Genetic Algorithm with Dynamic Selection Based on Quadratic Ranking Applied to Induction Machine Parameters Estimation

Boudissa, El Ghalia; Bounekhla, M.

doi:10.1080/15325008.2012.682246

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…In Table 2 the values of and are illustrated. is calculated by (18), (16) and is the obtained solution of the problem defined by (15). The optimal solutions are deduced using the reducing transformation (17) where .…”

Section: Benchmark Functionsmentioning

confidence: 99%

“…Where 0.34668, which is calculated by (17) and (18). And: It has used a step With the develop program, it find the optimal solution of the problem defined by (15), . Then using (17), the estimated parameters are deduced.…”

Section: Simulated Datamentioning

confidence: 99%

“…An improved PSO called Systematic Mutation based Particle Swarm Optimization [14] is applied to parameter estimation and efficiency determination, so that parameter variation effect is taken into account. A dynamic real-coded genetic algorithm is applied in [15] to estimate the both of electrical and mechanical parameters.…”

Section: Introductionmentioning

confidence: 99%

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Alienor method applied to induction machine parameters identification

Khemici¹,

Bounekhla²,

Boudissa³

2020

IJECE

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<p>This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time.</p>

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Section: Benchmark Functionsmentioning

confidence: 99%

Section: Simulated Datamentioning

confidence: 99%

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Alienor method applied to induction machine parameters identification

Khemici¹,

Bounekhla²,

Boudissa³

2020

IJECE

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show abstract

“…The major methods developed for different stages of the genetic algorithm include: binary method [10], continuous method [11], hybrid method [12], variable length method [13], and multidimensional method [14] for the population generation stage; single-point crossover [15], multi-point crossover [16], heuristic method, and hybrid method [17] for the crossover operation; direct adaptive mutation [18] and power mutation [19] for the mutation operation; single-objective approach [20] and multi-objective approach [21] for the evaluation process; and tournament method [22], roulette wheel methods [23], and rank/ merit based methods [24] for the selection operation. In fact, the choice of the method to be used at each stage of the genetic algorithm has a significant impact on its efficiency and effectiveness and how well it can converge to the optimal solution with the least amount of computation [25].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Madhkhan

Baradaran²

2018

Period. Polytech. Civil Eng.

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Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language.The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.

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“…And it is difficult for traditional rank-based model to make the selection probabilities of individuals adaptively changed along with evolution process. So some research works have been devoted to the improvement of traditional rank-based selection for these years [9,10]. In this paper, based on the mathematical concept of interpolation method, we introduce interpolating rank-based selection with pressure and its relevant formulas.…”

Section: Interpolating Rank-based Selection With Pressurementioning

confidence: 99%

A New Hybrid PSO-based Genetic Algorithm and its Application to Layout Problems

Zhao¹,

Guang-qiang²,

Du³

et al. 2014

TOAUTOCJ

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Layout problems belong to NP-Complete problems theoretically. They are concerned more and more in recent years and arise in a variety of application fields such as the layout design of spacecraft modules, plant equipments, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are relatively effective to solve these kind of problems. But usually there still exist two main defects, i.e. premature convergence and slow convergence rate. To overcome them, a new improved hybrid PSO-based genetic algorithm (HPSO-GA) is proposed on the basis of parallel genetic algorithms (PGA). In this algorithm, chaos initialization, hybrid strategy and multisubpopulation evolution based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure can prevent the algorithm from premature in the early stage and benefit accelerating convergence in the late stage as well. And more importantly, in accordance with characteristics of different classes of subpopulations, different modes of PSO update operator are introduced. It aims at making full use of the fast convergence property of particle swarm optimization (PSO). An example of layout problems shows that HPSO-GA is feasible and effective.

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Genetic Algorithm with Dynamic Selection Based on Quadratic Ranking Applied to Induction Machine Parameters Estimation

Cited by 16 publications

References 15 publications

Alienor method applied to induction machine parameters identification

Alienor method applied to induction machine parameters identification

Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

A New Hybrid PSO-based Genetic Algorithm and its Application to Layout Problems

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