(s):  
Yafei Xing ◽  
◽  
Singo Mabu ◽  
Lian Yuzhu ◽  
Kotaro Hirasawa
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Genetic Network ◽  
Rule Extraction ◽  
Network Programming ◽  
Evolutionary Computations ◽  
Stock Trading ◽  
Trading Rules ◽  
Rules Extraction ◽  
Genetic Network Programming

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by Genetic Network Programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that themulti-order rules extracted by GNP is more effective than the 1st order rules for stock trading.

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

Optimization-based transient control of turbofan engines: a sequential quadratic programming approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jiqiang Wang ◽  
Huan Hu ◽  
Weicun Zhang ◽  
Zhongzhi Hu
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Programming Approach ◽  
Evolutionary Computations ◽  
Turbofan Engines ◽  
Flight Envelope ◽  
Transient Control ◽  
Limit Protection ◽  
Computational Resources ◽  
Design Guidance

Abstract Engine transient control has been challenging due to its stringent requirements from both performance and safety. Many methodologies have been proposed such as conventional schedule-based methods, linear parameter varying, multiobjective optimization and evolutionary computations etc. These approaches have been well-established and led to a series of significant results. However, they are either not providing limit protection or requiring exhaustive computational resources, particularly when generating results into full flight envelope applications. Consequently a compromise between limit protection and computational complexity is necessitated. This note considers a sequential quadratic programming (SQP)-based method for full flight envelope investigations. The proposed method can provide important design guidance and the corresponding claims are validated through detailed analysis and simulations.

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