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Evolutionary Bi-objective Optimization for the Dynamic
Chance-Constrained Knapsack Problem Based on Tail
Bound Objectives
Hirad Assimi 1and Oscar Harper 2and Yue Xie 3and Aneta Neumann 4and Frank Neumann 5
Abstract. Real-world combinatorial optimization problems are
often stochastic and dynamic. Therefore, it is essential to make opti-
mal and reliable decisions with a holistic approach. In this paper, we
consider the dynamic chance-constrained knapsack problem where
the weight of each item is stochastic, the capacity constraint changes
dynamically over time, and the objective is to maximize the total
profit subject to the probability that total weight exceeds the capac-
ity. We make use of prominent tail inequalities such as Chebyshev’s
inequality, and Chernoff bound to approximate the probabilistic con-
straint. Our key contribution is to introduce an additional objective
which estimates the minimal capacity bound for a given stochastic
solution that still meets the chance constraint. This objective helps
to cater for dynamic changes to the stochastic problem. We apply
single- and multi-objective evolutionary algorithms to the problem
and show how bi-objective optimization can help to deal with dy-
namic chance-constrained problems.
1 INTRODUCTION
Many real-world combinatorial optimization problems involve
stochastic as well as dynamic components which are mostly treated
in isolation. However, in order to solve complex real-world problems,
it is essential to treat stochastic and dynamic aspects in a holistic ap-
proach and understand their interactions.
Dynamic components in an optimization problem may change the
objective function, constraints or decision variables over time. The
challenge to tackle a dynamic optimization problem (DOP) is to track
the moving optima when changes occur [21].
Moreover, uncertainty is pervasive in a real-world optimization
problem. The source of uncertainty may involve the nature of data,
measurement errors or lack of knowledge. Ignoring uncertainties in
solving a problem may lead to obtaining suboptimal or infeasible so-
lutions in practice [16].
Chance-constrained programming (CCP) is a powerful tool to
model uncertainty in optimization problems. It transforms an in-
equality constraint into a probabilistic constraint to ensure that the
probability of constraint violation is smaller than a limit predefined
1The University of Adelaide, Australia, email: hirad.assimi@adelaide.edu.au
2The University of Adelaide, Australia, email: os-
car.harper@student.adelaide.edu.au
3The University of Adelaide, Australia, email: yue.xie@adelaide.edu.au
4The University of Adelaide, Australia, email:
aneta.neumann@adelaide.edu.au
5The University of Adelaide, Australia, email:
frank.neumann@adelaide.edu.au
by the decision-maker [3]. CCP has been applied successfully in dif-
ferent domains such as process control, scheduling and supply man-
agement where safety requirements are concerned [11].
Evolutionary algorithms (EAs) have been applied to many combi-
natorial optimization problems and demonstrate a high capability in
solving hard problems, including a wide range of real-world appli-
cations [18, 4]. Multi-objective EAs deal with several (conflicting)
objectives and provide a set of solutions which are non-dominated to
each other with respect to the given objective functions [7, 24, 23].
In addition to solving problems with conflicting objectives, several
studies have indicated that transforming a single-objective optimiza-
tion problem to a multi-objective optimization problem may lead to
obtaining better solutions. This transformation leads to obtaining a
set of non-dominated solutions instead of a single solution. There-
fore, each individual in the Pareto front contains helpful information
which can improve the performance of the algorithm in exploring the
search space [20, 24, 23].
1.1 Related work
EAs are a natural way to deal with DOPs because they are inspired by
nature which is an ever-changing environment [21]. The behaviour
of EAs has been analyzed on a knapsack problem with dynamically
changing constraint [25]. They carried out bi-objective optimization
with respect to the profit and dynamic capacity constraint. They pro-
posed an algorithm to track the moving optimum and showed that
handling the constraint by a bi-objective approach can be beneficial
in obtaining better solutions when the changes occur less frequently.
Their studies have been extended to analyze the EA behaviour depen-
dent on the submodularity ratio of a broad class of problems [26].
Chance-constrained knapsack problem (CCKP) is a variant of the
classical NP-hard deterministic knapsack problem where the weights
and profits can be stochastic [13]. Approximation algorithm in com-
bination with a robust approach, has been applied to CCKP to find
feasible solutions for a simplified knapsack problem [15].
Recently, Xie et al. [27] integrated inequality tails with single and
bi-objective EAs to solve CCKP. To estimate the probability of con-
straint violation, they used popular tail inequalities. They investi-
gated the behaviour of Chebyshevs inequality, and Chernoff bound
for approximation in CCKP. They also carried out bi-objective op-
timization with respect to the profit and probability of constraint
violation when the capacity is static. Doerr et al. [9] have investi-
gated adaptations of classical greedy algorithms for the optimiza-
tion of submodular functions with chance constraints of knapsack
type. They have shown that the adapted greedy algorithms maintain
arXiv:2002.06766v1 [cs.NE] 17 Feb 2020
asymptotically almost the same approximation quality as in the de-
terministic setting when considering uniform distributions with the
same dispersion for the knapsack weights.
1.2 Our Contribution
In this paper, we consider the dynamic chance-constrained knapsack
problem (DCCKP) with dynamically changing constraint. We also
assume that each item in the knapsack problem has an uncertain
weight while the profits are deterministic. For the dynamic compo-
nent, we follow the settings defined in [25]: the knapsack capacity
changes over time every τiterations with a predefined magnitude.
Moreover, for the stochastic component, we follow the approach and
settings proposed in [27] which employs inequality tails to estimate
the violation in probabilistic constraint.
