Multiobjective Location of Automatic Voltage Regulators in a Radial Distribution Network Using a Micro Genetic Algorithm

Mendoza, Jorge E.; Morales, Dario; Lopez, Rigoberto; López, E.; Vannier, Jean-Claude; Coello, Carlos A. Coello

doi:10.1109/tpwrs.2006.887963

Cited by 84 publications

(39 citation statements)

References 28 publications

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“…It should be noted unlike the traditional GA, mGA has a smaller population size, and it uses a special strategy to preserve diversity [1]. On top of the normal mutation operation, a re-start strategy is utilised to introduce diversity in the mGA population overtime [17], [37], [36]. On the other hand, MmGA is derived to improve its convergence capability towards pf true without sacrificing the salient properties of mGA.…”

Section: Modified Micro Genetic Algorithm (Mmga)mentioning

confidence: 99%

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

et al. 2017

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Application of an Evolutionary Algorithm-based Ensemble Model to Job-Shop SchedulingChoo Jun TAN, Siew Chin NEOH, Chee Peng LIM, Samer HANOUN, Wai Peng WONG, Chu Kong LOO, Li ZHANG, Saeid NAHAVANDI Abstract In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.

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Section: Modified Micro Genetic Algorithm (Mmga)mentioning

confidence: 99%

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

et al. 2017

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“…Over the years, other authors have adopted micro-genetic algorithms for solving a variety of problems (see for example [5,30,46,47,61,72,76,77]). Additionally, the use of very small population sizes has also been attempted with other bio-inspired metaheuristics, such as particle swarm optimization (see [22]).…”

Section: Use Of Very Small Population Sizesmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges

Coello

2014

Computational Methods in Applied Sciences

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This chapter provides a short overview of the most significant research work that has been conducted regarding the solution of computationally expensive multi-objective optimization problems. The approaches that are briefly discussed include problem approximation, function approximation (i.e., surrogates) and evolutionary approximation (i.e., clustering and fitness inheritance). Additionally, the use of alternative approaches such as cultural algorithms, small population sizes and hybrids that use a few solutions (generated with optimizers that sacrifice diversity for the sake of a faster convergence) to reconstruct the Pareto front with powerful local search engines are also briefly discussed. In the final part of the chapter, some topics that (from the author's perspective) deserve more research, are provided.

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“…This algorithm works with a population of 4 individuals and uses a secondary memory that stores potential solutions throughout the search. This approach was widely used to successfully solve various engineering problems as discussed in [11] and [12].…”

Section: A Micro Artificial Immune Systemmentioning

confidence: 99%

A Micro Artificial Immune System

Lozada¹,

Calvo²,

Taud³

2011

Polibits

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Abstract-In this paper, we present a new algorithm, namely, a micro artificial immune system (Micro-AIS) based on the Clonal Selection Theory for solving numerical optimization problems. For our study, we consider the algorithm CLONALG, a widely used artificial immune system. During the process of cloning, CLONALG greatly increases the size of its population. We propose a version with reduced population. Our hypothesis is that reducing the number of individuals in a population will decrease the number of evaluations of the objective function, increasing the speed of convergence and reducing the use of data memory. Our proposal uses a population of 5 individuals (antibodies), from which only 15 clones are obtained. In the maturation stage of the clones, two simple and fast mutation operators are used in a nominal convergence that works together with a reinitialization process to preserve the diversity. To validate our algorithm, we use a set of test functions taken from the specialized literature to compare our approach with the standard version of CLONALG. The same method can be applied in many other problems, for example, in text processing.

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Multiobjective Location of Automatic Voltage Regulators in a Radial Distribution Network Using a Micro Genetic Algorithm

Cited by 84 publications

References 28 publications

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges

A Micro Artificial Immune System

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