Optimization of Complex Industrial Processes
Using Constrained Genetic Algorithms
Hamza Turabieh
Information Technology Department
AlBalqa Applied University
AlSalt,Jordan
H
turabieh@yahoo.com
Alaa Sheta
Information Technology Department
AlBalqa Applied University
AlSalt,Jordan
asheta2@yahoo.com
ABSTRACT
Tuning the parameters of complex industrial processes to provide an outstanding product with minimum
losses represents a challenge for optimization theory.Many algorithms were proposed in the past to handle
such type of optimization problems.In this paper we explore the use of Evolutionary Computation tech
niques to handle such a problem.We focus on the use of Genetic Algorithms (GAs) in solving constrained
optimization problems for industrial processes.On doing this,we plan to explore the sensitivity of the
evolutionary process with respect to variations in the tuning parameters of the GAs (i.e.population size).
A careful tuning for the evolutionary process parameters lead to fast convergence to the optimal solutions.
Number of test cases and a reactor network design problem are solved.
Key Words
:Industrial Processes,Genetic Algorithms,Constrained Optimization
1 Introduction
Most industrial manufacturing processes involve
dynamic nonlinearity,uncertainty and constraints.
Currently there is a growing interest on using Evo
lutionary Algorithms (EAs) to assist providing a
reasonable solution for physical nonlinear systems
in industry.EAs techniques are among those opti
mization techniques which have been used to solve
a variety of optimization problems in industry.EA
include Genetic Algorithms (GAs) [1],Evolutionary
strategies (ESs) [2],Evolutionary Programming [3]
and Genetic Programming (GP) [4].In [5,6],Ge
netic Algorithms and Evolutionary strategies have
been used in the parameter identiﬁcation process
of nonlinear systems with various degrees of com
plexity.In [7],GAs have been successfully used
to provide an automatic methodology for gener
ating model structure for nonlinear systems based
the Volterra timeseries and leastsquare estimation
(LSE) was used to identify the model parameters.
Using this methodology an eﬃcient model structure
was built to model the dynamics of the automotive
engine.Modeling the dynamics of a winding ma
chine in industry using genetic programming was
presented in [8].
For any optimization problem,there is an opti
mization criterion (i.e.evaluation function) has to
be minimized or maximized.The evaluation func
tion represents a measure of the quality of the de
veloped solution.Searching the space of all possi
ble solution is a challenging task.Additional con
straints of the domain of search for the parameters
makes the problem quite diﬃcult.The constraints
might aﬀect the performance of the evolutionary
process since some of the produced solutions (i.e
individuals) may be unfeasible.Unfeasible solution
represents a wast of computation eﬀort.
Although there is no general methodology to
handle constraints several methods were introduced
[9,10,11].Evolutionary Strategies and Evolution
ary Programming were modiﬁed to handle numer
ical optimization problems with constraints simply
by rejecting unfeasible individuals.Genetic Algo
rithms used an alternative approach that is penal
izing the unfeasible individuals.Unfortunately is
there is no general adopted strategy to design the
penalty functions [9,10].In the following sections,
we will give an overview of GAs,formulate the con
strained optimization problem using GAs and ﬁ
nally provide number of case studies in industry.
2 Why Genetic Algorithms?
GAs are the most famous among EA algorithms.
GAs have been employed as a tool that can han
dle multimodel function and complex search space.
They have the capability to search complex spaces
with high probability of success in ﬁnding the
points of minimumor maximumon the search space
(i.e.landscape).Genetic Algorithms (GAs) are
derivativefree stochastic search algorithms.
GAs apply the concept of natural selection.This
idea was ﬁrst introduced by John Holland at the
University of Michigan in 1975 [1].GAs have been
successful used in solving numerous applications in
engineering and computer science [12,13,14,15].
GAs gain a great popularity due to their known
attributes.These attributes include:
• GAs can handle both continuous and dis
crete optimization problems.They require no
derivative information about the ﬁtness crite
rion [16,17].
• GAs have the advantageous over other search
algorithm since it is less likely to be trapped
by local minimum.
• GAs provide a more optimal and global so
lution.They are less likely to be trapped by
local optimal like Newton or gradient descent
methods [18,19].