Therefore, the goal in this study is to re-compute a solution of
maximal profit after a dynamic change occurs to the capacity con-
straint, while the total uncertain weight can exceed the capacity with
a small probability. To benefit from the bi-objective optimization, we
cannot directly apply the second objective used in previous studies
because they only considered either dynamic or stochastic aspect of
the optimization problem in isolation for the second objective func-
tion. Therefore, we introduce an objective function which deals with
uncertainties and caters for dynamic aspects of the problems. This
objective evaluates the smallest knapsack capacity bound for which
a solution would not violate the chance constraint. This objective also
can keep a set of non-dominated solutions to be used for tracking the
moving optimum. This objective makes use of tail inequalities such
as Chebyshev’s inequality and Chernoff bounds to approximate the
probabilistic constraint violation.
To solve DCCKPs, we apply a single objective EA, a modified
version of GSEMO [12] and NSGA-II [8], where the last two com-
pute a trade-offs with respect to the profit and the newly introduced
objective for dealing with chance constraints. Our experimental re-
sults show that the bi-objective EAs perform better than the single
objective approaches. Introducing the additional objective function
to the problem helps the bi-objective optimization algorithm to deal
with the constraint changes as it obtains the non dominated solutions
with respect to the objective functions.
The rest of this article is organized as follows. In the next sec-
tion, we define the DCCKP and introduce the two tail inequalities
for quantifying uncertainties. Afterwards, we introduce the objec-
tive function for dealing with DCCKP and develop the bi-objective
model. Next, we report on the behaviour of single objective and bi-
objective baseline EAs in solving DCCKP. We show that bi-objective
optimization with the introduced second objective can obtain better
solutions on a wide range of instances of the DCCKP. Finally, we
finish with some concluding remarks.
2 DYNAMIC CHANCE-CONSTRAINED
KNAPSACK PROBLEM
In this section, we introduce the problem and provide Chebyshev
and Chernoff tail inequalities to estimate the probability of chance
constraint violation in the problem.
2.1 Problem Formulation
The classical knapsack problem can be defined as follows. Given n
items where each item i,1≤i≤nhas a profit piand a weight wi
and a knapsack capacity C. The goal is to find a selection of items of
maximum profit whose weight does not exceed the capacity bound.
A candidate solution is an element x∈ {0,1}nwhere item iis cho-
sen iff xi= 1. In this paper, we consider the stochastic and dynamic
setting for the knapsack problem where each weight is chosen inde-
pendently according to a given probability distribution. Furthermore,
the capacity bound Cchanges dynamically over time.
The search space is {0,1}nand we denote by P(x) = Pn
i=1 pixi
the profit and by W(x) = Pn
i=1 wixithe weight of a solution x. We
investigate the chance-constrained knapsack problem where the goal
is to maximize P(x)under the condition that the probability that the
weight of the solution is at least as high as the capacity is at most α.
Formally, we define this constraint as
Pr [W(x)≥C]≤α
where αis a parameter that upper bounds the probability of exceed-
ing the knapsack capacity (0 <α<1).
Furthermore, the knapsack capacity in our problem is dynamic
and changes over time every τiterations. We call τthe frequency
of changes which denotes after how many iterations a change occurs
in the knapsack capacity with the magnitude of changes raccording
to some probability distributions.
2.2 Tail Bounds
Chebyshev’s inequality tail can determine a bound for a cumulative
distribution function of a random design variable. Chebyshev’s in-
equality requires to know the standard deviation of the design vari-
ables and gives a tighter bound in comparison to the weaker tails such
as Markov’s inequality. Therefore, it can be applied to any distribu-
tion if the expected weight and standard deviation of the involved
random variables are known. The standard Chebyshev inequality is
two-sided and provides tails for upper and lower bounds [2]. As
we are only interested in the probability of exceeding the weight
bound, we use a one-sided Chebyshev inequality which is also known
as Cantelli’s inequality [6]. For brevity, we refer to the one-sided
Chebyshev as Chebyshev’s inequality in this paper.
Theorem 1 (One-sided Chebyshev inequality).Let Xbe an inde-
pendent random variable, and let E(X)denote the expected weight
of X. Further, let σ2
Xbe the variance of X. Then for any λ∈R+,
we have
Pr [(X−E(X)) ≥λ]≤σ2
X
σ2
X+λ2.
Compared to Chebyshev’s inequality, Chernoff bound provides a
sharper tail with an exponential decay behavior. In order to use Cher-
noff bound, it is essential that the random variable is a summation of
independent random variables. Chernoff bound seeks a positive real
number tin order to find the probability where the sum of indepen-
dent random variables exceeds a particular threshold [19]. Therefore,
Chernoff bound for an independent variable Xcan be given as fol-
lows based on theorem 2.3 in [17].
Theorem 2. Let X=Pn
i=1 Xibe the sum of independent random
variables Xi∈[0,1] chosen uniformly at random, and let E(X)be
the expected weight of X. For any t > 0, we have
Pr[X≥(1 + t)E(X)] ≤exp −t2
2 + 2
3tE(X).
2
3 BI-OBJECTIVE OPTIMIZATION FOR
DCCKP
In this section, we introduce a new objective-function to transform
the single-objective optimization problem into a bi-objective opti-
mization problem. We also describe (1+1)-EA and POSDC as base-
line single and bi-objective EAs.
3.1 Bi-objective Model
We redefine DCCKP by introducing a new second objective function
to transform it into a bi-objective optimization problem. Therefore,
we introduce (C∗)as the stochastic bound as our second objective
function. This objective function evaluates the smallest knapsack ca-
pacity for a given solution such that it satisfies the predefined limit
on the chance constraint. Therefore, the fitness f(x)of a solution x
is given as
f(x) = (P(x), C∗(x))
where
C∗(x) = min {C|s.t. Pr[W(x)≥C]≤α}
is the smallest weight bound Csuch that the probability that the
weight W(x)of xis at least Cis at most α. Using this objective
allows to cater for dynamic changes of the weight bound of our prob-
lem. In bi-objective optimization of DCCKP, the goal is to maximize
P(x)and minimize C∗(x). Hence, we have
f(x0)f(x)iff P(x0)≥P(x)∧C∗(x0)≤C∗(x)
for the dominance relation of bi-objective optimization for two solu-
tions namely xand x0. Evaluating the chance constraint is computa-
tionally difficult [1]. It has been shown that even if random variables
are from a Bernoulli distribution, calculating the probability of vio-
lating the constraint exactly is #P-complete, see Theorem 2.1 in [14].
