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Accepted papers
- An Ant Algorithm for the Weighted Minimum Hitting Set Problem
A. Cincotti, V. Cutello, and F. Pappalardo
Abstract:
We present an ant-based algorithm for finding good
near optimal solutions to Weighted Minimum Hitting Set problem. We
compared our results with the ones obtained by a greedy procedure and by
an ad hoc genetic algorithm.
- Evolutionary Multiobjective Optimization using a Cultural
Algorithm
Carlos A. Coello Coello and Ricardo Landa Becerra
Abstract:
In this paper, we present the first proposal to use a cultural
algorithm to solve multiobjective optimization problems. Our proposal
uses evolutionary programming, Pareto ranking and elitism (i.e., an
external population). The approach proposed is validated using several
examples taken from the specialized literature. Our results are compared
with respect to the NSGA-II, which is an algorithm representative of the
state-of-the-art in evolutionary multiobjective optimization. The
performance of our approach indicates that cultural algorithms are a
viable alternative for multiobjective optimization.
- Multi-Robot Cooperation Method Based
on the Ant Algorithm
Yingying Ding, Yan He, and Jingping Jiang
Abstract:
Ant Algorithm is an optimization algorithm that gained by observing the
real ant colonies, and it is very useful in solving difficult
optimization problems and distributed control problems. The algorithm is
modeled on a key concept called "stigmergy" of the ant societies, which
is used in our algorithm to design the cooperation of multi-robots. In
an unknown environment, one of the most important problems to the
multi-robot system is to decide a task be completed by how many robots.
With the concept "stigmergy", the number of the robots cooperating on a
task is decided according to the difficulty of the task. Given the
definition of the "Task Deadlock", an adaptive attenuation factor to
eliminate task deadlock is introduced in the cooperation algorithm.
Simulations show the effectiveness of the algorithm.
- Using Swarm Intelligence for Dynamic Web Content Organizing
Salima Hassas
Abstract:
The World Wide Web contains a huge amount of unstructured,
distributed, multi-media data. This content provides a great potential
source for knowledge acquisition that needs to be filtered, organized,
and maintained in order to permit an efficient use. The wide
distribution of the web, its openness and high dynamics make any
tentative of content organization or maintaining, a task very hard to
achieve. The WWW is thus, a complex system, for which we have to imagine
mechanisms of content maintaining, filtering and organizing, that are
able to deal with its content evolving, dynamics and distribution.
Integrating mechanisms of self-organization of the web content is an
attractive perspective, to match with these requirements.
- Strategies for Finding Good Local Guides
in Multi-objective Particle Swarm Optimization (MOPSO)
Sanaz Mostaghim and Jürgen Teich
Abstract:
In multi-objective particle swarm optimization (MOPSO) methods,
selecting the best local guide (the global best particle) for each
particle of the population from a set of Pareto-optimal solutions has a
great impact on the convergence and diversity of the solutions.
especially when optimizing problems with high number of objectives. This
paper introduces the Sigma method as a new method for finding best local
guides for each particle of the population. The Sigma method is
implemented and is compared with another method, which uses the strategy
of an existing MOPSO method for finding the local guides. These methods
are examined for different test functions and the results are compared
with the results of a multi-objective evolutionary algorithm (MOEA)
- Computing Periodic Orbits of
Nondifferentiable/ Discontinuous Mappings Through Particle Swarm
Optimization
K.E. Parsopoulos and M.N. Vrahatis
Abstract:
Periodic orbits of nonlinear mappings play a central role in the study
of dynamical systems. Traditional root finding algorithms, such as the
Newton–family algorithms, have been widely applied for the detection of
periodic orbits. However, in the case of discontinuous
/nondifferentiable mappings and mappings with poorly behaved partial
derivatives, this approach is not valid. In such cases, stochastic
optimization algorithms have proved to be a valuable tool. In this
paper, a new approach for computing periodic orbits through Particle
Swarm Optimization, is introduced. The results indicate that the
algorithm is robust and efficient. Moreover, the method can be combined
with established techniques, such as Deflection, to detect several
periodic orbits of a mapping. Finally, the minor effort which is
required to implement the proposed approach, renders it an efficient
alternative for computing periodic orbits of nonlinear mappings.
