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Recent Trends in Soft Computing Techniques for
Solving Real Time Engineering Problems
Tharwat O. S. Hanafy1,2 , H. Zaini 1, Kamel A. Shoush1,2 and Ayman A. Aly1,3
1
Faculty of Engineering, Taif University, 888, Taif, Saudi Arabia,
2
Computers and Systems Engineering Department, Faculty of Engineering, Al-Azher University, Egypt,
3
Mechanical Engineering Department, Faculty of Engineering, Assiut University, 71516, Assiut, Egypt,
AI builds up an intelligent system by studying first the structure
of the problem (typically in formal logical terms), then formal
reasoning procedures are applied within that structure.
Intelligent systems (IS) provide a standardized methodological
approach to solve important and fairly complex problems and
obtain consistent and reliable results over time [2]. Extracting
from diverse dictionaries, intelligence means the ability to
comprehend; to understand and profit from experience. There
are, of course, other meanings such as ability to acquire and
retain knowledge; mental ability; the ability to respond quickly
and successfully to a new situation. In Control Systems the
main objective of modern industries is to manufacture low cost,
high quality products in short time[8]. The selection of optimal
cutting parameters is a very important issue for every
machining process in order to enhance the quality of machining
products and reduce the machining costs (Cus and Zuperl,
2009). It is expected that the next decade machine tools will be
intelligent machines with various capabilities such as
prediction of self set up required parameters to reach to the best
surface finishing qualities. Typically, surface inspection is
carried out through manually inspecting the machined surfaces
and using surface profile meters with a contact stylus. As it is a
post-process operation, it becomes both time-consuming
and-labor intensive. In addition, a number of defective parts can
be found during the period of surface inspection, which leads to
additional production cost (Aykut, 2011). Milling process is
one of the common metal cutting operations and is especially
used for making complex shapes and finishing of machined
parts. The quality of the surface plays a very important role in
the performance of the milling as a good quality milled surface
significantly improves fatigue strength, corrosion resistance or
creep life. Surface roughness also affects several functional
attributes of parts, such as contact causing surface friction,
wearing, heat transmission, light reflection, ability of
distributing and holding a lubricant, load bearing capacity,
coating or resisting fatigue. Therefore the desired finish surface
is usually specified and the appropriate processes are selected
to reach the desired surface quality (Lou et al.,1999).
Unlike turning, face milling or flat end milling operations,
predicting surface roughness for ball end milling by
mathematical models is very difficult. In recent years, the
trends are towards modeling of machining processes using
artificial intelligence due to the advanced computing capability.
Researchers have used various intelligent techniques, including
neural network, fuzzy logic, neuro-fuzzy, adaptive neuro-fuzzy
inference system (ANFIS), etc., for the prediction of machining
parameters and to enhance manufacturing automation.
Abstract— In recent times, engineers have very well
accepted soft computing techniques such as fuzzy sets
theory, neural nets, neuro fuzzy system, adaptive neuro
fuzzy inference system (ANFIS), coactive neuro fuzzy
inference system (CANFIS), evolutionary computing,
probabilistic computing, Computational intelligence (CI),
etc. for carrying out varying numerical simulation analysis.
In last two decades, these techniques have been successfully
applied in various engineering problems independently or
in hybrid forms. The main objective of this paper is to
introduce engineers and students about the latest trends in
soft computing. Also they will help young researchers to
develop themselves in futures.In recent years
Computational intelligence (CI) has gained a widespread
concern of many scholars emerging as a new field of study.
CI actually uses the bionics ideas for reference, it origins
from emulating intelligent phenomenon in nature. CI
attempts to simulate and reappearance the characters of
intelligence, such as learning and adaptation, so that it can
be a new research domain for reconstructing the nature
and engineering. The essence of CI is a universal
approximator, and it has the great function of non-linear
mapping and optimization.In this paper we give an
overview of intelligent systems. We discuss the notion itself,
together with diverse features and constituents of it. We
concentrate especially on computational intelligence and
soft computing.
Keywords: intelligent systems, computational intelligence,
soft computing, ISM.