Because it is difficult to compute C∗exactly, we make use of the tail
inequalities to calculate the second objective function. For Cheby-
shev’s inequality, the stochastic bound is given as follows.
Proposition 1 (Chebyshev Constraint Bound Calculation).Let
E(W(x)) be the expected weight, σ2
W(x)be the variance of the
weight of solution xand αbe the probability bound of the chance
constraint. Then setting C∗
1(x) = E(W(x)) + σW(x)√α(1−α)
αim-
plies Pr[W(x)≥C∗
1(x)] ≤α.
Proof. Using Chebyshev’s inequality, we have
Pr [W(x)≥E(W(x)) + λ]≤σ2
W(x)
σ2
W(x)+λ2.
We set C∗
1(x) = E(W(x)) + λwhich implies
λ=σW(x)pα(1 −α)
α.
Hence, we have
Pr[W(x)≥C∗
1(x)]
=Pr "W(x)≥E(W(x) + σW(x)pα(1 −α)
α#
≤σ2
W(x)
σ2
W(x)+σW(x)√α(1−α)
α2
=α
which completes the proof.
We consider wi∈ U[E(wi)−δ, E(wi) + δ]and Pn
i=1 xidenotes
the total number of chosen items in a solution, then the stochastic
bound based on Chebyshev’s inequality for uniform distribution is
given as follows
σW(x)=δrPn
i=1 xi
3.
We substitute λas
λ=δp3α(1 −α)Pn
i=1 xi
3α
Therefore, we have
C∗
1(x) = E(W(x)) + δp3α(1 −α)Pn
i=1 xi
3α.
Moreover, to derive the second objective function by making use of
the Chernoff bound, we have:
Proposition 2 (Chernoff Constraint Bound Calculation).Let wi∈
U[E(wi)−δ, E(wi) + δ]be independent weights chosen uniformly
at random. Let E(W(x)) be the expected weight of xand αbe the
probability bound of the chance constraint. Then setting
C∗
2(x) = E(W(x))
−0.66δ
ln(α)−v
u
u
tln2(α)−9 ln(α)
n
X
i=1
xi
implies Pr[W(x)≥C∗
2(x)) ≤α.
Proof. We consider wi∈ U[E(wi)−δ, E(wi) + δ]. Then, to satisfy
Chernoff bound summation requirement, we normalize each random
weight into [0, 1] which yidenotes the normalized weight of item.
yi=wi−(E(wi)−δ)
2δ∈[0,1]
Y(x) =
n
X
i=1
yi=
n
X
i=1
wi−(E(wi)−δ)
2δxi.
Since yiis symmetric, then the total expected weight of Yis E(Y) =
1
2Pn
i=1 xi.Then, the total weight of a solution is given as
W(x) =
n
X
i=1
wixi= 2δY (x) + E(W(x)) −δ
n
X
i=1
xi.
We set
C∗
2=E(W(x)) + b
where
b=−0.66δ
ln(α)−v
u
u
tln2(α)−9 ln(α)
n
X
i=1
xi
.
Hence, the probability of violating the chance constraint. for a so-
lution is given as
Pr [W(x)≥C∗
2(x)]
=Pr "2δY (x) + E(W(x)) −δ
n
X
i=1
xi≥E(W(x)) + b#
=Pr "Y(x)≥1
2
n
X
i=1
xi+b
2δ#
=Pr Y(x)≥E(Y(x)) + b
2δ
=Pr [Y(x)≥(1 + t)E(Y(x))]
3
where
t=b
2δE(Y(x)) .
Using Chernoff bounds, we have
Pr [(Y(x)≥(1 + t)E(Y(x)))]
≤exp −t2
2 + 2
3tE(Y(x))
We have
t=−0.66 ln(α)−qln2(α)−9 ln(α)Pn
i=1 xi
Pn
i=1 xi
.
We use
ˆ
t=−0.66 ln(α)
Pn
i=1 xi≤b
2δE(Y(x)) =t
instead of twhich results in
Pr [Y(x)≥(1 + t)E(Y(x))]
≤Pr Y(x)≥(1 + ˆ
t)E(Y(x))
≤exp
−
(−0.66)2ln2α
(2E(Y(x)))2
2 + 2
3(−0.66 ln α
2E(Y(x)) )·E(Y(x))
= exp (−0.66)2·ln α·ln α
−8E(Y(x)) + 4
3(0.66 ln α)
≤α.
The last inequality holds as
−4
n
X
i=1
xi+4
3(0.66 ln α)≤(−0.66)2ln α
which completes the proof.
Note that the introduced additional objectives as C∗
1and C∗
2cal-
culate the smallest possible bound for which a solution meets the
chance constraint according to the used tail bound (Chebyshev or
Chernoff). The terms added to the expected total weight guarantee
that a given solution meets the chance constraint.
3.2 POSDC Algorithm
We adapt the algorithm proposed in [25] for our bi-objective op-
timization. We call the adapted algorithm, Pareto Optimization for
Stochastic Dynamic Constraint (POSDC) which deals with DCCKP.
POSDC (see Algorithm 1) is a baseline multi-objecitve EA which
tracks the moving optimum by storing a population in the vicinity
of the dynamic knapsack capacity. POSDC keeps a solution (x) if
C∗(x)is in [C−η, C +η], where ηdetermines the storing range.
Therefore, POSDC has two subpopulations which include feasible
and infeasible solutions (S=S−∪S+). Keeping an infeasible sub-
population helps POSDC to be prepared for the next change in the
dynamic constraint.
S−← {x∈S|C−η≤C∗(x)≤C}
S+← {x∈S|C < C ∗(x)≤C+η}.