- Virtual Instrument Parameter Calibration
with Particle Swarm Optimization
Yu Peng, Xiyuan Peng, and Shengwei Meng
Abstract:
In virtual instrument designs and applications, lots of functional
parameters can be set through software methods. Currently, most
parameter settings methods are tightly linked with the knowledge of
instruments and basic principles related to specific applications.
However, it is difficult for some end users to deal with those advanced
operations. By adopting the Particle Swarm Optimization (PSO) Algorithm,
the adaptive set and calibration of instrument parameters can be
achieved by software with computational intelligence. Experiments and
applications showed adaptive parameter calibrating method based on the
PSO can enhance the effectiveness of debug and maintenance of virtual
instrument and test system.
- Ant Colony Optimisation for E-Learning:
Observing the Emergence of Pedagogic Suggestions
Yann Semet, Evelyne Lutton, and Pierre Collet
Abstract:
An attempt is made to apply Ant Colony Optimization (ACO) heuristics
to an E-learning problem: the pedagogic material of an online teaching
website for high school students is modeled as a navigation graph where
nodes are exercises or lessons and arcs are hypertext links. The arcs’
valuation, representing the pedagogic structure and conditioning the
website’s presentation, is gradually modified through the release and
evaporation of virtual pheromones that reflect the successes and
failures of students roaming around the graph.
A compromise is expected to emerge between the pedagogic structure as
originally dictated by professors, the collective experience of the
whole pool of students and the particularities of each individual.
The purpose of this study conducted for Paraschool, the leading French
e-learning company, is twofold: enhancing the website by making its
presentation intelligently dynamic and providing the pedagogical team
with a refined auditing tool that could help it identify the strengths
and weaknesses of its pedagogic choices.
- Engineering Optimization with Particle
Swarm
Xiaohui Hu, Russell C. Eberhart, and Yuhui Shi
Abstract:
This paper presents a modified particle swarm optimization (PSO)
algorithm for engineering optimization problems with constraints. PSO is
started with a group of feasible solutions and a feasibility function is
used to check if the newly explored solutions satisfy all the
constraints. All the particles keep only those feasible solutions in
their memory. Several engineering design optimization problems were
tested and the results show that PSO is an efficient and general
approach to solve most nonlinear optimization problems with inequity
constraints.
- Using a Collection of Humans as an
Execution Testbed for Swarm Algorithms
Daniel W. Palmer, Marc Kirschenbaum, Jon P. Murton, Michael A.
Kovacina, Daniel H. Steinberg, Sam N. Calabrese, Kelly M. Zajac, Chad M.
Hantak, and Jason E. Schatz
Abstract:
To gain insight into swarm algorithms, researchers can study insect
societies and other natural collectives, program multi-agent software
simulations or build groups of cooperating robots. In our research, we
consider another resource: swarms of humans. Human swarms provide three
primary benefits: quick feedback and evaluation of swarm algorithms,
experience with high-level swarm directives instead of low-level agent
programs, and a source of swarm algorithms that can potentially be
reverse-engineered for use in other applications. Planning is a human's
preferred problem solving methodology because we are intelligent
creatures with high-level communication skills. Due to the intelligence
of the agents, human swarms can be quickly programmed, for subsequent
observation and analysis. This paper describes human swarm experiments
designed for gathering information on swarm algorithms. At these events
100 volunteers, wearing data-encoded T-shirts, work together to perform
tasks of differing degrees of complexity. Researchers provide simple
instructions for each task (programming the swarm), record the swarm's
behavior (videotaped observation) and analyze the results (problem
identification and algorithm-mining). We demonstrate the viability of
this research by presenting the quick identification of a swarm
algorithm "bug" and by producing a software implementation of a swarm
algorithm gleaned from our observations.
