10,192 research outputs found
Fish School Search Algorithm for Constrained Optimization
In this work we investigate the effectiveness of the application of niching
able swarm metaheuristic approaches in order to solve constrained optimization
problems. Sub-swarms are used in order to allow the achievement of many
feasible regions to be exploited in terms of fitness function. The niching
approach employed was wFSS, a version of the Fish School Search algorithm
devised specifically to deal with multi-modal search spaces. A base technique
referred as wrFSS was conceived and three variations applying different
constraint handling procedures were also proposed. Tests were performed in
seven problems from CEC 2010 and a comparison with other approaches was carried
out. Results show that the search strategy proposed is able to handle some
heavily constrained problems and achieve results comparable to the
state-of-the-art algorithms. However, we also observed that the local search
operator present in wFSS and inherited by wrFSS makes the fitness convergence
difficult when the feasible region presents some specific geometrical features
A Brief Review of Nature-Inspired Algorithms for Optimization
Swarm intelligence and bio-inspired algorithms form a hot topic in the
developments of new algorithms inspired by nature. These nature-inspired
metaheuristic algorithms can be based on swarm intelligence, biological
systems, physical and chemical systems. Therefore, these algorithms can be
called swarm-intelligence-based, bio-inspired, physics-based and
chemistry-based, depending on the sources of inspiration. Though not all of
them are efficient, a few algorithms have proved to be very efficient and thus
have become popular tools for solving real-world problems. Some algorithms are
insufficiently studied. The purpose of this review is to present a relatively
comprehensive list of all the algorithms in the literature, so as to inspire
further research
Social cognitive optimization with tent map for combined heat and power economic dispatch
Combined heat and power economic dispatch (CHPED) problem is a sophisticated
constrained nonlinear optimization problem in a heat and power production
system for assigning heat and power production to minimize the production
costs. To address this challenging problem, a novel social cognitive
optimization algorithm with tent map (TSCO) is presented for solving the CHPED
problem. To handle the equality constraints in heat and power balance
constraints, adaptive constraints relaxing rule is adopted in constraint
processing. The novelty of our work lies in the introduction of a new powerful
TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of
the presented algorithm is validated by conducting 2 typical CHPED cases. The
numerical results show that the proposed approach has better convergence speed
and solution quality than all other existing state-of-the-art algorithms.Comment: Accepted by International Transactions on Electrical Energy System
The Minimum Spanning Tree of Maximum Entropy
In computer vision, we have the problem of creating graphs out of
unstructured point-sets, i.e. the data graph. A common approach for this
problem consists of building a triangulation which might not always lead to the
best solution. Small changes in the location of the points might generate
graphs with unstable configurations and the topology of the graph could change
significantly. After building the data-graph, one could apply Graph Matching
techniques to register the original point-sets. In this paper, we propose a
data graph technique based on the Minimum Spanning Tree of Maximum Entropty
(MSTME). We aim at a data graph construction which could be more stable than
the Delaunay triangulation with respect to small variations in the neighborhood
of points. Our technique aims at creating data graphs which could help the
point-set registration process. We propose an algorithm with a single free
parameter that weighs the importance between the total weight cost and the
entropy of the current spanning tree. We compare our algorithm on a number of
different databases with the Delaunay triangulation.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Network Routing Optimization Using Swarm Intelligence
The aim of this paper is to highlight and explore a traditional problem,
which is the minimum spanning tree, and finding the shortest-path in network
routing, by using Swarm Intelligence. This work to be considered as an
investigation topic with combination between operations research, discrete
mathematics, and evolutionary computing aiming to solve one of networking
problems.Comment: 10 Page
Swarm Intelligence
Biologically inspired computing is an area of computer science which uses the
advantageous properties of biological systems. It is the amalgamation of
computational intelligence and collective intelligence. Biologically inspired
mechanisms have already proved successful in achieving major advances in a wide
range of problems in computing and communication systems. The consortium of
bio-inspired computing are artificial neural networks, evolutionary algorithms,
swarm intelligence, artificial immune systems, fractal geometry, DNA computing
and quantum computing, etc. This article gives an introduction to swarm
intelligence
Characterizing the Social Interactions in the Artificial Bee Colony Algorithm
Computational swarm intelligence consists of multiple artificial simple
agents exchanging information while exploring a search space. Despite a rich
literature in the field, with works improving old approaches and proposing new
ones, the mechanism by which complex behavior emerges in these systems is still
not well understood. This literature gap hinders the researchers' ability to
deal with known problems in swarms intelligence such as premature convergence,
and the balance of coordination and diversity among agents. Recent advances in
the literature, however, have proposed to study these systems via the network
that emerges from the social interactions within the swarm (i.e., the
interaction network). In our work, we propose a definition of the interaction
network for the Artificial Bee Colony (ABC) algorithm. With our approach, we
captured striking idiosyncrasies of the algorithm. We uncovered the different
patterns of social interactions that emerge from each type of bee, revealing
the importance of the bees variations throughout the iterations of the
algorithm. We found that ABC exhibits a dynamic information flow through the
use of different bees but lacks continuous coordination between the agents.Comment: 9 pages, 10 figure
Hybrid data clustering approach using K-Means and Flower Pollination Algorithm
Data clustering is a technique for clustering set of objects into known
number of groups. Several approaches are widely applied to data clustering so
that objects within the clusters are similar and objects in different clusters
are far away from each other. K-Means, is one of the familiar center based
clustering algorithms since implementation is very easy and fast convergence.
However, K-Means algorithm suffers from initialization, hence trapped in local
optima. Flower Pollination Algorithm (FPA) is the global optimization
technique, which avoids trapping in local optimum solution. In this paper, a
novel hybrid data clustering approach using Flower Pollination Algorithm and
K-Means (FPAKM) is proposed. The proposed algorithm results are compared with
K-Means and FPA on eight datasets. From the experimental results, FPAKM is
better than FPA and K-Means.Comment: 11 pages, Journal. Advanced Computational Intelligence: An
International Journal (ACII), Vol.2, No.2, April 201
Active Animations of Reduced Deformable Models with Environment Interactions
We present an efficient spacetime optimization method to automatically
generate animations for a general volumetric, elastically deformable body. Our
approach can model the interactions between the body and the environment and
automatically generate active animations. We model the frictional contact
forces using contact invariant optimization and the fluid drag forces using a
simplified model. To handle complex objects, we use a reduced deformable model
and present a novel hybrid optimizer to search for the local minima
efficiently. This allows us to use long-horizon motion planning to
automatically generate animations such as walking, jumping, swimming, and
rolling. We evaluate the approach on different shapes and animations, including
deformable body navigation and combining with an open-loop controller for
realtime forward simulation.Comment: 17 page
Improvement of PSO algorithm by memory based gradient search - application in inventory management
Advanced inventory management in complex supply chains requires effective and
robust nonlinear optimization due to the stochastic nature of supply and demand
variations. Application of estimated gradients can boost up the convergence of
Particle Swarm Optimization (PSO) algorithm but classical gradient calculation
cannot be applied to stochastic and uncertain systems. In these situations
Monte-Carlo (MC) simulation can be applied to determine the gradient. We
developed a memory based algorithm where instead of generating and evaluating
new simulated samples the stored and shared former function evaluations of the
particles are sampled to estimate the gradients by local weighted least squares
regression. The performance of the resulted regional gradient-based PSO is
verified by several benchmark problems and in a complex application example
where optimal reorder points of a supply chain are determined.Comment: book chapter, 20 pages, 7 figures, 2 table
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