42,073 research outputs found
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
Cuckoo Search: A Brief Literature Review
Cuckoo search (CS) was introduced in 2009, and it has attracted great
attention due to its promising efficiency in solving many optimization problems
and real-world applications. In the last few years, many papers have been
published regarding cuckoo search, and the relevant literature has expanded
significantly. This chapter summarizes briefly the majority of the literature
about cuckoo search in peer-reviewed journals and conferences found so far.
These references can be systematically classified into appropriate categories,
which can be used as a basis for further research.Comment: 14 pages 3 figure
Using the quaternion's representation of individuals in swarm intelligence and evolutionary computation
This paper introduces a novel idea for representation of individuals using
quaternions in swarm intelligence and evolutionary algorithms. Quaternions are
a number system, which extends complex numbers. They are successfully applied
to problems of theoretical physics and to those areas needing fast rotation
calculations. We propose the application of quaternions in optimization, more
precisely, we have been using quaternions for representation of individuals in
Bat algorithm. The preliminary results of our experiments when optimizing a
test-suite consisting of ten standard functions showed that this new algorithm
significantly improved the results of the original Bat algorithm. Moreover, the
obtained results are comparable with other swarm intelligence and evolutionary
algorithms, like the artificial bees colony, and differential evolution. We
believe that this representation could also be successfully applied to other
swarm intelligence and evolutionary algorithms.Comment: Technical Report on Faculty of Electrical Engineering and Computer
Science, Maribor, Slovenia, 201
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
Particle Swarm Optimization (PSO) is a metaheuristic global optimization
paradigm that has gained prominence in the last two decades due to its ease of
application in unsupervised, complex multidimensional problems which cannot be
solved using traditional deterministic algorithms. The canonical particle swarm
optimizer is based on the flocking behavior and social co-operation of birds
and fish schools and draws heavily from the evolutionary behavior of these
organisms. This paper serves to provide a thorough survey of the PSO algorithm
with special emphasis on the development, deployment and improvements of its
most basic as well as some of the state-of-the-art implementations. Concepts
and directions on choosing the inertia weight, constriction factor, cognition
and social weights and perspectives on convergence, parallelization, elitism,
niching and discrete optimization as well as neighborhood topologies are
outlined. Hybridization attempts with other evolutionary and swarm paradigms in
selected applications are covered and an up-to-date review is put forward for
the interested reader.Comment: 34 pages, 7 table
Microstrip Coupler Design Using Bat Algorithm
Evolutionary and swarm algorithms have found many applications in design
problems since todays computing power enables these algorithms to find
solutions to complicated design problems very fast. Newly proposed hybrid
algorithm, bat algorithm, has been applied for the design of microwave
microstrip couplers for the first time. Simulation results indicate that the
bat algorithm is a very fast algorithm and it produces very reliable results.Comment: 7 pages, 4 figures, 1 tabl
Improved Local Search in Artificial Bee Colony using Golden Section Search
Artificial bee colony (ABC), an optimization algorithm is a recent addition
to the family of population based search algorithm. ABC has taken its
inspiration from the collective intelligent foraging behavior of honey bees. In
this study we have incorporated golden section search mechanism in the
structure of basic ABC to improve the global convergence and prevent to stick
on a local solution. The proposed variant is termed as ILS-ABC. Comparative
numerical results with the state-of-art algorithms show the performance of the
proposal when applied to the set of unconstrained engineering design problems.