- Clustering Ensemble Using Swarm
Intelligence
Yan Yang and Mohamed Kamel
Abstract:
This paper presents a clustering ensemble using three colonies of ants,
each colony having different ant speed model: constant, random, and
randomly decreasing. The algorithm is a two-phase process. Initially
clusterings are visually formed on the plane by ants walking, picking up
or dropping down projected data objects with different probability, and
then a hypergraph model is used to combine clusterings. Results on
synthetic and real data sets are given to show that the number of
clusters can be adaptively determined and clusterings ensemble can
improve the clustering performance.
- Particle Swarm Optimization with
Gaussian Mutation
Natsuki Higashi and Hitoshi Iba
Abstract:
In this paper we present Particle Swarm Optimization with Gaussian
Mutation combining the idea of the particle swarm with concepts from
Evolutionary Algorithms. This method combines the traditional velocity
and position update rules with the ideas of Gaussian Mutation. This
model is tested and compared with the standard PSO and standard GA. The
comparative experiments have been conducted on unimodal functions and
multimodal functions. PSO with Gaussian Mutation is able to obtain the
result superior to GA. We also apply the PSO with Gaussian Mutation to a
gene network. Consequently, it has succeeded in acquiring the better
results than those by GA and PSO alone.
- Swarm Intelligence for Permutation
Optimization: a Case Study on N-Queens Problem
Xiaohui Hu, Russell C. Eberhart, and Yuhui Shi
Abstract:
This paper introduces a modified Particle Swarm Optimizer, which deals
with permutation problems. Particles are defined as permutations of a
group of unique values. Velocity updates are redefined based on the
similarity of two particles. Particles changed their permutations with
a random rate defined by their velocities. A mutation factor is
introduced to provent the current pBest from stucking at local minima.
Preliminary study on n-queens problem showed that the modified PSO is
promising in solving the constraint satisfication problems.
- Bare Bones Particle Swarms
James Kennedy
Abstract:
The particle swarm algorithm is modified by eliminating the velocity
formula. Variations are compared.
- Watch thy Neighbor or How the
Swarm can Learn From its Environment
Rui Mendes, James Kennedy, and Jose Neves
Abstract:
Particle swarm Optimization is a novel algorithm where a population of
candidate problem solution vectors evolves "social" norms by being
influenced by their topological neighbors. Until now, an individual
was in influenced by its best performance acquired in the past and the
best experience observed in its neighborhood. In this paper, we
introduce new ways an individual can be influenced by its neighbors.
- Synchronized Multi-Point Attack by
Autonomous Reactive Vehicles with Simple Local Communication
Chin A. Lua, Karl Altenburg, and Kendall E. Nygard
Abstract:
We present a model consisting of a swarm of unmanned, autonomous
flying munitions to conduct a synchronized multi-point attack on a
target. The Unpiloted Air Vehicles (UAVs) lack global communication or
extensive battlefield intelligence, instead, relying on passive
short-range sensors and simple, inter-agent communication. The
multi-point synchronized attack is successfully demonstrated in a
simulated battlefield environment. The simulation results indicate
that the reactive, synchronized, multi-point attack is effective,
robust and scalable. It is especially well suited for numerous, small,
inexpensive, and expendable UAVs.
- Social Programming using
Functional Swarm Optimization
Mark S. Voss
Abstract:
The development of mathematical neural networks was based on an
analogy with biological neural networks found in nature. Recently
there has been a resurgence in research and understanding in
self-organizing networks that are based on other metaphors: genetics,
immune systems etc. In this paper a new methodology is presented for
creating Complex Adaptive Functional Networks (CAFN) that are based on
the Particle Swarm socialpsychological metaphor. The proposed Social
Programming methodology is base on combining the Particle Swarm
methodology with The Group Method of Data Handling and Cartesian
Programming.
