Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants

Mehra, Molly; Jayalal, M.L.; Arul, A. John; Rajeswari, S.; Kuriakose, K. K.; Murty, S.A.V. Satya

doi:10.5815/ijisa.2014.01.03

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…If there are nutrients that have a number less than a predetermined number, then the number of nutrient deficiencies is used as a penalty value. This penalty technique is used to penalize the ISSN: 2089-3272  Fodder composition optimization using modified genetic algorithm (Vivi Nur Wijayaningrum) 71 infeasible solutions, which depends on the number of violation of the constraints that occur [24]. Fitness function can be calculated using (1) [8].…”

Section: Fitness Functionmentioning

confidence: 99%

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Fodder composition optimization using modified genetic algorithm

Wijayaningrum

Mahmudy

2019

IJEEI

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Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

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Section: Fitness Functionmentioning

confidence: 99%

“…The selection process is done by comparing each chromosome in a population based on its fitness value. This process is used to select individuals used for the reproduction process in the next generation in order to generate new search areas [24].…”

Section: Selectionmentioning

confidence: 99%

Fodder composition optimization using modified genetic algorithm

Wijayaningrum

Mahmudy

2019

IJEEI

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“…In this paper, we have used two crossovers i.e. k-point crossover and Discrete TPX [8]. To provide multiple combinations of selected parents it selects more than one crossover points.…”

Section: A Crossover Operatorsmentioning

confidence: 99%

Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

Umbarkar¹,

Moon²,

Sheth³

2018

IJIEEB

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Evolutionary Algorithms (EAs) are found to be effective for solving a large variety of optimization problems. In this Paper Dual Population Genetic Algorithm (DPGA) is used for solving the test functions of Congress on Evolutionary Computation 2013 (CEC'2013), by using two different crossovers. Dual Population Genetic Algorithm is found to be better in performance than traditional Genetic Algorithm. It is also able to solve the problem of premature convergence and diversity of the population in genetic algorithm. This paper proposes Dual Population Genetic Algorithm for solving the problem regarding unconstrained optimization. Dual Population Genetic Algorithm is used as metaheuristic which is verified against 28 functions from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2013 on unconstrained set of benchmark functions using two different crossovers. The results of both the crossovers are compared with each other. The results of both the crossovers are also compared with the existing results of Standard Particle Swarm Optimization algorithm. The Experimental results showed that the algorithm found to be better for finding the solution of multimodal functions of the problem set.

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“…P r denotes the scalar penalty mu ltiplier and is typically increased as the optimizat ion goes on to put more and mo re emphasis on avoiding constraint violations [78]. Metaheuristic algorith ms, that include GA , are rapidly beco ming the most used methods of choice for some intractable systems [79][80][81].…”

Section: Optimization Of T He O2fo-pid Controllermentioning

confidence: 99%

Review, Design, Optimization and Stability Analysis of Fractional-Order PID Controller

Soukkou¹,

BELHOUR²,

Leulmi³

2016

IJISA

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This paper will establish the importance and significance of studying the fractional-order control of nonlinear dynamical systems. The foundation and the sources related to this research scope is going to be set. Then, the paper incorporates a brief overview on how this study is performed and p resent the organization of this study. The present work investigates the effectiveness of the physical-fractional and biological-genetic operators to develop an Optimal Form of Fract ional-order PID Controller (O2Fo-PIDC). The newly developed Fo-PIDC with optimal structure and parameters can, also, imp rove the performances required in the modeling and control of modern manufacturing-industrial process (MIP). The synthesis methodology of the proposed O2Fo-PIDC can be viewed as a mu lti-level design approach. The hierarchical Multiobject ive genetic algorith m (M GA), adopted in this work, can be visualized as a comb ination of structural and parametric genes of a controller orchestrated in a h ierarch ical fashion. Then, it is applied to select an optimal structure and knowledge base of the developed fractional controller to satisfy the various design specification contradictories (simplicity, accuracy, stability and robustness).

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Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants

Cited by 12 publications

References 5 publications

Fodder composition optimization using modified genetic algorithm

Fodder composition optimization using modified genetic algorithm

Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

Review, Design, Optimization and Stability Analysis of Fractional-Order PID Controller

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