1.Introduction
S everal computational analytic tools have matured in the last
10 to 15 years that facilitate solving problems that were
previously difficult or impossible to solve. These new
analytical tools, known collectively as computational
intelligence tools, include artificial neural networks, fuzzy
systems, and evolutionary computation. They have recently
been combined among themselves as well as with more
traditional approaches, such as statistical analysis, to solve
extremely challenging problems.
The intelligent systems are complexand are subject to a great
deal of debate. From the perspective of computation, the
intelligence of a system can be characterized by its flexibility,
adaptability, memory, learning, temporal dynamics, reasoning,
and the ability to manage uncertain and imprecise information.
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Artificial neural network (ANN) and Fuzzy logic are two
important methods of artificial intelligence in modeling
nonlinear problems[8]. A neural network can learn from data
and feedback; however, understanding the knowledge or the
pattern learned by it is difficult. But fuzzy logic models are easy
to comprehend because they use linguistic terms in the form of
if-then rules. A neural network with their learning capabilities
can be used to learn the fuzzy decision rules, thus creating a
hybrid intelligent system (John and Reza, 2003). A fuzzy
inference system consists of three components. First, a rule
base contains a selection of fuzzy rules; secondly, a database
defines the membership functions used in the rules and, finally,
a reasoning mechanism to carry out the inference procedure on
the rules and given facts. This combination merges the
advantages of fuzzy system and a neural network. In the present
work, the adaptive neuro-fuzzy model has been developed for
the prediction of surface roughness.
The quality of surface finish mainly depends on the interaction
between the work piece, cutting tool and the machining system.
Due to these reasons, there have been a series of attempts by
researchers to develop efficient prediction systems for surface
roughness before machining. Survey on previous surface
roughness research reveals that most of the researches proposed
multiple regression method to predict surface roughness. Some
research applied neural network, fuzzy logic, and neural-fuzzy
approaches. Optimization of surface roughness prediction
model, developed by multiple regression method, with a
genetic algorithm is presented in some journals. Among them,
statistical (multiple Hossain and Ahmad 113 regression
analysis) and artificial neural network (ANN) based modeling
are commonly used by researchers. Mital and Mehta (1988)
conducted a survey of surface roughness prediction models
developed and factors influencing surface roughness. They
found that most of the surface roughness prediction models are
developed for steels. For the prediction of surface roughness, a
feed forward ANN is used for face milling of aluminum alloy
by Bernardos et al. (2003) high chromium steel (AISI H11) by
Rai et al. (2010) and AISI 420 B stainless steel by Bruni et al.
(2008). Bruni et al. (2008) proposed the analytical and artificial
neural network models. Yazdi and Khorram (2010) worked for
selection of optimal machining parameters (that is, spindle
speed, depth of cut and feed rate) for face milling operations in
order to minimize the surface roughness and to maximize the
material removal rate using response surface methodology
(RSM) and perceptron neural network. Munoz-Escalona and
Maropoulos (2009) proposed the radial basis feed forward
neural network model. This paper is organized as follows:
Section 2 presents the Computational Intelligence and hybrid
algorithm on CI.
Section 3 gives a general description of the Soft Computing.
The last two sections give the application of different areas and
conclusion.
intelligence. Instead of using this general name to cover
practically any approach tointelligent systems, the AI research
community restricts its meaning to symbolic representations
and manipulationsin a top-down way [3]. In other words, AI
builds up an intelligent system by studying first the structure of
theproblem (typically in formal logical terms), then formal
reasoning procedures are applied within that structure.