- Comparison of Particle Swarm
Optimization and Back-propagation as Training Algorithms for Neural
Networks
Venu G. Gudise and Ganesh K. Venayagamoorthy
Abstract:
Particle swarm optimization (PSO) motivated by the social behavior of
organisms, is a step up to existing evolutionary algorithms for
optimization of continuous nonlinear functions. Backpropagation (BP)
is generally used for neural network training. Choosing a proper
algorithm for training a neural network is very important. In this
paper, a comparative study is made on the computational requirements
of the PSO and BP as training algorithms for neural networks. Results
are presented for a feedforward neural network learning a non-linear
function and these results show that the feedforward neural network
weights converge faster with the PSO than with the BP algorithm.
- Utilizing Particle Swarm
Optimization to Label A Structured Beam Matrix - Preliminary Study
Qin Sun, Yuhui Shi, Russell C. Eberhart, and William A. Bauson
Abstract:
The use of structured lighting is common in machine vision systems
where three-dimensional measurements must be made. In one method, a
matrix of bright spots is projected onto an object. The spots move
from predetermined positions depending on the distance between the
object and the camera. Each spot must be labeled and tracked in order
to compute the distance. In this paper, particle swarm optimization
(PSO) has been utilized to label a structured beam matrix.
Experimental results illustrate the effectiveness of the PSO approach.
- Particle Swarm Optimization
Recommender System
Supiya Ujjin and Peter J. Bentley
Abstract:
Recommender systems are new types of internet-based software tools,
designed to help users find their way through today’s complex on-line
shops and entertainment websites. This paper describes a new
recommender system, which employs a particle swarm optimization (PSO)
algorithm to learn personal preferences of users and provide tailored
suggestions. Experiments are carried out to observe the performance of
the system and results are compared to those obtained from the genetic
algorithm (GA) recommender system [1] and a standard, non-adaptive
system based on the Pearson algorithm [2].
- Ant Algorithms for the Optimal
Restoration of Distribution Feeders During Cold Load Pickup
Indira Mohanty, Jugal Kalita, Sanjoy Das, Anil Pahwa, and Erik
Buehler
Abstract:
The ant colony algorithm is a new technique for combinatorial
optimization borrowed from swarm intelligence. This paper outlines an
ant colony algorithm to compute the optimal order of restoring
sections in a power distribution system. Restoration of distribution
feeders after long interruptions creates cold load pickup conditions
due to loss of diversity among the loads. The distribution system load
may have to be restored step-by-step using sectionalizing switches
under such conditions to prevent overheating of substation
transformer. The restoration time is dependent on the order in which
sections are restored. Results obtained using this method for two test
cases are presented including a comparison with the simulated
annealing algorithm.
- Optimal Operational Planning
for Cogeneration System Using Particle Swarm Optimization
Tatsuya Tsukada, Toyokazu Tamura, Shinji Kitagawa, and Yoshikazu
Fukuyama
Abstract:
This paper proposes optimal operational planning for cogeneration
system (CGS) using particle swarm optimization (PSO). CGS is usually
connected to various facilities such as refrigerators, reservoirs, and
cooling towers. In order to generate optimal operational planning for
CGS, startup and shutdown status and input values of the facilities
for each control interval should be determined. The facilities may
have nonlinear input-output characteristics. Therefore, the problem
can be formulated as a mixed-integer nonlinear optimization problem (MINLP).
PSO can be easily expanded to be utilized for MINLP. The simple
expansion of PSO for the optimal generation system operational
planning problem is proposed and the proposed method is applied to
typical CGS planning problems with promising results.