• GAs have been shown to be less sensitive to
the presence of noise and uncertainty in mea
surements [5,20].
• GAs use probabilistic operators (i.e.crossover
and mutation) not deterministic ones.
3 How GAs Code a Solution?
Genetic algorithms code the candidate solutions of
an optimization algorithm as a string of characters
which are usually binary digits [1].In accordance
with the terminology that is borrowed fromthe ﬁeld
of genetics,this bit string is usually called a chro
mosome (i.e.individuals).
A number of chromosomes generate what is
called a population.The structure for each indi
vidual can be represented as follows:
gene
1
gene
2
......
gene
n
11101
00101
......
11011
This a chromosome has number of genes equal to
n.These genes are used in the evaluation function
f.Thus,f(gene
1
,gene
2
,...,gene
n
) is the function
to be minimized or maximized.
4 Evolutionary Process
The evolutionary process of GAs start by the com
putation of the ﬁtness of the each individual in the
initial population.While stopping criterion is not
yet reached we do the following:
• Select individual for reproduction using some
selection mechanisms (i.e.tournament,rank,
etc).
• Create an oﬀspring using crossover and mu
tation operators.The probability of crossover
and mutation is selected based on the appli
cation.
• Compute the new generation of GAs.This
process will end either when the optimal so
lution is found or the maximum number of
generations is reached.
A ﬂowchart for a simple GA process is given [21]
in Figure 1.
4.1 Selection Mechanism
Selection is the process which guides the evolution
ary algorithm to the optimal solution by preferring
chromosomes with high ﬁtness.The chromosomes
evolve through successive iterations,called genera
tions.During each generation,the chromosomes are
evaluated,using some measure of ﬁtness.To cre
ate the next generation,new chromosomes,called
4.4 Fitness Function
GA evaluates the individuals in the population us
ing a selected ﬁtness function (criterion).This func
tion indicates how good or bad a candidate solution
is.The way to select the ﬁtness function is a very
important issue in the design of genetic algorithms,
since the solution of the optimization problem and
the performance of the algorithm count mainly on
this function.
It is important to recognize that GAs is diﬀer
ent from other optimization techniques like gradi
ent descent,since they evaluate a set of solution
in the population at each generation,makes them
more likely to ﬁnd the optimum solution.
The ﬁtness of the individuals within the popula
tion is evaluated,and new individuals are generated
for the next generation using a selection mechanism.
Although convergence to a global optimum is not
guaranteed in many cases,these populationbased
approaches are much less likely to converge to lo
cal optimal and are quite robust in the presence of
noise [16,17].
4.5 Genetic Algorithms Summary
Assume that Pop(k) and Oﬀspring(k) are the par
ents and oﬀspring in current generation t;the gen
eral structure of a genetic algorithm procedure can
be described by the simple C code.
begin
k=0;
initialize Pop(k);
evaluate Pop(k);
while (termination not reached) do
recombine Pop(k) to generate Oﬀspring(k);
evaluate Oﬀspring;
Select Pop(k + 1) from Pop(k) and
Oﬀspring(k);
k = k +1
end while
end
5 Optimization Problem
A constrained optimization problem can be pre
sented as follows:
Optimize f(X) (1)
Subject to:
g
j
(X) ≤ 0,∀j = 1,...,q
h
j
(X) = 0,∀j = q +1,...,m
where X = (x
1
,...,x
n
) represents the array of sys
tem variables.The search space S for the above
problemis split into two domains.They are the fea
sible S
f
and the unfeasible space S
unf
.The func
tion variables are deﬁned is a speciﬁc domain de
ﬁned as:
l(i) ≤ x
i
≤ u(i),1 ≤ i ≤ n (2)
The feasible S
f
set is deﬁned by a number of addi
tional m constraints (i.e.g
j
(X),h
j
(X)).
6 Constrained Handling
In [24],Michalewicz and Attia presented a method
ology to deal with the unfeasible individual.This
methodology can be described in the following
steps:
• The problem constraints can be classiﬁed into
four types.There are linear equalities (LE),
linear inequalities (LI),nonlinear equalities
(NE),nonlinear inequalities (NI) constraints.
