14,650 research outputs found
Feedback learning particle swarm optimization
This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published and is available at the link below - Copyright @ Elsevier 2011In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle’s history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund
for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany
Algorithms Inspired by Nature: A Survey
Nature is known to be the best optimizer. Natural processes most often than
not reach an optimal equilibrium. Scientists have always strived to understand
and model such processes.Thus, many algorithms exist today that are inspired by
nature. Many of these algorithms and heuristics can be used to solve problems
for which no polynomial time algorithms exist,such as Job Shop Scheduling and
many other Combinatorial Optimization problems. We will discuss some of these
algorithms and heuristics and how they help us solve complex problems of
practical importance
Improving PSO Global Method for Feature Selection According to Iterations Global Search and Chaotic Theory
Making a simple model by choosing a limited number of features with the
purpose of reducing the computational complexity of the algorithms involved in
classification is one of the main issues in machine learning and data mining.
The aim of Feature Selection (FS) is to reduce the number of redundant and
irrelevant features and improve the accuracy of classification in a data set.
We propose an efficient ISPSO-GLOBAL (Improved Seeding Particle Swarm
Optimization GLOBAL) method which investigates the specified iterations to
produce prominent features and store them in storage list. The goal is to find
informative features based on its iteration frequency with favorable fitness
for the next generation and high exploration. Our method exploits of a new
initialization strategy in PSO which improves space search and utilizes chaos
theory to enhance the population initialization, then we offer a new formula to
determine the features size used in proposed method. Our experiments with
real-world data sets show that the performance of the ISPSO-GLOBAL is superior
comparing with state-of-the-art methods in most of the data sets
Controller design for synchronization of an array of delayed neural networks using a controllable
This is the post-print version of the Article - Copyright @ 2011 ElsevierIn this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation
Project of Shanghai(Grant No 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the U.K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
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
A new SSO-based Algorithm for the Bi-Objective Time-constrained task Scheduling Problem in Cloud Computing Services
Cloud computing distributes computing tasks across numerous distributed
resources for large-scale calculation. The task scheduling problem is a
long-standing problem in cloud-computing services with the purpose of
determining the quality, availability, reliability, and ability of the cloud
computing. This paper is an extension and a correction to our previous
conference paper entitled Multi Objective Scheduling in Cloud Computing Using
MOSSO published in 2018 IEEE Congress on Evolutionary Computation. More new
algorithms, testing, and comparisons have been implemented to solve the
bi-objective time-constrained task scheduling problem in a more efficient
manner. Furthermore, this paper developed a new SSO-based algorithm called the
bi-objective simplified swarm optimization to fix the error in previous
SSO-based algorithm to address the task-scheduling problem. From the results
obtained from the new experiments conducted, the proposed BSSO outperforms
existing famous algorithms, e.g., NSGA-II, MOPSO, and MOSSO in the convergence,
diversity, number of obtained temporary nondominated solutions, and the number
of obtained real nondominated solutions. The results propound that the proposed
BSSO can successfully achieve the aim of this work
Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner
tree problem, an NP-hard graph problem related to the minimum labelling spanning tree
problem. Given an undirected labelled connected graph, the aim is to find a spanning
tree covering a given subset of nodes of the graph, whose edges have the smallest number
of distinct labels. Such a model may be used to represent many real world problems in
telecommunications and multimodal transportation networks. Several metaheuristics are
proposed and evaluated. The approaches are compared to the widely adopted Pilot Method
and it is shown that the Variable Neighbourhood Search metaheuristic is the most effective
approach to the problem, obtaining high quality solutions in short computational running
times
Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling
Broadcasting antenna array null filling is a very
challenging problem for antenna design optimization. This paper
compares five antenna design optimization algorithms (Differential
Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive
Invasive Weed) as solutions to the antenna array null filling
problem. The algorithms compared are evolutionary algorithms
which use mechanisms inspired by biological evolution, such as
reproduction, mutation, recombination, and selection. The focus of
the comparison is given to the algorithm with the best results,
nevertheless, it becomes obvious that the algorithm which produces
the best fitness (Invasive Weed Optimization) requires very
substantial computational resources due to its random search
nature
Enhanced Estimation of Autoregressive Wind Power Prediction Model Using Constriction Factor Particle Swarm Optimization
Accurate forecasting is important for cost-effective and efficient monitoring
and control of the renewable energy based power generation. Wind based power is
one of the most difficult energy to predict accurately, due to the widely
varying and unpredictable nature of wind energy. Although Autoregressive (AR)
techniques have been widely used to create wind power models, they have shown
limited accuracy in forecasting, as well as difficulty in determining the
correct parameters for an optimized AR model. In this paper, Constriction
Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine
the parameters of an Autoregressive (AR) model for accurate prediction of the
wind power output behaviour. Appropriate lag order of the proposed model is
selected based on Akaike information criterion. The performance of the proposed
PSO based AR model is compared with four well-established approaches;
Forward-backward approach, Geometric lattice approach, Least-squares approach
and Yule-Walker approach, that are widely used for error minimization of the AR
model. To validate the proposed approach, real-life wind power data of
\textit{Capital Wind Farm} was obtained from Australian Energy Market Operator.
Experimental evaluation based on a number of different datasets demonstrate
that the performance of the AR model is significantly improved compared with
benchmark methods.Comment: The 9th IEEE Conference on Industrial Electronics and Applications
(ICIEA) 201
LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques
Optimization techniques play an important role in several scientific and
real-world applications, thus becoming of great interest for the community. As
a consequence, a number of open-source libraries are available in the
literature, which ends up fostering the research and development of new
techniques and applications. In this work, we present a new library for the
implementation and fast prototyping of nature-inspired techniques called
LibOPT. Currently, the library implements 15 techniques and 112 benchmarking
functions, as well as it also supports 11 hypercomplex-based optimization
approaches, which makes it one of the first of its kind. We showed how one can
easily use and also implement new techniques in LibOPT under the C paradigm.
Examples are provided with samples of source-code using benchmarking functions
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