Alternatively, non-symbolic and bottom-up approaches (in
which the structure is discovered and resulted for man
unordered source) to intelligent systems are also known. The
conventional approaches for understanding and predicting the
behavior of such systems based on analytical techniques can
prove to be inadequate, even at the initial stages of establishing
an appropriate mathematical model. The computational
environment used in such an analytical approach may be too
categorical and inflexible in order to cope with the intricacy and
the complexity of the real world industrial systems. It turns out
that in dealing with such systems, one has to face a high degree
of uncertainty and tolerate imprecision, and trying to increase
precision can be very costly [11].In the face of difficulties
stated above fuzzy logic (FL), neural networks (NN) and
evolutionary computation(EC) were integrated under the name
computational intelligence (CI) as a hybrid system. Despite the
relatively widespread use of the term CI, there is no commonly
accepted definition of the term. The birth of CI is attributed to
the IEEE World Congress on Computational Intelligence in
1994 (Orlando, Florida). Since that time not only a great
number of papers and scientific events have been dedicated to
it, but numerous explanations of the term have been published.
In order to have a brief outline of history of the term the
founding and most interesting definitions will be summarized
now.
2.1. HYBRID ALGORITHMS ON CI
In recent years, computational intelligence has gained a
widespread concern of many scholars emerging as a new field
of study. In various fields of research and applications, more
and more CI branches has made considerable progress, has
become a hot research subject.
 SMB Classification
The computational intelligence methods, although different
from each other, share the property of being non-symbolic and
operating in a bottom-up fashion, where structure emerges from
an unordered begin and is different from the imposed method in
AI[5].
CI mainly adopts Connectionism ideology and actually uses the
bionics ideas for reference, it origins from emulating intelligent
phenomenon in nature and is depicted in mathematics
language. CI attempts to simulate and reappearance the
characters of intelligence so that it can be a new research
domain for reconstructing the nature and engineering. Bezdek
consider that CI is based on data provided by the operator rather
than relying on ”knowledge”, which solve problems through
establishment of connections by training. CI is databased
intelligence. It can be found in many CI branches that neural
networks reflect the high-level structure of the brain; fuzzy
systems mimic low-level structure of the brain; evolution
computing has many similar characteristics with biological
2. Computational Intelligence
The development of digital computers made possible the
invention of human engineered systems that showintelligent
behavior or features. The branch of knowledge and science that
emerged together and from suchsystems is called artificial
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populationevolution. All these methods play a special role in
certain area respectively, but there are some inherent
limitations of them. Combine some methods and cross each
other on Computational Intelligence has become the new
direction of CI development. Based on this consideration,
domestic and foreign scholars put forward a variety of CI
classification methods, the purpose is to further explore the
nature of Computational Intelligence and develop hybrid
algorithms on CI methods. As a result, establishment of the
correct classification method and in depth study on the
computational mechanism and connection of each method will
give an approach to combine these CI methods effectively,
which provides the theoretical basis for establishment of a
unified intelligence system design and optimization
methodology. From the comprehensive discussion on the
definitions of Computational Intelligence and correct
understanding the nature and simulation mechanism of CI, this
paper
introduces
the
Simulation-Mechanism-Based
classification method for Computational Intelligence according
to the collection of all the branches of CI and all the different
classification methods. This method divides all the branches
into three categories: Organic mechanism simulation class
(OMS), Inorganic mechanism simulation class (IMS) and
Artificial mechanism simulation class (AMS). For a variety of
CI branches in each category, we can develop new hybrid
algorithms through combing them among internal or
inter-category according to the actual requirements. SMB
classification method and some of the hybrid algorithms are
shown in figure 1.Fig.1.SMB Classification and Composite
Patterns Computational Intelligence is a methodology class of
high consistent in goal level and unique in technique level,
which determines the characteristics of natural complimentarily
and integration among members of the CI. Combing CI
branches is often able to give attention to different levels and
perspectives in problem solving, and concerted by
complementing each other which can achieve very good
results. For complex systems solving problem in uncertain
environment, any method can’t give the perfect answer to the
exterior and interior of problem. In this background, only
applying”cocktail therapy” similar to the biomedical
application field can give a satisfactory answer to such a
complex and difficult proposition. This is the original intention
for the study on CI hybrid algorithm.