• A random start point is selected for the
search.This initial random point should sat
isfy both LE and LI constraints.
• Set the initial temperature λ = λ
0
.
• Evaluate each individual in the population us
ing the evaluation function eval.
eval(X,λ) = f(X) +
1
2λ
m
j=1
f
2
j
(X),(3)
• if λ < λ
f
stop,else
– decrease λ.
– use the best individual as an initial solu
tion for the next generation.
– repeat the previous steps of the algo
rithms.
This method requires an initial starting temper
ature λ
0
and a ﬁnal freezing temperature λ
f
.A
recommended values are,reported in [24],λ
0
= 1,
λ
i+1
= 0.1 ×λ
i
with λ
f
= 10
−6
.
7 Constrained Optimization Software
Solving constrained optimization problem was ex
plored by Michalewicz and others [9,25,10,24].
To develop our results we used the GENOCOP 5.0
software tool which was provided in [26,27].To run
the GENOCOP software we need to specify a set of
variables in an input ﬁle.These variables include
the number of variables,the number of equalities,
the number of inequalities,the domains speciﬁed
for each variable.We also specify the population
size and the total number of generations.
The proposed solution of the constrained opti
mization problem was compared with the Sequen
tial Quadratic Programming (SQP) [28] solution.
We used the Optimization Toolbox with Matlab
to develop a solution for the cases under study based
SQP.The sequential quadratic programming (SQP)
algorithm is a powerful technique for solving non
linear constrained optimization problems [29].
SQP allows you to closely mimic Newton’s
method for constrained optimization just as is done
for unconstrained optimization.At each major it
eration,an approximation is made of the Hessian of
the Lagrangian function using a quasiNewton up
dating method.This is then used to generate a QP
subproblem whose solution is used to form a search
direction for a line search procedure [30,31].
8 Test Problem 1
A nonlinear constrained optimization problem de
scribed in [32] and extensively discussed in [33,34,
11,35] is presented in this section.
Min φ(x,y) = 2x +y (4)
Subject to:
1.25 −x
2
−y ≤ 0
x +y ≤ 1.6
Given that 0 ≤ x ≤ 1.6 and y ∈ {0,1}.To optimize
the above function,we generated the problem sur
face (i.e.landscape) deﬁned within the given search
space.The landscape is shown in Figure 4.To
check the performance of the evolutionary process
we changed the population size number of time.
Our goal is to do some sensitivity analysis to
show that GAs will converge every time we change
Table 1
Solution provide by GAs and SQP:Case 1
x
y
φ(x,y)
Technique
0.5
1
2
GAs
0.5
1
2
SQP
the population size.In Figure 5,we show the con
vergence of the evolutionary process with various
population size.It can be seen that with various
population size the optimal value of the function
reached the acceptable level.
A comparison between the developed results us
ing the constrained GAs and Matlab Optimization
Toolbox is provided in Table 1.The results show
that GAs can provide the same results as the SQP
technique.This means that both techniques are ef
fective in this case.
9 Test Problem 2
This problem was presented in [36] and was studied
in [11,35,25].
Min φ(x
1
,x
2
,y) = −y +2x
1
+x
2
(5)
Subject to:
x
1
−2e
−x
2
= 0
−x
1
+x
2
+y ≤ 0
Given that 0.5 ≤ x
1
≤ 1.4 and y ∈ {0,1}.
The above problem can be formulated to eliminate
equality constraints as shown in Equation 6.
Min φ(x
1
,y) = −y +2x
1
−ln(
x
1
2
) (6)
Subject to:
−x
1
−ln(
x
1
2
) +y ≤ 0,y ∈ {0,1}
In this section,we explore the issue of selecting
the optimal tuning parameters for the second test
case using genetic algorithms with constraints.The
problem landscape is presented in Figure 6.The
landscape seems not very complex but the domain
of search space for each model parameters repre
sents a challenge since we are having nonlinear con
straints.
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
0
0.5
1
1.5
2
2.5
3
x
Search Space
y
function value
Fig.4.Search space of Test Problem 1
10
20
30
40
50
60
70
2
2.05
2.1
2.15
2.2
2.25
2.3
2.35
2.4
Generation
Fitness
Best so far curves with various population sizes
population size= 30
population size= 40
population size= 50
population size= 60
population size= 80
population size= 100
Fig.5.Test Problem 1:Convergence of the evolutionary process with various population sizes
0.4
0.6
0.8
1
1.2
1.
