Decentralized control in nature for production and logistics
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Transcript of Decentralized control in nature for production and logistics
RHINE WAAL UNIVERISTY OF APPLIED SCIENCES
Faculty of Communication and Environment
Advanced Logistics Concepts for Production and Supply
Winter Semester 2014/2015
Prof. Dr. Andreas Schürholz
Title of Paper:
Decentralized Control in Nature for Production and Logistics
Syed Shahzaib Raza
16277
January 05, 2015
List of Contents
1. Introduction: ................................................................................................................ 1
2. Swarm Intelligence ...................................................................................................... 1
2.1 Ant Colony Optimization (ACO) ............................................................................................. 2
2.1.1 Procedure ................................................................................................................ 3
2.1.2 Applications ............................................................................................................. 3
2.2 Particle Swarm Optimization (PSO) ....................................................................................... 3
2.2.1 Procedure ................................................................................................................ 4
2.2.2 Applications ............................................................................................................. 5
2.3 Artificial Bee Colony (ABC) .................................................................................................... 5
2.3.1 Procedure ................................................................................................................ 6
2.3.2 Applications ............................................................................................................. 6
2.4 Firefly Algorithm (FA) ............................................................................................................ 6
2.4.1 Procedure ................................................................................................................ 7
2.4.2 Applications ............................................................................................................. 7
2.5 Cuckoo Search (CS) ................................................................................................................ 7
2.5.1 Procedure ................................................................................................................ 8
2.5.2 Applications ............................................................................................................. 8
3. Conclusion ................................................................................................................... 9
4. Recommendation ......................................................................................................... 9
References ......................................................................................................................... 10
List of Abbreviations
ABC Artificial Bee Colony
ACO Ant Colony Optimization
CS Cuckoo Search
FA Firefly Algorithm
JSSP Job Shop Scheduling Problem
NP Non-deterministic Polynomial
PSO Particle Swarm Optimization
SI Swarm Intelligence
List of Figures
Figure 1 Generalized procedure of ACO ............................................................................ 3
Figure 2 Generalized procedure of PSO ............................................................................. 4
Figure 3 Generalized procedure of ABC ............................................................................ 6
Figure 4 Generalized procedure of FA ............................................................................... 7
Figure 5 Generalized procedure of CS ................................................................................ 8
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1. Introduction: Nature has inspired scientists in different ways. Biologists and computer scientists found
ways of inspiration from the behaviour of living organisms in their groups. The learning
outcomes from this behaviour of living organisms developed new concepts and
algorithms. They can be based on swarm intelligence (SI), biological, chemical or
physical systems. These algorithms are referred as meta-heuristic optimization techniques
which have significant role in production and logistics industry. Many of these
algorithms are efficient and helpful in solving real-world problems. The nature-inspired
meta-heuristic optimization techniques which are based on swarm intelligence have
received great attention in recent years. Swarm intelligence emerged from the collective
behavior of the social insects like ants, bees, wasps and other animals like flocks of birds
or fish. SI based algorithms have decentralized control, self-organization and information
sharing between the agents and learning which provides the high efficiency to the
complex systems. The algorithms have iterative procedure, which after number of
iterations converges to the optimal solution for the problem. SI based algorithms such as
particle swarm optimization, ant and bee colony, cuckoo search and firefly algorithms
have advantages over other algorithms. These SI-based algorithms can be classified on
different levels depending upon the complexity of the problem. This paper mainly
focuses on the literature review of SI-based algorithms. After the literature review there is
a short summary of SI-based algorithms which are discussed in the report. are divided
into groups of general problems and their application procedures. Further the paper
describes the comparison of SI-based algorithms.
2. Swarm Intelligence Swarm Intelligence (SI) is a discipline of artificial intelligence (AI) which consists of
intelligent multi agents emerged by studying the collective behaviour of social insects
and animals. The agents work in colonies with cooperation which are non-sophisticated
individuals and achieve the optimal solution of complex tasks. The individuals interact
with the members and perform actions to solve the problem. The common features of the
cooperative activities are decentralized control, self-organization and information
sharing. The examples can be taken from the potential of wasps to build their nests, the
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formation of ants to search food, and organization of birds in a flock. A bird in a flock is
can be considered as autonomous as it has no commands to follow. It involves itself in a
flock and interacts with it mates and perform the task for searching of food avoiding any
collisions with neighbours. In the same way swarm intelligence has autonomous agents
which are subsystems. They communicate with the environment which typically consists
of its neighbours and performs the tasks itself. The advanced view of swarm proposes the
N number of agents that are supportive to each other in order to achieve the optimal
solution of a problem. These optimization techniques have great importance in world of
science and industry. Some of the application areas of these optimization techniques are
manufacturing, logistics, communication, networks, transportation and informatics. There
are various optimization techniques which are being implemented in industries. Some
delivered optimal solutions to the real problems efficiently. Table 1 represents the
popular optimization techniques of swarm intelligence whereas few of them are discussed
in the report.