Algorithm 1: POSDC
1Generate x∈ {0,1}nuniformly at random
2if C−η≤C∗(x)≤C+ηthen
3S←x
4else
5while S=∅do
6repair an offspring (y) by (1+1)-EA
7x←y
8if C−η≤C∗(y)≤C+ηthen
9S←x
10 while (not max iteration) do
11 if change in the capacity occurs (after τiterations) then
12 x←best solution in S
13 Update S−and S+with respect to the shifted capacity
14 if S=∅then
15 S←x
16 choose x∈Suniformly at random
17 y←create an offspring by flipping each bit of x
independently with the probability of 1
n
18 if (C−η≤C∗(y)< C)∧(@z∈S−:zPOSDC y)then
19 S−←(S−∪y)\ {z∈S−|yPOSDC z}
20 else if (C≤C∗(y)≤C+η)∧(@z∈S+:zPOSDC y)
then
21 S+←(S+∪y)\ {z∈S+|yPOSDC z}
22 return best solution
POSDC generates the initial solution uniformly at random, if the gen-
erated solution is out of the storing range, then (1+1)-EA (see Algo-
rithm 2) repairs the solution and stores it in the appropriate subpopu-
lation. (1+1)-EA is a single-objective baseline EA which is described
later.
POSDC uses a mutation operator to explore the search space and
find trade-off solutions. POSDC maintains a set of non-dominated
solutions with respect to P(x)and C∗(x)in its subpopulations. The
best solution in POSDC at each iteration is the solution with the high-
est profit in S−; If S−is empty, POSDC prefers the solution with the
smallest C∗in S+.
Note that if we can compute the solutions exactly, some solutions
in S+can be feasible. However, because computing C∗in exact is
difficult, we designate the optimum as the solution with the highest
profit in S−.
3.3 Single Objective Approach
We only use simple baseline algorithms to make a fair comparison
between the single-objective optimization and bi-objective optimiza-
tion. (1+1)-EA and GSEMO [12] are their equivalent counterparts if
we consider identical objective functions because they use the same
mutation operator. In this study, we adapt POSDC as a variant of
GSEMO to tackle both dynamic and chance-constrained components
of the problem. Therefore, we show the efficiency of bi-objective op-
timization by comparing POSDC with (1+1)-EA (see Algorithm 2).
(1+1)-EA generates one potential solution uniformly at random;
In each iteration, an offspring x0is produced by flipping each bit of
xwith probability 1/n [10]. The offspring x0replaces xif it is fitter
with respect to the fitness of a solution which is as follows,
f(1+1)(x) = (max{0, α(x)−α}, P (x))
where α(x)denotes the probability of chance constraint violation
based on Chebyshev’s inequality or Chernoff bound derived for
4
Algorithm 2: (1+1)-EA
1generate x∈ {0,1}nuniformly at random
2while termination criterion not satisfied do
3y←create an offspring by flipping each bit of x
independently with the probability of 1
n
4if f(1+1)(y)f(1+1) (x)then
5x←y
6return x
CCKP with uniform distribution in [27] as follows,
PrChebyshev ≡Pr [W(x)≥C]≤δ2Pn
i=1 xi
δ2Pn
i=1 xi+ 3(C−E(W(x)))2,
PrChernoff ≡Pr[W(x)≥C]
≤exp −3 (C−E(W(x)))2
4δ3δPn
i=1 xi+C−E(W(x))!.
The fitness function f(1+1) is in lexicographic order which means
that first, the algorithm searches for a feasible solution according to
the chance constraint and optimizes the profit afterwards. We have,
f(1+1)(x0)f(1+1) (x)
⇐⇒ (max{0, α(x0)−α}<max{0, α(x)−α})
∨((max{0, α(x0)−α}= max{0, α(x)−α})
∧(P(x0)≥P(x)))
Table 1. Corresponding weight and profit interval for knapsack problems
benchmark
type weight (wi)profit
Uncorrelated [1,1000] [1,1000]
Bounded strongly correlated [1,1000] E(wi) + c¸
When a change occurs in the dynamic constraint, the individual
(x) may become infeasible, and its probabilistic constraint violates
α. Therefore, (1+1)-EA mutates xto find a feasible solution for the
newly given constraint and optimizes the profit afterwards.
4 EXPERIMENTAL INVESTIGATION
In this section, we define the setup of our experimental investigation;
we apply the bi-objective optimization with the introduced objectives
and compare it with the single-objective optimization.
4.1 Experimental Setup
For this study, we use the binary knapsack test problems introduced
in [22] and later developed for dynamic knapsack problem in [25].
We consider two types of uncorrelated and bounded strongly cor-
related test problems. The latter is more difficult to solve because
the profit correlates with the weight [22]. Note that in our chance-
constrained setting, for bounded strongly correlated instances, we
consider the correlation between the expected weight and the profit.
Table 1 lists the corresponding weight and profit for each type of
knapsack instance where c¸ denotes a constant number [22].
For the Dynamic parameters of test problems, we define rwhich
determines the magnitude of changes. we consider changes accord-
ing to the uniform distribution in [−r, r]where r∈ {500,2000}to
consider the small and large magnitude of changes in the knapsack
constraint, respectively.
Also, the parameter ηfor POSDC has been considered equal to r
to cover the interval of the uniform distribution entirely for storing
desirable solutions [25].
Another dynamic parameter is the frequency parameter of τ,
which determine how many iterations there are between dynamic
constraint changes. We set τ∈ {100,1000}to observe fast and slow
changes in the constraint, respectively.
For stochastic parameters, we set α∈ {0.01,0.001,0.0001}to
consider loose and tight probability of chance-constraint violation
probability. We also set δ∈ {25,50}to assign small and large un-
certainty interval in the weight of items with uniform distribution. To
ensure that the weights of items subject to uncertainty are positive,
we also add a value of 100 to all weights to avoid negative values.