4
0
0.2
0.4
0.6
0.8
1
1
1.5
2
2.5
3
3.5
x
1
Search Space
y
function value
Fig.6.Search space of Test Problem 2
5
10
15
20
25
2.15
2.2
2.25
2.3
2.35
2.4
Generation
Fitness
Best so far curves with various population sizes
population size= 30
population size= 40
population size= 50
population size= 60
population size= 80
population size= 100
Fig.7.Test Problem 2:Convergence of the evolutionary process with various population sizes
Table 2
Solution provide by GAs and SQP:Case 2
x
1
x
2
φ(x
1
,x
2
)
Technique
1.375
1
2.124
GAs
1.3748
1
2.12452
SQP
In Figure 7,we show the best so far curves of
the GAs with various population sizes.In Table 2,
the GAs provided a slightly better results than the
SQP technique.
10 Reactor Network Design Problem
The reactor network consist of two CSTR reactors
where the a sequence of reaction A,B,then C takes
place.The design problem objective is to maximize
the concentration of product B in the exit stream.
This can be achieved by ﬁnding the optimal value
of the states x
1
,x
2
,x
3
,x
4
,x
5
and x
6
.The optimiza
tion problem can be represented mathematically as
given in Equation 7.The problem under study was
fully described in [34].
Min φ = −x
4
(7)
Subject to:
x
1
+k
1
x
2
x
5
= 1
x
2
−x
1
+k
2
x
2
x
6
= 0
x
3
+x
1
+k
3
x
3
x
5
= 1
x
4
−x
3
+x
2
−x
1
+k
4
x
4
x
6
= 0
x
0.5
5
+x
0.5
6
≤ 4
The domain of search for the states x
1
,x
2
,x
3
,x
4
,x
5
and x
6
are given as follows.0 ≤ x
1
≤ 1,0 ≤ x
2
≤ 1,
0 ≤ x
3
≤ 1,0 ≤ x
4
≤ 1,10
−5
≤ x
5
≤ 16,10
−5
≤
x
6
≤ 16.The values of the coeﬃcient k
1
,k
2
,k
3
and
k
4
are given as:
k
1
= 0.09755988
k
2
= 0.99k
1
k
3
= 0.0391908
k
4
= 0.9k
3
To deal with the above problem,we decide to trans
fer the problemto a maximization problemby elim
inating the equality constraints.The new math
ematical description can be given as in Equation
Table 3
Solution provide by GAs and SQP:Reactor
Network Problem
x
5
x
6
φ(x
5
,x
6
)
Technique
3.038
5.096
0.3881
GAs
15.975
1e005
0.3746
SQP
8.with the boundary values of x
5
and x
6
are
10
−5
≤ x
5
≤ 16,10
−5
≤ x
6
≤ 16.
Max φ =
k
2
x
6
(1 +k
3
) +k
1
(1 +k
2
x
6
)
(1 +k
1
x
5
)(1 +k
2
x
6
)(1 +k
3
x
5
)(1 +k
4
x
6
)
(8)
Subject to:
x
0.5
5
+x
0.5
6
≤ 4
We ran GA with population sizes 30,40,50,60,80
and 100 and computed the best solution after each
generation.The landscape for the network design
problem is shown in Figure 8.The results of each
run is shown in Figure 9.In Figure 10,we show
the network design problemstructure.To maximize
the function φ(x
5
,x
6
),we used both GAs and SQP.
In this case,GAs outperform SQP in providing a
better maximum to the function φ(x
5
,x
6
).This is
shown in Table 3.
11 Conclusions
In this paper,we used Genetic Algorithms (GAs) to
solve constrained optimization problems for number
of processes.We explored the performance of the
evolutionary process under variations in the popu
lation size.The results show that GAs are robust
and can provide optimal solution after each run.
A practical example of an industrial process,the
reactor network design problem,was studied with
promising results.
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0
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0
0.1
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Fig.9.Reactor Network Problem:Convergence of the evolutionary process with various population sizes
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