Algorithm Author Reference
Ant Colony Optimization Dorigo 4.
Particle Swarm Optimization Kennedy and Eberhart 7.
Artificial Bee Colony Karaboga and Basturk 8.
Firefly Algorithm Yang 11
Cuckoo Search Yang and Deb 14
Table 1 Algorithms of Swarm Intelligence
2.1 Ant Colony Optimization (ACO)
The first ant colony optimization algorithms were introduced by Marco Dorigo and his
colleagues in early 90s by the observing ant colonies. Ants are social insects and live in
colonies. The focus of ants is on the survival of the colony rather than the individuals.
The foraging behaviour of ants inspired the researchers for developing the concept of ant
colony optimization which was initiated for the application of discrete optimization
problems. The basis of this behaviour is the communication between the ants by using
pheromone, a chemical substance which allows indirect communication to find the
shortest path between the food and their nest. ACO was implemented in the field of
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communications to solve the problems in networking and later on also used for
continuous optimization problems. This optimization technique solved many problems in
routing and load balancing. ACO algorithms are meta-heuristic as they solve the complex
problems at upper level. The ACO algorithm consists of the initial condition which is the
nest and food is represented as terminal condition. The considered as agent which moves
in a network so that it could drop the pheromones over the interface and selects the node
of the network for further steps. On the other hand the application of ACO also used in
manufacturing to minimize the time and number of work stations.
2.1.1 Procedure
2.1.2 Applications
ACO can be applied to various real-world problems. This algorithm focuses on the
shortest path finding which is found to be useful in solving complex problems. The
popular application areas of ACO are travelling salesman problem, scheduling, graph
colouring, constraint satisfaction, and routing in telecommunication networks.
2.2 Particle Swarm Optimization (PSO)
A population based stochastic optimization technique which is modelled on the behaviour
of social animals like flock of birds or school of fish is described as particle swarm
Set current position
Find the best point until the best path is discovered
Evaporate pheromone
Update path pheromones until the maximum iterations reached
Figure 1 Generalized procedure of ACO
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optimization. PSO has been found to be very interesting for scientists and researchers as
it is efficient in solving optimization problems. James Kennedy and Russell Eberhart
proposed PSO in 1995. The PSO emerged from the inspiration from fish and birds in
flocks where particles behave as individuals of a swarm to seek optimal solution of the
problem. The particles position themselves according to their neighbours and keep
updating their velocities with respect to the neighbours regardless of the space. The focus
of the swarm is on the area of search which contains high-quality solutions. The three
main characteristics of PSO are updating individuals in parallel, the value of each cell
depends on the old values and its neighbour, and all the same rules implies on all the cells
for updating. PSO delivers the quality solution of the problem and the particles search
considering the position and velocity that is advised. The principle is based on the flock
of birds which is in search of food. The members of the flock adjust their positions and
velocities according to their neighbours while the space is not taken into account.
2.2.1 Procedure
Initialize the particles
Calculate the fitness values for each particle
If the current fitness value of particle is better than previous then assign the new value otherwise keep
the previous value
Compare the best position of a particle with the position of the swarm, if the position of the particle is
better than assign the position of the particle as best position of swarm
Calculate the velocity of each particle
Use the velocity of each particle to update its data values
Repeat the process until the target
Figure 2 Generalized procedure of PSO
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2.2.2 Applications
PSO can be applied to various industries and can be very efficient. The general
applications of PSO are transportation, power systems, data mining, network design,
informatics and location finding.
2.3 Artificial Bee Colony (ABC)
Artificial bee colony (ABC) also referred as bee colony optimization was introduced by
Karaboga in 2005. Just like ACO and PSO are inspired by social life of ants and birds,
ABC is inspired by the social behaviour of bees in colonies. This meta-heuristic
algorithm initiated a new way of optimization to solve complex problems. The function
of ABC is described considering the bee’s behaviour in colony. First the bee surveys the
areas to find the food. When the food is discovered, the bee starts dancing in order to
inform the bees in the colony. The bees of the respective colony gather the food and get it
into the hive. There are three actions which they perform like searching for other food
location while abandoning the previous source of food, inform the nest mates before
returning to the food source, or continue the hunting of food. This foraging behaviour of
bees can be applied technically to solve many complicated problems of engineering,
transport, optimization and computational sciences. ABC is used by the researchers to
solve many complex problems including logistics and found to be efficient as compared
to other algorithms like PSO.
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2.3.1 Procedure
2.3.2 Applications
ABC is found to be very effective as compared to other algorithms especially in the areas
of bioinformatics, image processing, clustering, economic dispatch problem,
optimization, scheduling, vehicle routing and engineering design problems.
