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A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective
Blast Furnace Optimization Problem
Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu
Chugh, T., Chakraborti, N., Sindhya, K., & Jin, Y. (2017). A Data-Driven Surrogate-
Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace
Optimization Problem. Materials and Manufacturing Processes, 32(10), 1172-1178.
https://doi.org/10.1080/10426914.2016.1269923
2017
1
A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-
Objective Blast Furnace Optimization Problem
Tinkle Chugh
1
, Nirupam Chakraborti
2
, Karthik Sindhya
1
, Yaochu Jin
1,3
1
Faculty of Information Technology, University of Jyväskylä, Finland
2
Department of
Metallurgical & Materials Engineering, Indian Institute of Technology, Kharagpur, India
3
Department of Computer Science, University of Surrey, Guildford, United Kingdom
Corresponding author E-mail: tinkle.chugh@jyu.fi
Received 08 Sep 2016 Accepted 12 Nov 2016
Abstract
A new data-driven reference vector guided evolutionary algorithm has been successfully
implemented to construct surrogate models for various objectives pertinent to an
industrial blast furnace. A total of eight objectives have been modeled using the
operational data of the furnace using twelve process variables identified through a
principal component analysis and optimized simultaneously. The capability of this
algorithm to handle a large number of objectives, which has been lacking earlier, results
in a more efficient setting of the operational parameters of the furnace, leading to a
precisely optimized hot metal production process.
KEYWORDS: blast furnace, ironmaking, metamodeling, multi-objective optimization,
model management, data-driven optimization, Pareto optimality
1. INTRODUCTION
Iron blast furnace is an immensely complex reactor and running it in an optimized
fashion is a very complex task
[1]
. Although analytical models exist for this type of
2
reactors that produces hot metal
[2]
, such models are often quite cumbersome and of
limited applicability in a real-life industrial scenario. In addition, a complete
understanding of the blast furnace process involves handling several objectives together,
which so far has been only marginally successful
[3]
. Thus, it is extremely complex, if not
impossible, to build a simulator for blast furnace optimization and one has to rely upon
limited amount of noisy data collected in daily operations to perform optimization.
Another challenge in optimization of blast furnaces is that it involves multiple conflicting
objectives, which is often known as multiobjective optimization
[4]
. The evolutionary
algorithms have been widely used to solve multiobjective optimization problems
[5]
.
However, the efficacy of most multiobjective evolutionary algorithms deteriorates as the
number of objectives becomes more than four
[4]
, which makes them less suited for blast
furnace optimization. Fortunately, many-objective optimization to solve problems with
more than three objectives, has received increasing attention recently and many
evolutionary algorithms have been developed for such problems
[3,6]
.
Purely data-driven evolutionary optimization has received little attention with few
exceptions. Most recently, Wang et al.
[7]
have also categorized data-driven optimization
into two types: on-line and off-line. In on-line optimization, small amount of new data is
available during the optimization while in off-line optimization, no extra data other than
those in hands is available. The authors have also proposed a surrogate-based data-driven
approach, capable of optimizing a trauma system involving two conflicting objectives in
an evolutionary way. Although trauma system optimization belongs to offline data-driven
3
optimization
[7]
, there are a large amount of data available. By contrast, as indicated by
Guo et al.
[8]
, off-line optimization becomes extremely challenging, when amount of
historical data is small and noisy. Unfortunately, blast furnace optimization that is being
studied here requires off-line optimization where a very limited amount of data is
available.
Data-driven evolutionary optimization when conducted off-line with a small amount of
information must address the following two major challenges. First, how to construct a
reliable surrogate model based on the limited amount of data and how to manage the
surrogates without a true objective function, which are two most important questions in
surrogate assisted evolutionary optimization
[9]
. Second, how to handle the several
objectives simultaneously, in order to efficiently obtain a set of representative Pareto
optimal solutions.
Many real-world complex problems do not have any analytical functions or simulation
model, and optimal solutions can only be obtained based on the available data. Moreover,
collecting the data is usually very expensive and may involve a higher-level information,
e.g. from a decision maker. In such cases, getting incremental data or online data can be
cumbersome and expensive. Therefore, surrogates are built for the limited amount of
offline data to generate the Pareto optimal solutions. In addition, solutions obtained via
the offline approach can further be used to generate more incremental or online data
based on the performance of the algorithm.
4
This article presents an application of a new off-line data driven evolutionary many-
objective optimization algorithm to blast furnace optimization to address the above
mentioned two main challenges in off-line data-driven optimization. To this end, a new
surrogate management strategy is incorporated in a recently proposed surrogate assisted
many-objective evolutionary algorithm. Details of the algorithm will be presented below.
2. METHOD
2.1 The Strategy For Implementingpareto Optimality For Many Objectives
In many problems, like the blast furnace problem studied during the present investigation,
optimization leads to set of multiple optimal solutions, each representing its own trade-
off between the objectives. This set of solutions is known as a Pareto optimal solution
and locus connecting them constitutes the Pareto front
[10]
. Evolutionary multiobjective
optimization (EMO) algorithms that imitate the evolution process in nature to evolve a
population of candidate solutions to generate a representative set of Pareto optimal
solutions are commonly used for this purpose
[11]
. But the efficacy of most evolutionary
multiobjective optimization algorithms, judged in terms of their ability to generate a
diverse set of representative Pareto optimal solutions, in general, is limited to problems
with up to two or three objectives
[9]
.
In many practical problems ranging from aircraft design
[12]
to molecular design
[13]
the
number of objectives often exceeds three. Such optimization problems in the literature
are referred to as many-objective optimization problems
[6]
. Traditional EMO algorithms
developed cannot be simply used to solve problems with many-objectives due to