We use dynamic programming to find the exact optimal solution
with maximal profit P(x∗)for the deterministic variant of the knap-
sack problem. Therefore, we record P(x∗)for every dynamic capac-
ity change for each knapsack instances based on rand τ.
To evaluate the performance of our algorithms for DCCKP, we
consider the offline error which represents the distance between the
algorithm best-obtained solution in each iteration with respect to
P(x∗). Let xbe the best solution obtained by the considered algo-
rithm in iteration i. The offline error for iteration iis given as
φi=
P(x∗)−P(x)if Pr [W(x)≥C]≤α
(1 + Pr [W(x)≥C]) P(x∗)otherwise.
Note, that every solution xnot meeting the chance constraint receives
a higher offline error than any solution meeting the chance constraint.
The total offline error
Φ = P106
i=1 φi
106.
is the summation of offline error at each iteration divided by the num-
ber of total iterations (106).
4.2 Experimental Results
We combine the parameters of r,τ,αand, δto produce DCCKP test
problem instances for uncorrelated and bounded strongly correlated
with different types of complexities. For instance, a test problem with
r= 2000,τ= 100,α= 0.0001 and δ= 50 represents the most dif-
ficult test problem; because the magnitude of dynamic change in the
knapsack capacity is large and the capacity changes very fast every
100 iterations. Also, the allowable probability of chance-constraint
violation is very tight, and the uncertainty interval in the weight of
items is big.
We apply POSDC and (1+1)-EA integrated with Chebyshev and
Chernoff inequality tails to DCCKP instances. Specifically, we in-
vestigate the following algorithms:
•(1+1)-EA with Chebyshev’s inequality: (1)
•(1+1)-EA with Chernoff bound: (2)
•POSDC with Chebyshev’s inequality: (3)
•POSDC with Chernoff bound: (4)
Each algorithm initially runs for 104warm-up iterations before the
first change in the capacity occurs and continues for 106iterations.
5
Table 2. Statistical results of total offline error for (1+1)-EA and POSDC with small change in the dynamic constraint
r τ δ α (1+1)-EA-Chebyshev (1) (1+1)-EA-Chernoff (2) POSDC-Chebyshev (3) POSDC-Chernoff (4)
uncorrelated
Mean Std Stat Mean Std Stat Mean Std Stat Mean Std Stat
500 100 25 0.01 4232.51 475.50 2(∗),3(−),4(−)4288.23 481.60 1(∗),3(−),4(−)1485.56 177.13 1(+),2(+) ,4(∗)1381.90 183.45 1(+),2(+),3(∗)
500 100 25 0.001 5537.00 565.70 2(−),3(−),4(−)4457.91 512.51 1(+),3(−),4(−)3162.58 392.02 1(+) ,2(+),4(−)1561.59 210.73 1(+) ,2(+),3(+)
500 100 25 0.0001 9869.75 912.78 2(−),3(−),4(−)4590.04 518.27 1(+) ,3(+),4(−)7854.36 797.79 1(+) ,2(−),4(−)1718.78 243.25 1(+),2(+),3(+)
500 100 50 0.01 4862.10 512.38 2(∗),3(−),4(−)4916.24 540.29 1(∗),3(−),4(−)2266.92 281.86 1(+),2(+) ,4(∗)2062.41 291.98 1(+),2(+),3(∗)
500 100 50 0.001 7684.09 742.16 2(−),3(−),4(−)5252.78 593.48 1(+),3(∗),4(−)5442.18 616.59 1(+) ,2(∗),4(−)2414.63 346.82 1(+),2(+) ,3(+)
500 100 50 0.0001 14435.49 1539.90 2(−),3(∗),4(−)5559.46 621.60 1(+),3(+) ,4(−)13477.00 1291.40 1(∗),2(−),4(−)2730.43 400.21 1(+),2(+),3(+)
500 1000 25 0.01 2498.45 122.34 2(∗),3(−),4(−)2425.96 102.79 1(∗),3(−),4(−)1004.26 48.61 1(+),2(+),4(∗)896.22 77.77 1(+) ,2(+),3(∗)
500 1000 25 0.001 4240.41 286.22 2(−),3(−),4(−)2655.49 108.46 1(+),3(+),4(−)2900.27 130.49 1(+),2(−),4(−)1125.94 102.26 1(+),2(+) ,3(+)
500 1000 25 0.0001 8477.51 1091.61 2(−),3(∗),4(−)2837.93 119.76 1(+),3(+) ,4(−)7526.05 817.63 1(∗),2(−),4(−)1331.74 126.31 1(+),2(+) ,3(+)
500 1000 50 0.01 3342.10 178.28 2(∗),3(−),4(−)3195.27 148.83 1(∗),3(−),4(−)1904.68 83.35 1(+),2(+),4(−)1719.30 130.07 1(+),2(+) ,3(+)
500 1000 50 0.001 6440.89 611.99 2(−),3(−),4(−)3633.98 165.28 1(+),3(+),4(−)5282.87 380.55 1(+),2(−),4(−)2162.80 167.66 1(+),2(+) ,3(+)
500 1000 50 0.0001 11975.96 2404.11 2(−),3(∗),4(−)3983.10 193.16 1(+),3(+),4(−)11652.85 2129.08 1(∗),2(−),4(−)2555.44 201.20 1(+) ,2(+),3(+)