Research on CI hybrid algorithms has never stopped and
obtained good results in practical applications. In this area,
FNN and FEC are as the main research issues. Fuzzy logic
simulates human uncertainty understanding of the world; fuzzy
inference reflects the human reasoning processes. Neural
network tries to simulate the human brain’s neural structure and
solve complex problems by learning and training. Combining
fuzzy logic with neural networks has made human
understanding of the objective world turn into a learning
process, which is close to the mode of human thought process
and has provided a strong theoretical and methodological
support for us to understand and transform the nature.
Evolutionary Computing simulates the evolution process of
biology group, putting the human mind (knowledge) into this
process give birth to FEC, which achieve the optimal solution
faster and more effective. This article introduces hybrid
algorithm of Fuzzy Neural Network and puts forward the
M-ANFIS model finally.
Fogel formulated a third opinion in [7]. Starting from
adaptation as the key feature of intelligence, and observing that
traditional symbolic AI systems do not adapt to new problems
in new ways, he declares that AI systems emphasize artificial
and not the intelligence. Thus, it may be inferred that AI
systems are not intelligent, while CI systems are.
2.1 Inorganic Mechanism Simulation class (IMS)
Simulated annealing (SA) is a generic probabilistic met for the
global optimization problem of locating a good approximation
to the global optimum of a given function in a large search
space. It is often used when the search space is discrete (e.g., all
tours that visit a given set of cities). For certain problems,
simulated annealing may be more efficient than exhaustive
enumeration - provided that the goal is merely to find an
acceptably good solution in a fixed amount of time, rather than
the best possible solution. The name and inspiration come from
annealing in metallurgy, a technique involving heating and
controlled cooling of a material to increase the size of its
crystals and reduce their defects. The heat causes the atoms to
become unstuck from their initial positions (a local minimum of
the internal energy) and wander randomly through states of
higher energy; the slow cooling gives them more chances of
finding configurations with lower internal energy than the
initial one.By analogy with this physical process, each step of
the SA algorithm attempts to replace the current solution by a
random solution (chosen according to a candidate distribution,
often constructed to sample from solutions near the current
solution). The new solution may then be accepted with a
probability that depends both on the difference between the
corresponding function values and also on a global parameter T
(called the temperature), that is gradually decreased during the
process. The dependency is such that the choice between the
previous and current solution is almost random when T is large,
but increasingly selects the better or "downhill" solution (for a
minimization problem) as T goes to zero. The allowance for
"uphill" moves potentially saves the method from becoming
stuck at local optima which are the bane of greedier methods.
2.1.1Natural Computing
Natural computing, also called Natural computation, is a
terminology introduced to encompass three classes of methods:
1) Those that take inspiration from nature for the development
of novel problem-solving techniques;
2) Those that are based on the use of computers to synthesize
natural phenomena; and
3) Those that employ natural materials (e.g., molecules) to
compute. The main fields of research that compose these three
branches are artificial neural networks, evolutionary
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algorithms, swarm intelligence, artificial immune systems,
fractal geometry, artificial life, DNA computing, and quantum
computing, among others Computational paradigms studied by
natural computing are abstracted from natural phenomena as
diverse as self-replication, the functioning of the brain,
Darwinian evolution, group behavior, the immune system, the
defining properties of life forms, cell membranes, and
morphogenesis.
based Artificial Intelligence from Soft Computing techniques
based Computational Intelligence (Figure 3). Hard computing
is oriented towards the analysis and design of physical
processes and systems, and has the characteristics precision,
formality, categoricity. It is based on binary logic, crisp
systems, numerical analysis, probability theory, differential
equations, functional analysis, mathematical programming, and
approximation theory and crisp software .Figure 2: Artificial
Intelligence vs. Computational Intelligence Soft computing is
oriented towards the analysis and design of intelligent systems.
Although in hard computing imprecision and uncertainty are
undesirable properties, in soft computing the tolerance for
imprecision and uncertainty is exploited to achieve an
acceptable solution at a low cost, tractability, high Machine
Intelligence Quotient (MIQ). Prof. Zadeh argues that soft
computing, rather than hard computing, should be viewed as
the foundation of real machine intelligence. Soft computing, as
he explains, is a consortium of methodologies providing a
foundation for the conception and design of intelligent systems,
aimed at a formalization of the remarkable human ability to
make rational decision in an uncertain and imprecise
environment.