- Traffic Incident Detection
Using Particle Swarm Optimization
Dipti Srinivasan, Wee Hoon Loo and Ruey Long Cheu
Abstract:
This paper proposes a new approach to Automatic Incident Detection on
traffic highways using Particle Swarm Optimization (PSO). The rampant
growth in traffic incidents, which is high cost incurring, has led to
significant interest in the development of effective incident
detection techniques in recent years. Various techniques have been
proposed to effectively address this problem, the most promising of
which are Artificial Neural Networks (ANN) based methods.
Back-propagation (BP) has proven to be one of the best methods to
train weights of ANN for incident detection. However it has several
limitations including slow convergence, heuristic determination of
parameters and possibility of getting stuck in a local minima. This
paper overcomes these problems by using particle swarm optimization to
train a neural network in place of BP. Actual data from a highway was
used for training and testing of this method. Simulation results show
that PSO performed better than Back-propagation algorithm.
- Distributed Information
Retrieval and Dissemination in Swarm-Based Networks of Mobile,
Autonomous Agents
William Agassounon
Abstract:
In this paper, we present an optimal design for local explicit
communication and collaboration in a swarm-based, mobile, robotic
system. We propose a methodology for finding the design parameter,
i.e., the communication range, for a fast, distributed information
exchange and dissemination among the teammates, and a reliable and
efficient information retrieval by the base station(s). Our approach
consists of maximizing the interaction/collaboration rate, and hence
the rate of information exchange between the mobile agents in the
presence of destructive interferences. The proposed approach takes
into account the spatial distribution of the agents, which is a
function of the agents’ movement pattern in the environment. We
illustrate the usefulness and efficiency of local interaction between
the teammates and its effect on information retrieval, first by
presenting a collaborative, sensing task consisting of using a team of
autonomous agents with limited sensing capabilities to count some
objects scattered throughout an enclosed region. Then we introduce a
general mathematical methodology for maximizing the collaboration
rate, and hence the rate of information spread in similar networks of
multiple, cooperating mobile agents. We validate our results with a
sensor-based simulator of real, mobile robots.
- Obtaining Subtrees from
Graphs: An Ant Colony Approach
Shekhar Gosavi, Sanjoy Das, Shilpa Vaze, Gurdip Singh, and Erik
Buehler
Abstract:
The ant systems optimization approach is a new method of solving
combinatorial optimization problems. It was originally introduced as a
metaheuristic approach for the well-known traveling salesman problem.
But it was subsequently shown to be an equally effective algorithm for
solving other optimization problems. This article supplies more
results for an extended ant colony algorithm, which can be used to
compute a minimum cost Steiner tree from a graph. In each pass of the
proposed algorithm, ants are placed at the terminal nodes of the
Steiner tree to be computed. They are then allowed to move towards one
another, along the edges of the graph, until they merge into a single
entity. In this process, the paths taken by the ants define a Steiner
tree. Edges receive reinforcement in the form of pheromone deposits
along the paths taken by the ant, that is based on the quality of the
Steiner tree produced. Pheromones eventually accrue most along better
edges. As a result, after several iterations, a good Steiner tree can
be extracted from the deposit. The algorithm can easily be used in
several practical applications.
- Particle Swarm
Optimization-based Approach for Generator Maintenance Scheduling
Chin Aik Koay and Dipti Srinivasan
Abstract:
This paper introduces a particle swarm optimization-based method for
solving a multi-objective generator maintenance scheduling problem
with many constraints. It is shown that particle swarm
optimization-based approach is effective in obtaining feasible
schedules in a reasonable time. Actual data from a practical power
system was used in this study and results were compared against those
from other evolutionary methods on the same set of data.
This paper also introduces a novel concept for spawning and selection
mechanism in a hybrid particle swarm algorithm. The results suggest
that this hybrid model converges to better solution faster than
standard PSO algorithm. It is envisaged that this hybrid approach can
be easily implemented for similar optimization and scheduling problems
to obtain better convergence.