bounded-strongly-correlated
500 100 25 0.01 3287.08 390.63 2(∗),3(−),4(−)3333.50 389.40 1(∗),3(−),4(−)1523.05 166.13 1(+),2(+) ,4(∗)1400.05 125.34 1(+),2(+),3(∗)
500 100 25 0.001 4763.94 780.09 2(−),3(−),4(−)3509.95 428.48 1(+),3(∗),4(−)3251.30 454.50 1(+) ,2(∗),4(−)1583.87 142.56 1(+),2(+) ,3(+)
500 100 25 0.0001 9387.44 2060.47 2(−),3(∗),4(−)3674.83 446.95 1(+),3(+) ,4(−)7843.42 1450.53 1(∗),2(−),4(−)1745.45 156.64 1(+),2(+) ,3(+)
500 100 50 0.01 3998.90 528.67 2(∗),3(−),4(−)4052.63 516.41 1(∗),3(−),4(−)2327.44 290.44 1(+),2(+) ,4(∗)2107.63 210.29 1(+),2(+),3(∗)
500 100 50 0.001 7092.19 1348.32 2(−),3(−),4(−)4405.88 575.40 1(+),3(+),4(−)5516.23 906.14 1(+),2(−),4(−)2465.45 254.71 1(+),2(+) ,3(+)
500 100 50 0.0001 13743.81 3964.29 2(−),3(∗),4(−)4736.65 625.25 1(+),3(+) ,4(−)12936.38 3124.07 1(∗),2(−),4(−)2790.44 288.47 1(+),2(+),3(+)
500 1000 25 0.01 1971.76 244.69 2(∗),3(−),4(−)1892.49 231.54 1(∗),3(−),4(−)823.88 116.26 1(+),2(+) ,4(∗)730.56 60.10 1(+),2(+),3(∗)
500 1000 25 0.001 3455.36 486.91 2(−),3(−),4(−)2063.78 247.58 1(+),3(∗),4(−)2265.75 288.37 1(+) ,2(∗),4(−)905.61 74.11 1(+),2(+) ,3(+)
500 1000 25 0.0001 6931.91 1328.75 2(−),3(∗),4(−)2226.64 246.62 1(+),3(+) ,4(−)5709.88 949.12 1(∗),2(−),4(−)1059.64 79.37 1(+),2(+) ,3(+)
500 1000 50 0.01 2694.66 351.22 2(∗),3(−),4(−)2539.21 302.30 1(∗),3(−),4(−)1513.94 197.83 1(+),2(+) ,4(∗)1358.32 120.78 1(+),2(+),3(∗)
500 1000 50 0.001 5297.71 876.15 2(−),3(−),4(−)2883.68 335.21 1(+),3(+),4(−)4044.39 603.89 1(+),2(−),4(−)1694.02 145.32 1(+),2(+) ,3(+)
500 1000 50 0.0001 9534.51 2279.76 2(−),3(∗),4(−)3182.34 366.82 1(+),3(+) ,4(−)8831.99 1787.84 1(∗),2(−),4(−)1990.19 163.30 1(+),2(+),3(+)
Table 3. Statistical results of total offline error for (1+1)-EA and POSDC with large change in the dynamic constraint
r τ δ α (1+1)-EA-Chebyshev (1) (1+1)-EA-Chernoff (2) POSDC-Chebyshev (3) POSDC-Chernoff (4)
uncorrelated
Mean Std Stat Mean Std Stat Mean Std Stat Mean Std Stat
2000 100 25 0.01 5948.81 569.75 2(∗),3(−),4(−)6018.58 560.15 1(∗),3(−),4(−)1931.58 366.87 1(+),2(+),4(∗)1909.30 381.82 1(+) ,2(+),3(∗)
2000 100 25 0.001 6387.66 508.07 2(∗),3(−),4(−)6074.30 577.59 1(∗),3(−),4(−)3133.97 466.62 1(+),2(+),4(−)2009.58 388.93 1(+) ,2(+),3(+)
2000 100 25 0.0001 10237.76 594.11 2(−),3(−),4(−)6170.77 579.85 1(+),3(∗),4(−)7010.05 698.68 1(+) ,2(∗),4(−)2102.31 402.86 1(+),2(+) ,3(+)
2000 100 50 0.01 6328.16 563.72 2(∗),3(−),4(−)6399.28 594.11 1(∗),3(−),4(−)2476.40 410.13 1(+),2(+),4(∗)2378.09 430.97 1(+) ,2(+),3(∗)
2000 100 50 0.001 8198.35 556.15 2(−),3(−),4(−)6592.78 582.25 1(+),3(−),4(−)4963.66 603.48 1(+),2(+) ,4(−)2601.97 454.21 1(+),2(+) ,3(+)
2000 100 50 0.0001 15154.74 668.43 2(−),3(−),4(−)6794.26 590.57 1(+),3(+) ,4(−)12102.77 742.79 1(+),2(−),4(−)2806.32 467.27 1(+) ,2(+),3(+)
2000 1000 25 0.01 3027.71 377.45 2(∗),3(−),4(−)2966.82 374.67 1(∗),3(−),4(−)974.46 188.68 1(+),2(+),4(∗)874.60 190.49 1(+) ,2(+),3(∗)
2000 1000 25 0.001 4429.57 502.95 2(−),3(−),4(−)3120.19 381.19 1(+),3(∗),4(−)2556.49 361.79 1(+),2(∗),4(−)1040.90 210.60 1(+) ,2(+),3(+)
2000 1000 25 0.0001 8650.49 843.44 2(−),3(−),4(−)3255.02 416.14 1(+),3(+) ,4(−)6959.35 732.40 1(+),2(−),4(−)1186.47 235.49 1(+) ,2(+),3(+)
2000 1000 50 0.01 3714.96 442.43 2(∗),3(−),4(−)3558.83 432.43 1(∗),3(−),4(−)1704.27 270.78 1(+),2(+),4(∗)1513.22 278.61 1(+) ,2(+),3(∗)
2000 1000 50 0.001 6464.63 664.72 2(−),3(−),4(−)3872.78 476.96 1(+),3(∗),4(−)4697.95 565.23 1(+),2(∗),4(−)1845.29 322.04 1(+) ,2(+),3(+)
2000 1000 50 0.0001 13380.75 1349.08 2(−),3(∗),4(−)4159.55 509.55 1(+),3(+),4(−)12017.35 1151.98 1(∗),2(−),4(−)2138.14 367.74 1(+) ,2(+),3(+)
bounded-strongly-correlated
2000 100 25 0.01 4560.36 185.97 2(∗),3(−),4(−)4568.83 197.34 1(∗),3(−),4(−)1840.56 84.26 1(+),2(+),4(∗)1712.51 123.38 1(+),2(+) ,3(∗)