The guiding principle of soft computing is: Exploit the
tolerance for imprecision, uncertainty and partial truth to
achieve tractability, robustness, low solution cost and better
rapport with reality.
Fuzzy logic (FL) is mainly concerned with imprecision and
approximate reasoning, neural networks (NN) mainly with
learning and curve fitting, evolutionary computation (EC) with
searching and optimization. The constituents of soft computing
are complementary rather than competitive. The constituents
and the characteristics of hard and soft computing are
summarized in Table I.
2.1.2 Quantum computing
Quantum computing is the area of study focused on
developing computer technology based on the principles of
quantum theory, which explains the nature and behavior of
energy and matter on the quantum (atomic and subatomic)
level. Development of a quantum computer, if practical, would
mark a leap forward in computing capability far greater than
that from the abacus to a modern day super computer, with
performance gains in the billion-fold realm and beyond. The
quantum computer, following the laws of quantum physics,
would gain enormous processing power through the ability to
be in multiple states, and to perform tasks using all possible
permutations simultaneously. Genetic algorithms are search
algorithms that incorporate natural evolution mechanisms
including crossover, mutation and survival of the fittest. GA
paradigms work on populations of individuals, rather than on
single data points or vectors. They are more often used for
optimization, but also for classification. Evolutionary
programming algorithms are similar to genetic algorithms, but
do not incorporate crossover. Rather, they rely on survival of
the fittest and mutation. Genetic algorithms (GA) maintain a
pool of solutions rather than just one. The process of finding
superior solutions mimics that of evolution, with solutions
being combined or mutated to alter the pool of solutions, with
solutions of inferior quality being discarded.
Reactive search optimization focuses on combining machine
learning with optimization, by adding an internal feedback loop
to self-tune the free parameters of an algorithm to the
characteristics of the problem, of the instance, and of the local
situation around the current solution.
4. Applications
In Engineering Applications:
While earlier applications of the EC were biased toward the
artistic fields, other application fields, such as engineering,
have been developed and expanded recently
4.1 Control System
It is well known that helicopter is a complicated MIMO
nonlinear system and the outputs of the helicopter couple
strongly. [1] Although the helicopter’s dynamics model can be
gotten by the measurement in the wind tunnel, the model gotten
by this method is not accurate, and modification and
identification, which is based on the actual fly test data, is
required. For the Unmanned Helicopter, which is too small to
measure in the wind tunnel and the cost is very expensive, the
only way to get its mathematical model is identification based
on the test data. While the mathematical model that can
describe the helicopter’s longitudinal and lateral movement
have almost 40 parameters, it is difficult to get the accurate and
full model by these identification based on the traditional Least
Square and frequency domain method [2],
2.1.3 Simulated Annealing (SA) - a technique which can be
applied to any minimization or learning process based on
successive update steps (either random or deterministic) where
the update step length is proportional to an arbitrarily set
parameter which can play the role of a temperature. Then, in
analogy with the annealing of metals, the temperature is made
high in the early stages of the process for faster minimization or
learning, then is reduced for greater stability. Simulated
annealing (SA) is a related global optimization technique which
traverses the search space by generating neighboring solutions
of the current solution.
3. Soft Computing
Prof. Lotfi A. Zadeh [14] proposed a new approach for
Machine Intelligence; separating Hard Computing techniques
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Fig 1.Modified of SMB Classification and Composite Patterns, [1]
Figure 2: Artificial Intelligence vs. Computational Intelligence
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Table I. The constituents and the characteristics of hard and soft computing after [11].
5. Conclusion
Computational intelligence, soft computing and their
applications were reviewed. Experiments results have shown
that these approaches achieve equivalent or better performance
than traditional methods. The large use of FIS in the industrial
field is mainly due to the nature of real data, that are often
incomplete, noisy and inconsistent, and to the complexity of
several processes, where the application of mathematical
models can be impractical or even impossible, due to the lack of
information on the mechanisms ruling the phenomena under
consideration.
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