scholarly journals Power Factor Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

2019 ◽  
Author(s):  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
Francisco M. Arrabal-Campos ◽  
Raul Baños
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Power Factor ◽  
Geometric Algebra ◽  
Active Filters ◽  
High Quality ◽  
Passive Filters ◽  
Algebra Theory ◽  
Open Question

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control over the flow of electricity between the source and the load, they are often used passive or active filters. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtain high quality solutions that could be generalized to similar problems of any dimension.

scholarly journals Quadrature Current Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 692 ◽  
Author(s):  
Francisco Montoya ◽  
Alfredo Alcayde ◽  
Francisco Arrabal-Campos ◽  
Raul Baños
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Geometric Algebra ◽  
Active Filters ◽  
High Quality ◽  
Current Compensation ◽  
Passive Filters ◽  
Algebra Theory ◽  
Open Question

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control the flow of electricity between the source and the load, passive or active filters are often used. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtains high quality solutions that could be generalized to similar problems of any dimension.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms

2012 ◽  
pp. 72-98
Author(s):  
Wenting Mo ◽  
Sheng-Uei Guan ◽  
Sadasivan Puthusserypady
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Problem Solving ◽  
Benchmark Problems ◽  
Multiple Objective ◽  
Nsga Ii ◽  
Incremental Approach ◽  
Strength Pareto Evolutionary Algorithm ◽  
Conflicting Objectives ◽  
Open Question

Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.

An Object-Oriented Framework for Rapid Genetic Algorithm Development

2007 ◽  
pp. 608-624 ◽  
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Rapid Development ◽  
Object Oriented ◽  
Lot Sizing Problem ◽  
Fast Development ◽  
Complex Optimization

JGA, the acronym for Java Genetic Algorithm, is a computational object-oriented framework for rapid development of evolutionary algorithms for solving complex optimization problems. This chapter describes the JGA framework and illustrates its use on the dynamic inventory lot-sizing problem. Using this problem as benchmark, JGA is compared against three other tools, namely, GAlib, an open C++ implementation; GADS, a commercial MatlabÒ toolbox; and PROC GA, a commercial (yet experimental) SASÒ procedure. JGA has proved to be a flexible and extensible object-oriented framework for the fast development of single (and multi-objective) genetic algorithms by providing a collection of ready-to-use modules (Java classes) that comprise the nucleus of any genetic algorithm. Furthermore, JGA has also been designed to be embedded in larger applications that solve complex business problems.

A meta-parameter tuning model to improve the genetic algorithms design of labeling diversity mappers

2021 ◽  
Author(s):  
Shaheen Solwa ◽  
Ayodeji James Bamisaye
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Parameter Tuning ◽  
Space Time ◽  
Diversity Gain ◽  
Parameter Setting ◽  
Fitness Value ◽  
Best Parameter ◽  
Diversity Gains

Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.

scholarly journals Evolutionary Algorithms in Stabilization of Inverted Pendulum

2020 ◽  
Vol 8 (6) ◽  
pp. 1447-1453
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Test Bench ◽  
Design Parameters ◽  
Characteristic Parameters ◽  
Linkage Learning

A novel optimization algorithm of fuzzy logic controller (FLC) using genetic algorithms is used to characterize the major design parameters of an FLC known as characteristic parameters. The characteristic parameters simplify the design of FLC which are encoded into a chromosome as an integer string. These are optimized by maximizing the evaluated fitness through genetic operations to achieve the optimized FLC .An effective Genetic Algorithm (GA) is proposed using linkage learning, or building block identification. The genes arranged to have a fitness enhancement is the essence of linkage learning. A perfect and faster extended GA is suggested using an effective method to learn distributions and then by linking them. Stabilization of Inverted pendulum pole angle is taken as test bench.

Heuristic Artificial Intelligent Algorithm for Genetic Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 516-521 ◽  
Author(s):  
Luo Lie
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Search Technique ◽  
Intelligent Algorithm ◽  
Search Problems ◽  
Artificial Intelligent ◽  
Search Heuristics

A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms

2010 ◽  
Vol 1 (2) ◽  
pp. 1-27 ◽  
Author(s):  
Wenting Mo ◽  
Sheng-Uei Guan ◽  
Sadasivan Puthusserypady
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
State Of The Art ◽  
Benchmark Problems ◽  
Multiple Objective ◽  
Nsga Ii ◽  
Incremental Approach ◽  
Strength Pareto Evolutionary Algorithm ◽  
Conflicting Objectives ◽  
Open Question

Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.

EVOLUTIONARY METHODS AND GENETIC ALGORITHMS FOR NUMERICAL CALCULATION OF THE OPTIMAL DESIGN OF NON-RIGID ROAD SURFACES

2021 ◽  
pp. 84-90
Author(s):  
А.А. Тайлакова ◽  
А.Г. Пимонов
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Numerical Calculation ◽  
Optimal Design ◽  
Evolutionary Algorithms ◽  
Hybrid Genetic Algorithm ◽  
Road Surfaces ◽  
Cost Of Materials ◽  
The Cost ◽  
Full Search

В статье представлена оптимизационная модель для расчета конструкций нежестких дорожных одежд, обосновано применение эволюционных алгоритмов в сочетании с полным перебором и параллельными вычислениями и описан разработанный гибридный генетический алгоритм The article presents an optimization model for calculating structures of non-rigid road surfaces, justifies the use of evolutionary algorithms in combination with full search and parallel calculations, and describes the developed hybrid genetic algorithm for optimizing the design of non-rigid road surfaces at the cost of materials of structural layers.

scholarly journals How Crossover Speeds up Building Block Assembly in Genetic Algorithms

2017 ◽  
Vol 25 (2) ◽  
pp. 237-274 ◽  
Author(s):  
Dirk Sudholt
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Building Block ◽  
Broad Class ◽  
Statistical Tests ◽  
Building Blocks ◽  
Fundamental Question ◽  
Royal Road ◽  
Crossover Experiments

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

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