2000 100 25 0.001 5784.27 319.36 2(−),3(−),4(−)4718.52 189.73 1(+),3(−),4(−)3795.55 168.68 1(+),2(+) ,4(−)1896.19 95.79 1(+),2(+) ,3(+)
2000 100 25 0.0001 12130.92 1256.94 2(−),3(−),4(−)4879.27 180.63 1(+),3(+) ,4(−)9177.87 928.21 1(+),2(−),4(−)2063.82 79.10 1(+) ,2(+),3(+)
2000 100 50 0.01 5337.16 166.82 2(∗),3(−),4(−)5291.89 176.82 1(∗),3(−),4(−)2745.54 52.90 1(+),2(+),4(−)2484.25 54.08 1(+),2(+) ,3(+)
2000 100 50 0.001 8834.24 746.73 2(−),3(−),4(−)5653.89 184.06 1(+),3(+),4(−)6452.92 512.65 1(+) ,2(−),4(−)2862.14 42.48 1(+),2(+) ,3(+)
2000 100 50 0.0001 19641.09 2649.89 2(−),3(−),4(−)5987.13 204.54 1(+),3(+) ,4(−)15189.24 2029.02 1(+),2(−),4(−)3205.05 58.24 1(+),2(+) ,3(+)
2000 1000 25 0.01 2508.30 264.64 2(∗),3(−),4(−)2390.56 233.31 1(∗),3(−),4(−)963.19 99.04 1(+),2(+),4(−)828.44 45.06 1(+),2(+) ,3(+)
2000 1000 25 0.001 4120.10 557.29 2(−),3(−),4(−)2581.26 264.54 1(+),3(∗),4(−)2568.42 347.75 1(+),2(∗),4(−)1010.08 58.55 1(+) ,2(+),3(+)
2000 1000 25 0.0001 8550.48 1602.26 2(−),3(∗),4(−)2745.14 281.97 1(+),3(+) ,4(−)6518.39 1139.10 1(∗),2(−),4(−)1163.33 72.29 1(+),2(+),3(+)
2000 1000 50 0.01 3302.84 395.53 2(∗),3(−),4(−)3103.47 336.52 1(∗),3(−),4(−)1732.35 211.93 1(+),2(+),4(∗)1495.03 118.31 1(+) ,2(+),3(∗)
2000 1000 50 0.001 6334.26 1019.02 2(−),3(−),4(−)3455.32 379.88 1(+),3(+),4(−)4582.94 717.91 1(+) ,2(−),4(−)1844.94 154.35 1(+),2(+),3(+)
2000 1000 50 0.0001 12880.38 2979.96 2(−),3(∗),4(−)3757.97 409.95 1(+),3(+),4(−)10047.45 2229.63 1(∗),2(−),4(−)2154.25 185.00 1(+) ,2(+),3(+)
Table 4. Statistical results of total offline error for NSGA-II with large change in the dynamic constraint (r= 2000)
τ δ α uncorrelated bounded-strongly correlated
NSGA-II-Chebyshev (5) NSGA-II-Chernoff (6) NSGA-II-Chebyshev (5) NSGA-II-Chernoff (6)
Mean Std Stat Mean Std Stat Mean Std Stat Mean Std Stat
100 25 0.01 2215.77 295.97 3(∗),4(∗),6(∗)2130.59 279.40 3(∗),4(−),5(∗)2390.79 189.51 3(−),4(−),6(∗)2234.28 194.74 3(−),4(−),5(∗)
100 25 0.001 3509.35 421.03 3(∗),4(−),6(−)2268.36 289.77 3(∗),4(∗),5(+) 4399.93 374.16 3(∗),4(−),6(−)2416.83 148.89 3(∗),4(∗),5(+)
100 25 0.0001 7401.77 621.03 3(∗),4(−),6(−)2387.85 290.26 3(∗),4(∗),5(+) 9648.37 1205.88 3(∗),4(−),6(−)2652.75 166.96 3(+) ,4(∗),5(+)
100 50 0.01 2828.78 329.66 3(−),4(−),6(∗)2637.66 327.82 3(+) ,4(∗),5(+) 3342.50 234.54 3(−),4(−),6(∗)3042.16 200.33 3(∗),4(−),5(∗)
100 50 0.001 5358.32 552.16 3(∗),4(−),6(−)2905.99 352.18 3(∗),4(∗),5(∗)6993.80 744.54 3(∗),4(−),6(−)3439.87 215.47 3(+),4(∗),5(+)
100 50 0.0001 12392.67 677.71 3(∗),4(−),6(−)3150.29 392.47 3(+),4(∗),5(+) 15160.00 2418.27 3(∗),4(−),6(−)3798.48 239.52 3(+),4(∗),5(+)
1000 25 0.01 1275.93 157.45 3(−),4(−),6(∗)1170.70 157.26 3(∗),4(−),5(∗)1123.35 150.54 3(−),4(−),6(∗)1009.96 115.48 3(∗),4(−),5(∗)
1000 25 0.001 2844.91 318.58 3(∗),4(−),6(−)1333.13 168.23 3(+),4(∗),5(+) 2726.67 392.87 3(∗),4(−),6(−)1198.56 122.26 3(+) ,4(∗),5(+)
1000 25 0.0001 7228.72 700.04 3(∗),4(−),6(−)1495.80 180.51 3(+),4(∗),5(+) 6597.74 1210.95 3(∗),4(−),6(−)1360.20 150.56 3(+),4(∗),5(+)
1000 50 0.01 2016.38 233.56 3(−),4(−),6(∗)1812.49 222.10 3(∗),4(∗),5(∗)1872.84 237.35 3(∗),4(−),6(∗)1682.10 184.08 3(∗),4(∗),5(∗)
1000 50 0.001 4967.78 537.00 3(∗),4(−),6(−)2153.47 265.98 3(+),4(∗),5(+) 4679.54 753.61 3(∗),4(−),6(−)2032.80 215.07 3(+) ,4(∗),5(+)
1000 50 0.0001 12192.94 1174.98 3(∗),4(−),6(−)2447.42 306.57 3(+),4(∗),5(+) 9885.44 2350.72 3(∗),4(−),6(−)2342.47 253.88 3(+),4(∗),5(+)
6
Tables 2 and 3 report the performance of single-objective and bi-
objective optimization by the average and standard deviation of to-
tal offline error for 30 independent runs. Lower total offline error is
better because it shows the algorithm was closer to the P(x∗)for
each iteration. Note that when the problem becomes more uncertain,
the feasible region (without violating the probabilistic constraint) be-
comes more restrictive and the offline error will be increased.