- Fitness-Distance-Ratio Based
Particle Swarm Optimization
Thanmaya Peram, Kalyan Veeramachaneni and Chilukuri K. Mohan
Abstract:
This paper presents a modification of the particle swarm optimization
algorithm (PSO) intended to combat the problem of premature
convergence observed in many applications of PSO. The proposed new
algorithm moves particles towards nearby particles of higher fitness,
instead of attracting each particle towards just the best position
discovered so far by any particle. This is accomplished by using the
ratio of the relative fitness and the distance of other particles to
determine the direction in which each component of the particle
position needs to be changed. The resulting algorithm (FDR-PSO) is
shown to perform significantly better than the original PSO algorithm
and some of its variants, on many different benchmark optimization
problems. Empirical examination of the evolution of the particles
demonstrates that the convergence of the algorithm does not occur at
an early phase of particle evolution, unlike PSO. Avoiding premature
convergence allows FDR-PSO to continue search for global optima in
difficult multimodal optimization problems.
- PSOt - a Particle Swarm
Optimization Toolbox for use with Matlab
Brian Birge
Abstract:
A Particle Swarm Optimization Toolbox (PSOt) for use with the Matlab
scientific programming environment has been developed. PSO is
introduced briefly and then the use of the toolbox is explained with
some examples. A link to downloadable code is provided.
- Using Cultural Algorithms in
Industry
Nestor Rychtyckyj, David Ostrowski, George Schleis, and Robert G.
Reynolds
Abstract:
Evolutionary computation has been successfully applied in a variety of
problem domains and applications. In this paper we discuss the use of
a specific form of evolutionary computation known as Cultural
Algorithms that has been applied successfully in various real-world
applications to solve problems of a very dynamic and complex nature.
Cultural Algorithms[1] introduce a learning component into an
evolutionary framework that influences the search strategy and is in
turn modified by the best-performing members of the population during
the entire process. Cultural Algorithms have been used in various
applications, including fraud analysis for automotive accident claims,
the re-engineering of a dynamic automobile manufacturing knowledge
base, the modeling of pricing strategies for automobiles in a
multi-agent environment and for data mining.
- Particle Swarm with Extended
Memory for Multiobjective Optimization
Xiaohui Hu, Russell C. Eberhart, and Yuhui Shi
Abstract:
This paper presents a modified dynamic neighborhood Particle Swarm
Optimization (DN-PSO) algorithm for multiobjective optimization
problems. PSO is modified by using a dynamic neighborhood strategy,
new particle memory updating, and one-dimension optimization to deal
with multiple objectives. A extended memory is introduced to store
global Pareto optimal solutions to reduce computation time. Several
benchmark cases were tested and the results show that the modified
DNPSO is much more efficient than the original DNPSO and other
multiobjective optimization techniques.
- Cultural Swarms: Modeling the
Impact of Culture on Social Interaction and Problem Solving
Radu Iacoban, Robert G. Reynolds, and Jon Brewster
Abstract:
In this paper we investigate how diverse knowledge sources interact to
direct individuals in a swarm population. We identify three basic
phases of problem solving that are generated by the swarm population
in the solution of real valued function optimization problems. The
question that we are interested in answering is how these phases
derive from the interaction of various sources of cultural knowledge
present in the belief space of a population. We map the central
tendency of the subset of individuals that are influences by each
knowledge source over time at the meta-level. The resultant patterns
suggest the presence of "cultural swarms" where various knowledge
sources take turns at leading and following in the exploration and
exploitation of the problem space. This suggests that the social
interaction of individuals coupled with their interaction with a
culture within which they are embedded provides a powerful vehicle for
the solution of complex problems.