The simulated results show that the proposed variant can be successfully
applied to solve real life problems.Comment: 6 Pages, Journal of Engineering (JOE), World Science Publisher 201
State-of-the-Art Economic Load Dispatch of Power Systems Using Particle Swarm Optimization
Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one
of the most promising optimization techniques in solving highly constrained
non-linear and non-convex optimization problems in different areas of
electrical engineering. Economic operation of the power system is one of the
most important areas of electrical engineering where PSO has been used
efficiently in solving various issues of practical systems. In this paper, a
comprehensive survey of research works in solving various aspects of economic
load dispatch (ELD) problems of power system engineering using different types
of PSO algorithms is presented. Five important areas of ELD problems have been
identified, and the papers published in the general area of ELD using PSO have
been classified into these five sections. These five areas are (i) single
objective economic load dispatch, (ii) dynamic economic load dispatch, (iii)
economic load dispatch with non-conventional sources, (iv) multi-objective
environmental/economic dispatch, and (v) economic load dispatch of microgrids.
At the end of each category, a table is provided which describes the main
features of the papers in brief. The promising future works are given at the
conclusion of the review
Heuristic Optimization of Electrical Energy Systems: Refined Metrics to Compare the Solutions
Many optimization problems admit a number of local optima, among which there
is the global optimum. For these problems, various heuristic optimization
methods have been proposed. Comparing the results of these solvers requires the
definition of suitable metrics. In the electrical energy systems literature,
simple metrics such as best value obtained, the mean value, the median or the
standard deviation of the solutions are still used. However, the comparisons
carried out with these metrics are rather weak, and on these bases a somehow
uncontrolled proliferation of heuristic solvers is taking place. This paper
addresses the overall issue of understanding the reasons of this proliferation,
showing a conceptual scheme that indicates how the assessment of the best
solver may result in the unlimited formulation of new solvers. Moreover, this
paper shows how the use of more refined metrics defined to compare the
optimization result, associated with the definition of appropriate benchmarks,
may make the comparisons among the solvers more robust. The proposed metrics
are based on the concept of first-order stochastic dominance and are defined
for the cases in which: (i) the globally optimal solution can be found (for
testing purposes); and (ii) the number of possible solutions is so large that
practically it cannot be guaranteed that the global optimum has been found.
Illustrative examples are provided for a typical problem in the electrical
energy systems area-distribution network reconfiguration. The conceptual
results obtained are generally valid to compare the results of other
optimization problems.Comment: Previous title: Heuristic Optimization of Electrical Energy Systems:
A Perpetual Motion Scheme and Refined Metrics to Compare the Solution
Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective
Artificial Intelligence has an important place in the scientific community as
a result of its successful outputs in terms of different fields. In time, the
field of Artificial Intelligence has been divided into many sub-fields because
of increasing number of different solution approaches, methods, and techniques.
Machine Learning has the most remarkable role with its functions to learn from
samples from the environment. On the other hand, intelligent optimization done
by inspiring from nature and swarms had its own unique scientific literature,
with effective solutions provided for optimization problems from different
fields. Because intelligent optimization can be applied in different fields
effectively, this study aims to provide a general discussion on
multidisciplinary effects of Artificial Intelligence by considering its
optimization oriented solutions. The study briefly focuses on background of the
intelligent optimization briefly and then gives application examples of
intelligent optimization from a multidisciplinary perspective.Comment: 9 page
Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem
Optimization problems in design engineering are complex by nature, often
because of the involvement of critical objective functions accompanied by a
number of rigid constraints associated with the products involved. One such
problem is Economic Load Dispatch (ED) problem which focuses on the
optimization of the fuel cost while satisfying some system constraints.
Classical optimization algorithms are not sufficient and also inefficient for
the ED problem involving highly nonlinear, and non-convex functions both in the
objective and in the constraints. This led to the development of metaheuristic
optimization approaches which can solve the ED problem almost efficiently. This
paper presents a novel robust plant intelligence based Adaptive Plant
Propagation Algorithm (APPA) which is used to solve the classical ED problem.
The application of the proposed method to the 3-generator and 6-generator
systems shows the efficiency and robustness of the proposed algorithm. A
comparative study with another state-of-the-art algorithm (APSO) demonstrates
the quality of the solution achieved by the proposed method along with the
convergence characteristics of the proposed approach.Comment: 11 pages, 2 figure
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