2.4 Firefly Algorithm (FA)
The firefly algorithm is a nature inspired algorithm which was introduced by Yang in
2007 based on the behaviour of fireflies and their flashing light pattern which helps in
finding mates and catching the attention of their prey. The swarm contains individuals
which move towards brighter locations with an objective to achieve the optimal solution
efficiently. The firefly algorithm is based on the attributes that artificial fireflies are
unisex, the more bright the flash light is the more attractive the firefly is which decreases
the distance as the most bright firefly convinces its neighbours to move towards it, and
the goal that is to be optimized can be the brightness of the flashing light. The process of
Initialize food positions
Select the food sources by determined by the neighbours of the employed bees
Store the location of food when all onlookers are distributed
Look for the locations of abandoned food sources
Create new positions for the abandoned food sources
Repeat the process until the termination criteria is satisfied
Figure 3 Generalized procedure of ABC
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iterations start from the lower intensity of light towards the higher intensity while the
distance of movement depends on the attractiveness of the firefly and the new firefly is
updated iteratively after evaluation. This process is repeated till the optimal solution is
obtained.
2.4.1 Procedure
2.4.2 Applications
FA has proved to be very efficient and the advancement in the literature shows the
potential of FA algorithm to deliver optimal solutions. The general application areas of
FA is JSSP, NP-hard, digital image compression and processing, antenna design and
multi-objective load dispatch problems.
2.5 Cuckoo Search (CS)
Cuckoo search (CS) is one of the latest optimization techniques which was developed
by Xin-She Yang and Suash Deb in 2009. This algorithm is natured inspired based on
the cuckoo species of birds and has been proved to be more efficient than PSO in
recent studies. Some species of cuckoo lay their eggs in the nests of other birds and
Create initial population of fireflies
Evaluate the fitness level of all the fireflies
Update the fitness value of fireflies
Rank the fireflies and update the position
Repeat the process until the maximum iterations number is reached
Figure 4 Generalized procedure of FA
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may remove the eggs of other birds to increase the probability of hatching of their
own eggs. The algorithm was developed which is based on the reproduction strategy
of cuckoo. This algorithm has three main rules i.e. a cuckoo lays one egg at a time
and selects the nest randomly to dump its egg, the nests which are best and contain
eggs of high quality will be carried over to the next generations, the host nests are
fixed in numbers and the cuckoo discovers its laid egg by the host bird. There can the
chance that the host bird gets rid of the egg or may abandon the nest to build its new
nest. These rules can be technically applied considering the egg in a nest as a solution
to the problem, while one cuckoo can lay one egg so it proposes one solution to reach
the goal of new and better solution replacing the old ones suggested. This can solve
many complex problems and may contain set of solutions to the problem.
2.5.1 Procedure
2.5.2 Applications
The CS algorithm is one of the latest algorithms and has been successful in solving real
world problems efficiently. The algorithm can be applied in the areas of pattern
recognition, job scheduling, networking, software testing, optimal path finding. This
algorithm is also applied in the business and health sector to deliver optimal solutions.
Start cuckoos with eggs
Find the nests and lay eggs in different nests
Repeat unless maximum population value is reached while some of the eggs are destroyed
Check the surviving eggs in the nests
Repeat the process until the condition is satisfied
Figure 5 Generalized procedure of CS
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3. Conclusion The SI-based algorithms have found to be very helpful in solving the real problems
efficiently. These algorithms are applied in different areas with different strategies to
deliver the optimal solutions. After the literature review of the algorithms, it can be
concluded that these algorithms have many advantage over each other. Considering the
algorithms for shortest path finding, ACO and CS can be implemented whereas the
output of these algorithms may vary from each other. The algorithms for job scheduling
are ABC and CS. For network designing problems, PSO and CS can be very helpful. FA
can be helpful for image processing whereas ACO and PSO are efficient in data
clustering. Thus there are various implementation areas where these algorithms can be
categorized and compared to solve the problems. These optimization techniques have
advantages over each other depending upon the area of application of the problem. The
complexities of the algorithms also vary from each other. These complexities can be
reduced while applying different techniques to achieve the best optimal solution for the
complex problems.
4. Recommendation
While considering the SI-based algorithms, there are many solutions that can be obtained.
These algorithms are being applied in different industries at different levels. It can be
recommended that studies of the applications of different algorithms in one industry can
be done. This can help the industries in evaluating the algorithms and their efficiency
while applying in the same industry. On the other hand the complexities of the algorithms
to a specific problem can also be determined. This would the industries to get optimized
solutions to a specific problem. On the other hand, the focus of the report was on SI-
based algorithms, whereas the literature review on other algorithms can also be carried
out. This would be helpful in evaluating the algorithms of other than nature inspired, and
the application areas of those algorithms can also give broader view of the advantages of
algorithms. Furthermore, there can be new sectors where these algorithms can be
implemented and could be helpful. The research on the applications of these algorithms
would be beneficial as well.
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