- Cultural Swarms: Assessing
the Impact of Culture on Social Interaction and Problem Solving
Radu Iacoban, Robert G. Reynolds, and Jon Brewster
Abstract:
In this paper we investigate how diverse knowledge sources interact to
direct individuals in a swarm population. We identify three basic
phases of problem solving that are generated by the swarm population
in the solution of real valued function optimization problems. The
question that we are interested in answering is how these phases
derive from the interaction of various sources of cultural knowledge
present in the belief space of a population. We map the central
tendency of the subset of individuals that are influenced by each
knowledge source over time at the meta-level. The resultant patterns
suggest the presence of "cultural swarms" where various knowledge
sources take turns at leading and following in the exploration and
exploitation of the problem space. This implies that the social
interaction of individuals coupled with their interaction with a
culture within which they are embedded provides a powerful vehicle for
the solution of complex problems.
- Visualizing Particle Swarm
Optimization - Gaussian Particle Swarm Optimization
Barry R. Secrest and Gary B. Lamont
Abstract:
Particle Swarm Optimization (PSO) conjures an image of particles
searching for the optima the way bees buzz around flowers. One
approach at visualizing the swarm graphs where all the particles are
each generation, thus demonstrating the random nature associated with
swarms of insects. Another approach is to show successive bests, thus
showing the way that the swarm progresses. Some have even looked at
the specific search path of the particle that eventually finds the
optima. These approaches provide limited understanding of PSO. This
paper presents a new visualization approach based on the probability
distribution of the swarm, thus the random nature of PSO is properly
visualized. The visualization allows better understanding of how to
tune the algorithm and depicts weaknesses. A new algorithm based on
moving the swarm a Gaussian distance from the global and local best is
presented. Gaussian Particle Swarm Optimization (GPSO) is compared to
PSO.
- Prediction of Surface Roughness in End Milling using Swarm
Intelligence
Hazim El-Mounayri, Zakir Dugla, and Haiyan Deng
Abstract:
A new technique from EC (Evolutionary Computation), PSO (Particle
Swarm Optimization), is implemented to model the end milling process
and predict the resulting surface roughness. Data collected from CNC
cutting experiments using DOE approach. Data used for model
calibration and validation. The inputs to the model consist of Feed,
Speed and Depth of cut while the output from the model is surface
roughness. The model is validated through a comparison of the
experimental values with their predicted counterparts. A good
agreement is found. The proved technique opens the door for a new,
simple and efficient approach that could be applied to the calibration
of other empirical models of machining.
- Scalability of Niche PSO
R. Brits, A. P. Engelbrecht, and F. van den Bergh
Abstract:
In contrast to optimization techniques intended to find a single,
global solution in a problem domain, niching (speciation) techniques
have the ability to locate multiple solutions in multimodal domains.
Numerous niching techniques have been proposed, broadly classified as
temporal (locating solutions sequentially) and parallel (multiple
solutions are found concurrently) techniques. Most research efforts to
date have considered niching solutions through the eyes of genetic
algorithms (GAs), studying simple multimodal problems. Little
attention has been given to the possibilities associated with emergent
swarm intelligence techniques. Particle swarm optimization (PSO)
utilizes properties of swarm behaviour not present in evolutionary
algorithms such as GAs, to rapidly solve optimization problems. This
paper investigates the ability of two genetic algorithm niching
techniques, sequential niching and deterministic crowding, to scale to
higher dimensional domains with large numbers of solutions, and
compare their performance to a PSO-based niching technique, NichePSO.
- Using Neighbourhoods with the
Guaranteed Convergence PSO
E.S. Peer, F. van den Bergh, and A.P. Engelbrecht
Abstract:
The standard Particle Swarm Optimiser (PSO) may prematurely converge
on suboptimal solutions that are not even guaranteed to be local
extrema. The guaranteed convergence modifications to the PSO algorithm
ensure that the PSO at least converges on a local extremum at the
expense of even faster convergence. This faster convergence means that
less of the search space is explored reducing the opportunity of the
swarm to find better local extrema. Various neighbourhood topologies
inhibit premature convergence by preserving swarm diversity during the
search. This paper investigates the performance of the Guaranteed
Convergence PSO (GCPSO) using different neighbourhood topologies and
compares the results with their standard PSO counterparts.
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