Open AccessJournal Article
Multiobjective Programming With Continuous Genetic Algorithm
TL;DR: This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraints and penalty function for constrained one.
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Abstract: Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination.
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Citations
Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation
TL;DR: In this paper , the authors proposed an accelerated GA for energy-efficient VM placement in large-scale data centers, where the most time-consuming element of the GA is the calculation of its fitness function.
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Optimization Assisted by Neural Network-Based Machine Learning in Electromagnetic Applications
TL;DR: In this article , an optimization assisted by a neural network (ONN) predictor is introduced to the electromagnetic community for antenna design problems, where the objective function is approximated using a non-linear approximator.
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Three-Objective Programming with Continuous Variable Genetic Algorithm
TL;DR: A continuous variable genetic algorithm is used to find approximate near optimal solution set for linear and nonlinear three objective optimization problems and Objective functions are considered as fitness function without modification.
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Energy-efficient virtual machine placement in data centres via an accelerated genetic algorithm
Atrin Oroojeni
- 26 Jun 2023
TL;DR: In this paper , the authors accelerate the GA for VM placement through simplified fitness function computation, which can reduce execution time and save energy for data centres, and outperforms the standard GA and First Fit Decreasing (FFD) algorithm.
1
References
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Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
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An Introduction to Genetic Algorithms.
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
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A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
Kalyanmoy Deb,Samir Agrawal,Amrit Pratap,T. Meyarivan +3 more
- 18 Sep 2000
TL;DR: Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.
An overview of evolutionary algorithms in multiobjective optimization
TL;DR: Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.
The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation
Joshua Knowles,David Corne +1 more
- 06 Jul 1999
TL;DR: It is argued that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set, and is intended as a good baseline approach against which more involved methods may be compared.
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