Statistical comparisons are carried out by using the Kruskal-Wallis
test with 95% confidence interval integrated with the posteriori Bon-
ferroni test to compare multiple solutions [5]. The stat column shows
the rank of each algorithm in the instances; If two algorithms can be
compared with each other significantly, X(+) denotes that the cur-
rent algorithm is outperforming algorithm X. Likewise, X(−)signi-
fies the current algorithm is worse than the algorithm Xsignificantly.
Otherwise, X(∗)shows that the current algorithm is not different sig-
nificantly with algorithm X. For example, numbers 1(+),3(∗),4(−)
denote the pairwise performance of algorithm (2). The numbers show
that algorithm (2) is statistically better than algorithm (1); it is not
different from algorithm (3) and it is inferior to algorithm (4).
Table 2 lists the results when ris 500. We observe that when the
environment of the problem becomes more complex, finding a so-
lution which has a close distance to the optimal solution is harder.
As τdecreases, δincreases and αbecomes tighter, the offline error
for both (1+1)-EA and POSDC increases. However, as the problem
becomes more difficult to solve, POSDC obtains solutions with a
lower total offline error and lower standard deviation in comparison
with (1+1)-EA. We also find that the algorithms which use Chernoff
bound outperform other algorithms which use the Chebyshev’s in-
equality.
Table 3 lists our results when ris 2000. We observe that POSDC
can obtain better solutions in comparison with (1+1)-EA. When we
consider a bigger magnitude of changes in the constraint bound, the
population size of non-dominated solutions in POSDC is bigger than
when ris 500; because ηis equal to r, POSDC covers a bigger range
of solutions which leads to a bigger population.
Therefore, when the changes occur faster (smaller τ), POSDC has
less time to evolve its population. POSDC only mutates one indi-
vidual chosen randomly in its population, leading to a lower chance
of choosing the best individual for the mutation in its population.
In contrast, (1+1)-EA only handles one individual, mutates and im-
proves it on all iterations. Introducing our second objective function
for the bi-objective optimization approach helps POSDC to tackle
all these drawbacks and outperform its counterpart single-objective
approach; because trade-off solutions contain more information in
principle of finding better solutions.
For further investigation of our bi-objective optimization, we also
apply the Non-dominated Sorting Genetic Algorithm (NSGA-II) [8],
which is a state of the art multi-objective EA when dealing with
two objectives. We run NSGA-II with a population size of 20 using
Chebyshev and Chernoff inequality tails which are algorithms (5)
and (6), respectively in Table 4. Table 4 shows the results of NSGA-
II when ris 2000 for uncorrelated and bounded strongly correlated
instances and compares the performance of NSGA-II with POSDC.
For brevity, we only report stats of comparison between NSGA-II
and POSDC.
To have a fair comparison, we modify NSGA-II to keep the best-
obtained solution for the given knapsack bound Cin each iteration.
Table 4 shows that in most of the instances, NSGA-II performs as
good as POSDC when using the Chernoff bound. However, POSDC
can outperform NSGA-II in instances where δ= 25 and α= 0.01,
which is the most straightforward instance. The main difference be-
tween NSGA-II and POSDC is the selection mechanism. NSGA-II
uses the crowding distance sorting to maintain diversity through the
evolution of its population. This comparison can point out the possi-
ble research line to further investigate state-of-art non-baseline EAs
and multi-objective EAs solving DCCKPs.
5 CONCLUSIONS
In this paper, we dealt with the dynamic chance-constrained knap-
sack problem where the constraint bound changes dynamically over
time, and item weights are uncertain. The key part of our approach
is to tackle the dynamic and stochastic components of an optimiza-
tion problem in a holistic approach. For this purpose and to apply bi-
objective optimization to the problem, we developed an objective C∗
which calculates for a given solution xthe smallest possible bound
for which xwould meet the chance constraint. This objective func-
tion allows keeping a set of non-dominated solutions with different
C∗where an appropriate solution can be used to track the optimum
after the dynamic constraint bound has changed. As it is hard to cal-
culate the bound C∗(x)in the stochastic setting exactly, we have
shown how to calculate upper bounds for C∗(x)based on Chernoff
bound and Chebyshev’s inequality. We evaluated the bi-objective op-
timization for a wide range of chance-constrained knapsack prob-
lems with dynamically changing constraint bounds. The results show
that the bi-objective optimization with the introduced additional ob-
jective function can obtain better results than single-objective opti-
mization in most cases. Note that we also applied NSGA-II to the
problem to point out possible improvements by using state of the art
algorithms. It would be interesting for future work to extend these
investigations. In addition, our approach is not limited to dynamic
chance-constrained knapsack problems and the formulation can be
adapted to a wide range of other problems where we would formu-
late a similar second objective to deal with the chance constraint.
ACKNOWLEDGEMENTS
This work has been supported by the Australian Research Council
through grant DP160102401 and by the South Australian Govern-
ment through the Research Consortium ”Unlocking Complex Re-
sources through Lean Processing”.
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