A new multi-objective genetic algorithm applied to hot-rolling process

Chakraborti, Nirupam; Kumar, B. Siva; Babu, V. Satish; Moitra, S.; Mukhopadhyay, Abhiroop

doi:10.1016/j.apm.2007.06.011

Cited by 37 publications

(21 citation statements)

References 26 publications

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“…It is worth noting that some literatures (Zhang and Mahfouf, 2006;Chakraborti et al, 2008) also proposed the methods of splitting the functional (objective) space. For example, in (Chakraborti et al, 2008), a multi-objective optimisation genetic algorithm was developed using a neighbourhood concept. It splits the functional space into discrete grids and each candidate solution is mapped to one grid.…”

Section: Basic Ideas Behind Rssamentioning

confidence: 99%

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

Zhang

Mahfouf

2010

Engineering Applications of Artificial Intelligence

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a b s t r a c tIn this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a 'real-life' problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single-objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of 'right-firsttime production' of metals.

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Section: Basic Ideas Behind Rssamentioning

confidence: 99%

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

Zhang

Mahfouf

2010

Engineering Applications of Artificial Intelligence

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“…This strategy could be helpful in progressing towards the true Pareto optimal front when solving difficult multiobjective optimization problems. Very recently, Chakraborti et al [37] used a rank based population sizing for preserving diversity. The new population in every generation were formed by members from different dominance ranks, which was in the similar way as that used in controlled NSGA II.…”

Section: Fitness Evaluationmentioning

confidence: 99%

A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic

et al. 2008

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“…The New multiobjective genetic algorithm (NMGA) was proposed recently [14,15] in connection with some research related to the steel industry. Still an algorithm for the bi-objective optimization, it is potentially expandable, at least to the three objective situations.…”

Section: 12mentioning

confidence: 99%

“…This is done through some datadriven models constructed using an emerging technique of evolutionary neural networks [9], multiobjective genetic algorithms [10,11], and Principal Component Analysis (PCA) [12] on which various data mining strategies could be based. Next, an optimization study has been carried out where the probability of formation of a number of AB 2 phases has been examined through some recently proposed evolutionary algorithms [13][14][15]. The phase identification strategy is presented first, followed by the optimization procedure.…”

Section: Introductionmentioning

confidence: 99%

Identification and Optimization of AB₂Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

Agarwal

Pettersson

Singh

et al. 2009

Materials and Manufacturing Processes

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Available data for a large number of AB 2 compounds were subjected to a rigorous study using a combination of Principal Component Analysis (PCA) technique, multiobjective genetic algorithms, and neural networks that evolved through genetic algorithms. The identification of various phases and phase-groups were very successfully done using a decision tree approach. Since the variable hyperspaces for the different phases were highly intersecting in nature, a cumulative probability index was defined for the formation of individual compounds, which was maximized along with Pauling's electronegativity difference. The resulting Pareto-frontiers provided further insight into the nature of bonding prevailing in these compounds.

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A new multi-objective genetic algorithm applied to hot-rolling process

Cited by 37 publications

References 26 publications

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic

Identification and Optimization of AB₂Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

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A new multi-objective genetic algorithm applied to hot-rolling process

Cited by 37 publications

References 26 publications

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic

Identification and Optimization of AB2Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

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Identification and Optimization of AB₂Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms