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A Clonal Selection Algorithm Based Tabu Search
for Satisfiability Problems
Abdesslem Layeb
Computer Science Department - Mentouri University Constantine, Algeria.
Layeb.univ@gmail.com
Abstract— We present in this paper a new memetic
algorithm to deal with the Max Sat problem. The objective
is to find the best assignment for a set of Boolean variables,
which gives the maximum of verified clauses in a Boolean
formula. Unfortunately, this problem has been shown to be
NP-hard if the number of variables per clause is greater
than 3. The proposed approach is based on clonal selection
principles and local search method. In order to increase the
performance of clonal selection to deal with Max Sat
problem, an adaptive fitness function based on weighted
clauses has been used. The underlying idea is to harness the
optimization capabilities of clonal selection algorithm to
achieve good quality solutions for Max SAT problem. A
local search based on Tabu Search has been embodied in
the clonal selection algorithm leading to an efficient hybrid
framework which achieves better balance between
exploration and exploitation capabilities of the search
process. The obtained results are very encouraging and
show the feasibility and effectiveness of the proposed
hybrid approach.
Index Terms—Maximum Satisfiabilty problem, Artificial
Immune System, Clonal Selection, Tabu Search.
I. INTRODUCTION
The combinatorial optimization plays a very important
role in operational research, discrete mathematics and
computer science. The aim of this field is to solve several
combinatorial optimization problems that are difficult to
solve. The propositional satisfiability problem (SAT) is
among the well-known combinatorial optimization
problems in the literature. It is the task of determining the
satisfiability of a given Boolean formula by looking for
the variable assignment that makes this formula
evaluating to true [1]. The SAT problem is very
important in many fields. In fact, many problems are
easily representable in propositional logic areas such as
model checking [2], graph coloring [3] and task
planning[4] to cite just few.
In some cases, we want to know how many clauses are
true. This is another instance of the Sat problems called
MAX-SAT (MAXimum SATisfiability). In recent years,
there is growing interest in this problem, given its use in a
wide range of domains. For a propositional formula in
CNF, the problem is to determine the maximum number
of clauses of this formula that can be fulfilled. An
instance of this problem is the problem Max k-Sat [5].
This problem relates to instances of clauses with at most
k literals. Max 3-SAT is special case of Max k-Sat
problems where the Boolean expressions are written in
CNF (Conjunctive Normal Form) with 3 variables per
clause. The problem Max k-Sat has been shown to be NP-
complete for any k> 2 [5]. There are other variants of the
Max-SAT problem such as Weighted Max-SAT [6] and
Partial Max-SAT [7].
To solve the Max Sat problem, many algorithms were
proposed. In fact, there are two classes of algorithms for
solving instances of Max-Sat in practice: exact and
heuristics methods. The exact algorithms are able to find
the exact solution of the Max Sat problem, although they
have an exponential complexity [8]. The most popular
algorithms of this class are based on the Davis-Putnam-
Loveland algorithm (DPLL) [9, 10]. On the other hand,
the metaheuristic methods find an optimal solution in
good time, but they don’t guarantee to give the exact
solution of the problem. This class of methods contains
Evolutionary Algorithms (EA) [11, 12], Stochastic Local
Search (SLS) methods [13, 14, 15] and hybrid methods of
EA and SLS or Exact methods [16, 17, 18, 19].
Evolutionary computation has been proven to be an
effective way to solve complex engineering problems. It
presents many interesting features such as adaptation,
emergence and learning. Artificial neural networks,
genetic algorithms and artificial immune systems are
examples of bio-inspired systems used to this end. An
Artificial Immune System (AIS) [20, 21, 22] is a type of
optimization algorithm inspired from the principles and
processes of the vertebrate immune system. One of the
most used variant of AIS is the Clonal Selection
Algorithms (CSA) [23, 24]. They are most usually
applied to optimization and pattern recognition problems.
However, the use of a pure clonal selection algorithm for
satisfiability problems is not good enough. To overcome
this weakness, the integration of the Stochastic Local
Search (SLS) is required in order to increase the
performances of the evolutionary algorithms to deal with
the SAT problems.
The stochastic local search methods were
demonstrated to be useful in solving many complex
problems. Tabu Search (TS) [25] is among the most
popular and robust local search methods. TS is found to
be practical in many hard combinatorial optimization
problems. The main idea underlying is to diversify the
search and avoid becoming trapped in local by forbidding
or penalizing moves which take the solution, in the next
iteration, to point in the solution space previously visited.
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doi:10.4304/jait.3.2.138-146
For this, the algorithm creates a memory list of
undesirable moves called "tabu list". However, Tabu
restrictions may be overruled under certain conditions, in
which, a Tabu move leads to a better solution, this
principle is called aspiration criterion. Tabu search
algorithms are amongst the most effective local search
based methods for the Max Sat problems [14, 26]. The
main difficult of the tabu search is the choice of the best
parameter setting.
The present study was designed to investigate the use
of clonal selection algorithm hybridized with tabu search
to deal with the maximum satisfiability problem. The
proposed approach is a population based method where
every individual represents a potential solution to the
problem at hand. The features of the proposed method
consist in applying different immune system principles
like clonal selection and mutation to govern the dynamics
of the population in a way to optimize an adaptive
objective function based on the clause weights. To foster
the convergence to optimality, a local search based on
tabu search has been embodied within the optimization
process.
The remainder of the paper is organized as follows. In
section 2, a formulation of the tackled problem is given.
Section 3 presents a state of art on the Max sat solvers.
Some basic concepts of Artificial Immune Computing are
presented in section 4. In section 5, the proposed method
is described. Experimental results are discussed in section
6. Finally, conclusions and future work are drawn.
II. PROBLEM FORMULATION
Throughout this paper, n will represent the number of
Boolean variables and m will denote the number of
clauses in formula F. Given a Boolean formula F
expressed in CNF and having n Boolean variables x1, x2...
xn, and m clauses. The k-SAT problem can be formulated
as follows:
An assignment to those variables is a vector
v = (v1, v2… vn)
∈
{0,1}n
A clause Ci of length k is a disjunction of k
literals, Ci =( x1
∨
x2
∨
…
∨
xk)
Each literal is a variable or a negation of a
variable
Each variable can appear multiple times in the
expression.
If each clause Ci in the CNF formula is a disjunction of
k literals Ci =( x1
∨
x2
∨
…
∨
xk), the problem is called k-
SAT. Another important variant of satisfiability problem
is the Max k-Sat, this is a combinatorial optimization
problem. In this problem, the objective is to find the best
assignment v
∈
{0, 1}n that maximizes the number of
satisfied clauses in the Boolean formula. Therefore, it is
categorized as an optimization problem. The Max-Sat
problem can be defined by specifying implicitly a
pair
SC,, where is the set of all potential solutions
({0, 1}n) and SC (SCore) is a function , called
score of the assignment, equal to the number of true
clauses. Consequently, the problem consists of defining
the best binary assignment that maximizes the number of
true clauses in the Boolean formula.
Clearly, there are 2n potential satisfying assignments
for this problem, and it has been proven that the k-SAT
problem is NP-complete for any k >2. In this paper we
deal with the Max 3-SAT problem. It is obviously that
this problem is a combinatorial optimization problem. It
appears to be impossible to obtain exact solutions in
polynomial time. The main reason is that the required
computation grows exponentially with the size of the
problem. Therefore, it is often desirable to find near
optimal solutions to these problems.
III. MAX 3-SAT SOLVERS STATE-OF-THE-ART
Modern Max-Sat solvers have deeply improved the
techniques and algorithms to find optimal solutions. In
practice there are two broad classes of algorithms for
solving instances of SAT: Complete and Incomplete
methods. The complete methods examine the entire
search space, they fully answer for the problem of
satisfiability of an instance. In the worst case, the
computation time required for the execution of such
methods, increases exponentially with the size of the
instance to solve. The method of Davis and Putnam (DP)
[27] is one of the first methods dedicated to solving SAT
problem. The DP method is based on the application of
the resolution rule in order to eliminate variables. The
procedure of Davis, Putnam, Logemann and Loveland
(commonly known DPLL) is an enhancement of the DP
algorithm. DPLL escapes the exponential clause
generation and the high memory usage of DP. This
method replaces the resolution mechanism for the
separation of the problem into two problems. The idea is
to construct a binary tree in which each node represents a
DPLL recursive procedure. Each leave represents the
search stopping unless an empty clause if the formula is
satisfiable. The process search uses the backtracking
technique to go back in the search tree and thus to test
new branches. The DPLL takes advantage of two
techniques that improve the selection of assignments to
variables: the pure literal elimination and the unit
propagation [16]. Currently, the most of exact methods
are based mainly on the DPLL procedure [28, 29].
The fact that complete algorithms require huge amount
of computing time, even for relatively small problem
instances, incomplete methods have been developed to
resolve SAT or Max-Sat problems of large size in
reasonable time. Incomplete methods do not guarantee
finding the best solutions, because they do not explore the
entire space of possible solutions. However, the
incomplete methods are particularly suitable large-scale
practical Max-Sat problem instances. They are able to
find quickly a good solution towards the cost and
constraints of the problem. The incomplete methods are
principally based on metaheuristics such as: local search
methods, evolutionary algorithms or hybrid methods. The
local search methods are widely used to solve Max 3-Sat
problems [5]. Among the popular methods of this class,
we can cite the popular method GSAT [13] and its
improved variant Walksat [30]. GSAT starts with a
random assignment and iteratively apply a set of flips by
using a specific heuristics in order to enhance the number
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of satisfied clauses. Walksat is an evolution of GSAT in
which the heuristics used to select the variable to flip are
improved. The main difference lies in the estimated size
of the neighborhood: Walksat greatly reduces the number
of neighbors as it selects a false clause, and only one
variable of this clause may be flipped. One biggest
improvement to GSAT method is adopting the strategy of
"random walk" while solving the problem. This strategy
allows us to accelerate the convergence and allow
avoiding some local minima. The idea of this strategy is
to choose a variable to flip among the variables involved
in randomly selected false clause. Unfortunately, Walksat
suffers from local minima problem. To overcome this
problem, Walksat insert some randomness in the search
process. Walksat swaps between greedy moves and noisy
moves. Those moves are randomly chosen from the
variables that appear in unsatisfied clauses. New
heuristics were integrated in Walksat to choose a variable
of the clause to flip such as Random, Best Novelty,
Rnovelty and Tabu [31].
Evolutionary algorithms have been applied to SAT
problems. However, the use of a pure evolutionary
algorithm has been unsuccessful in Max Sat problems. In
fact, Rana and Whitley [32] showed that a classical
genetic algorithm is unsuitable for Max 3-Sat problem,
because this later requires more intensification search
than diversification search. Therefore, hybridization
between evolutionary algorithms and local search
methods are needed to find success. This kind of
hybridization is called memetic algorithms. Recently,
several memetic algorithms were proposed to deal with
the Max Sat problems for example: FlipGA [11] based on
hybrid genetic algorithm and a special flip heuristics;
GASAT [12] based on hybrid genetic algorithm and tabu
search, QGASAT [15] based on quantum evolutionary
algorithm and local search procedure.
Recently, a new kind of hybridization based on
incomplete and complete algorithms has emerged.
Hybridization consists to use heuristics inspired DPLL
for local search heuristics, and reciprocally the local
search methods for DPLL algorithm. In such
combinations, the incomplete methods are used to
quickly identify promising areas of search space in which
the exact methods practiced an exhaustive exploration.
There are several methods in the literature based on this
kind of hybridization [16, 17, 18].
IV. ARTIFICIAL IMMUNE SYSTEMS
In order to solve complex problems, ideas gleaned
from natural mechanisms have been exploited to develop
heuristics. Nature inspired optimization algorithms has
been extensively investigated during the last decade
paving the way for new computing paradigms such as
neural networks, evolutionary computing, artificial
immune systems, etc.
A Natural Immune System (NIS) is responsible for
defending the body against viruses and other infections.
The natural immune system is a complex system that can
be analyzed at different levels: molecules, cells and
organs. The immune system is divided into two major
defense systems: the innate immune system, which
represents the immune defenses present from the birth
and the adaptive immune system which represents all
defenses learned over time. Complex interactions
between entities within each level enable the resulting
immune system to protect the body from any harmful
entity, or exogenous agent, called antigen. A specific kind
of cells, known as B-cells, is responsible for the
destruction of the antigen (Fig. 1). The B-cell produces
antibodies that binds with the antigens and marks them
for destruction. The strength of the antibody/antigen
binding is termed antigenic affinity [20, 21]. Interesting
features of the immune system have encouraged their
adaptation to information technology for solving real
world problems.
Artificial immune system (AIS) is a recent paradigm
that attempts to capture interesting features of the systems
natural immunity, such as memorization, pattern
recognition, learning and skills adaptation. An artificial
immune system has been defined as an adaptive system
inspired by the immune system biological problem
solving [16, 17, 18]. Artificial immune systems can be
viewed as a composition of intelligent methodologies
inspired from natural immune systems for solving real
world problems. Many models have been inspired from
natural immune systems to solve various problems.
Figure. 1 Immune response
One particular feature of the natural immune system is
its ability to construct specific antibodies against new
antigens. The clonal selection principle [23, 24] handles
this as follows: when a new antigen is detected, the
available B-cells start producing antibodies. Those B-
cells that best recognize the antigen proliferate by
cloning. The clones then undergo hyper-mutation
mechanisms to promote their genetic variation. B-cells
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with high affinity differentiate into plasma cells and
memory cells, and B-cells with low affinity are either
destroyed or mutated. Plasma cells produce a high
number of antibodies against the invading antigen (Fig.
2). Memory cells, on the other hand, are long living cells
that confers the system a memory of the encountered
antigen.
Artificial Clonal selection is an artificial immune
system particularly based on clonal selection theory.
CLONALG (Clonal selection Algorithm) is a popular
algorithm that implements the clonal selection algorithm
[23, 24, 33]. It has been developed not only for realizing
machine learning and pattern recognition tasks but also
for solving optimization problems. A basic clonal
selection algorithm is given by figure 3.
Figure. 2 The process of clonal selection.
Step 1 k = 0; Initialize randomly
antibody population A(0) then
calculate the affinity of initialized
population.
Step 2 Produce a new antibody
population A(k) by Clonal Selection
Operator taking into account the
antibody affinity and the clonal size.
k = k + 1;
Step 3 if the stopped condition is
satisfied return the best antibody;
else return to Step2.
Figure. 3 A simple schema of clonal selection algorithm.
The big problem encountered when using AIS to solve
optimization problems is how to map the problem domain
into an immune system. De Castro and Timmis [21]
propose a framework to simplify the process of the
correspondence between the biological immune systems
and artificial immune systems. The core of this
Framework is composed of the following three elements:
Representation of system components: The
representation of space search and the solutions.
Affinity function: Similar to the fitness function
in evolutionary algorithms. It’s used to evaluate the
interaction of components with the environment and
between them.
Immune dynamics: A set of adaptation
procedures that govern the system dynamics. The
adaptation procedures are determined according to
the problem.
V. THE PROPOSED APPROACH
To solve the Max Sat problem, a new approach is
proposed based on the clonal selection principles and
enhanced by a local search procedure. In this approach, a
new objective function is adapted based on the weight of
clauses. In order to show how clonal selection concepts
have been tailored to the problem at hand, we need first
to derive a representation scheme which includes the
definition of an appropriate clonal representation of
potential Boolean assignments and the definition of
clonal selection operators. Then, we describe how these
defined concepts have been combined with local search
algorithm to deal with the Max 3-Sat problem. For
correspondence with the immune system, an antigen
represents Max 3-Sat problem environments and the
antibodies correspond to different solutions.
A. Clonal representation of Max Sat solutions
To successfully apply clonal selection principles on
Max 3-Sat problem, we have needed to map potential
solutions into a clonal representation that could be easily
manipulated by clonal selection operators. We used a
binary representation to encode B-cells which represent
potential solutions. There are several possible
representation of the Max 3-Sat space search like the
floating point representation and the clausal
representation. However the most successful evolutionary
algorithms for the Sat problems use the bit string
representation. Thus, one potential solution is represented
by a binary vector of size n where n is the number of
Boolean variables in the CNF formula (Figure. 4). The
digit 0 digit represents a false value and the digit 1
represents true value. The initial solutions are randomly
generated. However, we can start with initial solutions
obtained by using greedy heuristics in order to accelerate
the optimization process.
0 1 0 1 1 0 1 0 0 0 1 0
Figure. 4 Binary representation of B-cell
B. The hypermutation process
Its goal is to maintain the maximum diversity among
antibodies that compose the memory set. The
hypermutation process consists in changing the structure
of an antibody Ab by applying p random mutations in
order to improve its affinity. The result is a mutated
antibody Abm that’s generally better than the original
antibody Ab. Several hypermutation techniques are
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reported in the literature [21, 22, 23]. In the proposed
approach, we have used the inversely proportional
hypermutation. In this kind of hypermutation, antibodies
that have high affinity are mutated slightly, while
antibodies that have poor affinity undergo hypermutation
process most important.
C. Selection strategy
The selection is to select antibodies that have the best
affinity to construct the intermediate population in order
to apply the cloning process and the hypermutation. Each
antibody is selected with a probability related to its
affinity. Thus, antibodies with high affinity value have
more chance of being selected for the next generation.
Selection strategies that we can use in clonal selection
algorithms are the same as those applied in the
evolutionary algorithms. In our approach, we have used
the roulette wheel selection. Classical roulette wheel
selection provides higher chances of being selected to
fitter solutions. It is commonly regarded as an
intensification component, contrary to diversification
operators.
D. Replacement strategy
After the hypermutation process, new antibodies are
introduced into the population. In order to keep the
population size constant from generation to generation, a
replacement operator is used. There are different
strategies taking into account the memory component.
After replacement, some antibodies are removed and
replaced by randomly generated new one to diversify the
population. Although, the affinity of the antibody is low,
its chance to be replaced is great.
E. Adaptive Affinity
The affinity between the antibody and antigen
measures the compatibility degree between them. This
measure is very important because it helps to determine if
a solution is enough good and generally has a great
impact on the performance of immune algorithms. From a
computational point of view, the affinity is usually related
to the distance. However, in the case of Max 3-Sat
problem and optimization problems in general, we use
appropriate evaluation functions to solve the problem.
The choice of the affinity measure is generally reliant on
the represented problem. Thus, for the Max 3-sat
problem, several fitness functions [11] were proposed to
be used by the evolutionary algorithms. The standard
evaluation function used in the most incomplete methods
is the number of satisfied clauses. The objective is to
maximize the number of the true clauses. Unfortunately,
this kind of objective function cannot lead an
evolutionary algorithm to find the best solution. In fact,
there are many different solutions having the same
quality concerning the Max 3-sat evaluation function,
which makes it hard for an evolutionary algorithm to
select the appropriate solution leading to the best solution
[11]. This observation is obviously clear when dealing
with the satisfiable SAT instances. For this reason, we
have used an adaptive affinity function based on the
mechanism of stepwise adaptation of weights (SAW)
introduced by Eiben et al. [34]. The affinity function is
given by the following formula:
)()(
1
xCWxF i
m
i
i
(1)
Each clause Ci has a weight Wi. The affinity function
is adapted by modifying the weights Wi appropriately in
order to identify the hard clauses in the preceding search
process. A high weight Wi indicates that the clause Ci is
difficult and leads to a higher fitness of those solutions
that satisfy Ci. Consequently, the weights lead the search
process towards solutions satisfying the most difficult
clauses. In the beginning all weights are initialized by Wj
= 1, and later the weights are adjusted according to
following formula (x* is the current best antibody):
)(1 *
1xCWW iii
(2)
F. Local Search
The study of the behavior of the clonal selection
algorithm with simple hypermutation for Max-Sat
problem [19] showed that the convergence speed is slow.
In order to increase intensification capabilities of search,
we apply a local search algorithm when the memory cell
has not been improved for more than a given number of
generations. In [19], the authors have integrated the
walksat method in the core of the clonal selection
algorithm in order to improve its performances. However,
the experiments showed that the performance to find the
exact solutions for satisfiable instances is low and the
convergence speed is slowed down. In this section, we
show how a clonal selection algorithm can be greatly
improved by hybridizing with a new local search based
on tabu search. The optimization process is governed by
the affinity function SAW (eq.1) in order to focus on
difficult clauses and to escape from local optima.
The local search used in our approach is hybridization
between the Flip heuristic used in flipGA [11] and the
tabu search. Flip heuristic consists in flipping the
variables of the Boolean formula (fig. 6). The flip is
accepted if there is improvement in the number of
satisfied clauses. The process is repeated until there is no
improvement. Unfortunately, the big problem of this
heuristic is the large neighborhood size. To overcome this
problem, we have introduced the tabu search principles in
order to reduce the number of neighbors. We put in the
tabu list each variable that does not improve the affinity
function. In more detail, the tabu search method is given
by the figure 5.
The tabu variables are stocked in ordered FIFO list and
they are prevented to being flipped again during a given
number of iterations. The tabu list length is set
empirically according to the number of variables in the
Boolean formula. Based on the experiments of Mazure
[26], the tabu list length is set to 10% of the number of
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variables. Concerning the aspiration criterion, we have
not used in our search local any aspiration condition
because it can slow down the approach without
improving the results. However, we suggest using a good
adaptive aspiration criterion in order to escape from the
local minima.
Input: B-cell (Boolean assignment),
formula ф in CNF, maxflip.
improvement=1;
nbrflip=0 ;
repeat
improvement=0;
For i=1 to nVar
if ( var i ∉ tabuList){
flip the i-th variable of the B-
cell;
nbrflip++;
compute the gain of flip;
If (gain>=0){
accept the flip;
improvement= improvement +gain; }
Else {
tabuList.push(var i);
undo the flip of i-th variable
if(tabuList.size()==tabuListMax)
tabueList.pop (); }
End For
Until (improvement > 0 && nbrflip <
maxflip)
output: Boolean assignment VB
Figure. 5 Local search procedure.
G. Outline of the proposed framework
Now, we describe how the representation scheme
including clonal representation and clonal selection
operators has been hybridized with local search
algorithm. The whole process is repeated until the
stopping criterion is met; the number of generations is
used as stopping criterion. In more details, the proposed
approach can be described as in Figure 6.
Firstly, a population of antibodies is initialized using
binary strings, each encoding a given solution. The
population size is important factor in evolutionary
algorithms. A large population represents a large space
search of solutions, but increases the computational cost.
On the other hand, a small population size can lead to
local solutions. Seen, we use a local search method in the
core of clonal selection algorithm, it is preferably to
reduce the population size. We have found that a
population between 10 and 30 can give good results.
I
nput: Boolean formula in CNF form A
Initialization:
G
enerate a population
of B-cells called pop.
Repeat for G generations
1.
E
valuate the affinity of the
elements of pop.
2. Select N best B-cells from pop
w
ith respect to their affinities.
Then clone this selected B-
c
ells
to produce a clone population.
3.
M
utate each clone to produce a
mature population.
4. Apply the local search method
5.
E
valuate the affinity of the
m
ature population and select the
best B-
c
ell as a candidate cell.
I
f it is better than the memory
c
ell, it becomes the new memory
cell.
6. Replace all the B-cells of pop
with the best ones from the matur
e
p
opulation. Make sure that pop
contains the memory cell.
7. Replace the worst elements of pop
with new randomly generated ones.
Output: best variable assignment
,
number of satisfied clauses
Figure. 6 The proposed approach for max 3-sat problem
At each iteration, our method begins by evaluating the
affinity of each B-cell. The affinity value is the sum of
weighted satisfied clauses in the Boolean formula (eq. 1).
Next, Antibodies in the population will be duplicated
proportional to their affinity, the N selected B-cells, using
roulette wheel mechanism, are allowed to clone
themselves. The better the B-cell, the more clones it will
produce. The number of clones for each B-cell is
calculated by using the following formula:
j
j
affinity
i
affinity
i
nbClones (3)
Where affinityi is the affinity of the ith selected B-cell
and β is a parameter that indicates the desired clone
population's size.
After the population cloning, the mutation process
starts which transforms the clones to a degree inversely
proportional to their affinity. The mutation process is as
follows:
a. For each clone clonei: compute its normalized
affinity:
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AffAff
Affaff
affN i
iminmax
min
(4)
minAff and maxAff are the minimal and maximal
affinities found in the clone population.
b. Calculate the number of mutations to apply to
clonei:
max)1(min
i
snbMutation i
affN
i
affN (5)
The min and max parameters indicate how many
mutations to apply to the worst and best individual
respectively.
c. For each mutation of nbMutationsi a random bit is
flipped.
However, we have noticed that the use of simple flip
mutation is inefficient for Max 3-Sat. In order to enhance
the capacity of mutation, we apply the local search
procedure. The local search is applied after a specified
number of generations passes without any improvement
in the population's fitness.
After the mutation step, we evaluate the affinity of the
mature population and select the best B-cell as a
candidate cell. If it is better than the memory cell, the
candidate cell becomes the new memory cell. Finally, we
replace all the B-cells of the current population with the
best ones from both the current population and the mature
population and we replace the worst elements of the
current population with new randomly generated ones.
VI. EXPERIMENTAL RESULTS
The developed method called WClonTS (Weighted
Clonal selection with Tabu for Satisfiability problems) is
implemented in Java and tested on a micro-computer 3
GHz and 1 GB of memory. To assess the experimental
performance of WClonTS, several tests are conducted
with instances taken from the AIM benchmark instances.
The AIM instances are all generated with a particular
Random-3-SAT instance generator [35]. The particularity
is that the generator produces yes-instances and no-
instances independently for wide ranges. The used AIM
instances include yes-instances with low and high
clause/variable ratios that have exactly one solution. We
have used only the satisfiable benchmarks because we
want to evaluate the capabilities of our approach to find
the exact solutions for yes-instances.
To analyze the performances of the approach, we
performed a comparison with other popular methods in
the literature. We have compared WClonTS against two
programs based on clonal selection algorithm: ClonsatW
based on Walksat method [19] and ClonTS based on flip
heuristic and the standard affinity function (number of
true clauses). The purpose of this comparison is to show
the impact of local search and the affinity functions on
clonal selection optimization. Furthermore, we compared
our results with those of another evolutionary algorithm
(QGASAT) which is based on a hybrid quantum
evolutionary algorithm and flipping procedure [12]. We
have made a comparison with three state-of-art stochastic
local search programs: Walksat, Novelty [36] and an
Iterated Robust Tabu Search for Max-Sat IROTS [37], for
these programs, we have used the default parameters used
in the program UBCSAT [31]. Finally, statistical tests of
Freidman were carried out to test the significance of the
difference in the accuracy of each method in this
experiment. In all experiments, we have run the
WClonTS, ClonsatW and ClonTS programs using the
parameters’ settings shown in Table 1.
The table 2 summarizes the obtained results. For each
program, the best of ten consecutive runs is taken. The
results found seem to be very promising and demonstrate
the feasibility and efficiency of the combining an immune
artificial system with local search based tabu search and
the adaptive affinity function (Table 2). Indeed, the found
results are similar to the exact solutions as it’s confirmed
by the Friedman test (Fig. 7). As we can see, except the
program WClonSAT, all the other programs used in this
experiment fail to find the exact solutions for the
benchmarks aim-100-1_6-yes1-1:4 and aim-100-2_0-
yes1-1:4. The results show that the performance of the
clonal selection algorithm with the local search based on
Flip heuristics and tabu search is better than one based on
walksat ClonSatW or flip heuristic with standard affinity
function ClonTS. Compared to the second evolutionary
algorithm used in this experiment QGASAT, WClonTS is
obviously better. QGASAT is not competitive to deal
with yes-instances; it ranks the last in the Freidman test
(Fig. 7). By another hand, the experiment shows that the
programs Walksat, Novelty+ and ClonTS have similar
performances, but they rank third in the Freidman test.
However, the results of the programs IROTS and
ClonsatW are very close to the exact solutions.
TABLE 1 PARAMETERS OF PROGRAMS BASED CLONAL SELECTION
Parameter name Parameter
population size 20
mutation min 1 / number-of-
variables
mutation max 0.01
clone size (β) 30
replacement size 5
total number of generations before
stopping
100
number generations before applying
local search
10
Maxflip in Flip heuristic 40000
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© 2012 ACADEMY PUBLISHER
Figure. 7 Freidman test.
TABLE 2 COMPARATIVE RESULTS.
Tests Novelty+ Walksat IROTS QGASAT ClonTS ClonsatW WClonTS
aim-50-1_6-yes 79 79 79,75 79 79 79,75 80
aim-50-2_0-yes 100 100 100 99 100 100 100
aim-50-3_4-yes 170 170 170 170 170 170 170
aim-50-6_0-yes 300 300 300 300 300 300 300
aim-100-1_6-yes 159 159 159 159 159 159 160
aim-100-2_0-yes 199 199 199 199 199 199 200
aim-100-3_4-yes 340 340 340 339 340 340 340
aim-100-6_0-yes 600 600 600 600 600 600 600
aim-200-2_0-yes 399 399 399 399 399 399 400
aim-200-6_0-yes 1200 1200 1200 1200 1200 1200 1200
VII. CONCLUSION
In this paper, a new approach to solve the Max-3-Sat
problem is proposed. The developed approach is based on
hybridizing clonal selection algorithm with a new local
search algorithm based on flip heuristic and tabu search.
Our approach yields high quality solutions. It reaps
advantage from characteristics of clonal selection
algorithm and tabu search, better balance between
intensification and diversification of the search is
achieved. The use of the adaptive affinity function based
on the clauses weights has increased the performance of
our approach. According to the experimental results, the
choice of the local search procedure is crucial for the
effectiveness of the resulting algorithm. In most cases,
our program gives comparable or better solutions than
other programs of literature. In addition, the proposed
framework provides an extensible platform for evaluating
different variant of satisfiability problems. We envisage
also improving our framework to implement a parallel
hybrid clonal selection algorithm to reduce the CPU
running time.
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Abdesslem Layeb is currently an associate professor in the
department of computer science at the University of
Constantine. He received his PhD degree in computer science
from the University Mentouri of Constantine, Algeria. He is
researcher in MISC laboratory. The author’s major field of
study is combinatorial optimization methods and its application
to solve several problems from different domains like
Bioinformatics, imagery, formal methods. He has many
publications in theoretical computer science, bioinformatics,
and formal methods.
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© 2012 ACADEMY PUBLISHER
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