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A modified particle swarm optimization via particle visual modeling analysis
A particle is treated as a whole individual in all researches on particle swarm optimization
(PSO) currently, these are not concerned with the information of every particle's
dimensional vector. A visual modeling method describing particle's dimensional vector
behavior is presented in this paper. Based on the analysis of visual modeling, the
reason for premature convergence and diversity loss in PSO is explained, and a new
modified algorithm is proposed to ensure the rational flight of every particle's dimensional
component. Meanwhile, two parameters of particle-distribution-degree and particle-
dimension-distance are introduced into the proposed algorithm in order to avoid
premature convergence. Simulation results of the new PSO algorithm show that it has a
better ability of finding the global optimum, and still keeps a rapid convergence as with
the standard PSO.
2010-05-09
A hybrid simplex search and particle swarm optimization for unconstrained optimization
This paper proposes the hybrid NM-PSO algorithm based on the Nelder–Mead (NM) simplex search method and particle
swarm optimization (PSO) for unconstrained optimization. NM-PSO is very easy to implement in practice since it
does not require gradient computation. The modification of both the Nelder–Mead simplex search method and particle
swarm optimization intends to produce faster and more accurate convergence. The main purpose of the paper is to demonstrate
how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy. In a suite
of 20 test function problems taken from the literature, computational results via a comprehensive experimental study, preceded
by the investigation of parameter selection, show that the hybrid NM-PSO approach outperforms other three relevant
search techniques (i.e., the original NM simplex search method, the original PSO and the guaranteed convergence
particle swarm optimization (GCPSO)) in terms of solution quality and convergence rate. In a later part of the comparative
experiment, the NM-PSO algorithm is compared to various most up-to-date cooperative PSO (CPSO) procedures
appearing in the literature. The comparison report still largely favors the NM-PSO algorithm in the performance of accuracy,
robustness and function evaluation. As evidenced by the overall assessment based on two kinds of computational
experience, the new algorithm has demonstrated to be extremely effective and efficient at locating best-practice optimal
solutions for unconstrained optimization.
2010-05-09
A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training
The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search,
but around global optimum, the search process will become very slow. On the contrary, the gradient descending method
can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher.
So in this paper, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP)
algorithm, also referred to as PSO–BP algorithm, is proposed to train the weights of feedforward neural network (FNN),
the hybrid algorithm can make use of not only strong global searching ability of the PSOA, but also strong local searching
ability of the BP algorithm. In this paper, a novel selection strategy of the inertial weight is introduced to the PSO algorithm.
In the proposed PSO–BP algorithm, we adopt a heuristic way to give a transition from particle swarm search to
gradient descending search. In this paper, we also give three kind of encoding strategy of particles, and give the different
problem area in which every encoding strategy is used. The experimental results show that the proposed hybrid PSO–BP
algorithm is better than the Adaptive Particle swarm optimization algorithm (APSOA) and BP algorithm in convergent
speed and convergent accuracy.
2010-05-09
A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems
In a distributed system, a number of application tasks may need to be assigned to different processors such that the system
cost is minimized and the constraints with limited resource are satisfied. Most of the existing formulations for this problem have
been found to be NP-complete, and thus finding the exact solutions is computationally intractable for large-scaled problems.
This paper presents a hybrid particle swarm optimization algorithm for finding the near optimal task assignment with reasonable
time. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also,
our method converges at a fast rate and is suited to large-scaled task assignment problems.
2010-05-09
A hybrid Particle Swarm Optimization – Simplex algorithm (PSOS) for structural damage identification
This study proposes a new PSOS-model based damage identification procedure using frequency domain
data. The formulation of the objective function for the minimization problem is based on the Frequency
Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization
(PSO) parameters based on the Nelder–Mead algorithm (Simplex method) is presented; consequently,
the convergence of the PSOS becomes independent of the heuristic constants and its stability
and confidence are enhanced. The formulated hybrid method performs better in different benchmark
functions than the Simulated Annealing (SA) and the basic PSO (PSOb). Two damage identification problems,
taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss
and second, a cracked free–free beam, both modeled with finite elements. In these cases, the damage
location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator)
was identified by PSOS providing good results.
2010-05-09
A global Particle Swarm-Based-Simulated Annealing Optimization technique for under-voltage load shedding problem
In this paper, a new approach based on hybrid Particle Swarm-Based-Simulated Annealing Optimization
technique (PSO-B-SA) is proposed for solving under-voltage load shedding (UVLS) problem. Undervoltage
load shedding (UVLS) is one of the most important tools for avoiding voltage instability. In this
paper, the UVLS problem is formulated based on the concept of the static voltage stability margin and its
sensitivity at the maximum loading point or the collapse point. The voltage stability criterion is modeled
directly into the load-shedding scheme. In any UVLS scheme finding the global point is very important for
having cost effective economy. The proposed PSO-B-SA methodology is implemented in the undervoltage
load shedding scheme for IEEE 14 and 118 bus test systems. In addition to having better solution,
the global property of the proposed approach plays an important role in on-line applications. Simulation
results show the efficacy and advantage of the proposed scheme
2010-05-09
A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search
This paper integrates Nelder–Mead simplex search method (NM) with genetic algorithm (GA) and particle swarm optimization
(PSO), respectively, in an attempt to locate the global optimal solutions for the nonlinear continuous variable
functions mainly focusing on response surface methodology (RSM). Both the hybrid NM–GA and NM–PSO algorithms
incorporate concepts from the NM, GA or PSO, which are readily to implement in practice and the computation of functional
derivatives is not necessary. The hybrid methods were first illustrated through four test functions from the RSM
literature and were compared with original NM, GA and PSO algorithms. In each test scheme, the effectiveness, efficiency
and robustness of these methods were evaluated via associated performance statistics, and the proposed hybrid approaches
prove to be very suitable for solving the optimization problems of RSM-type. The hybrid methods were then tested by ten
difficult nonlinear continuous functions and were compared with the best known heuristics in the literature. The results
show that both hybrid algorithms were able to reach the global optimum in all runs within a comparably computational
expense.
2010-05-09
A dynamic inertia weight particle swarm optimization algorithm
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was
introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm
(IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases
according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different
dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance
on the benchmark functions significantly.
2010-05-09
A discrete particle swarm optimization algorithm for scheduling parallel machines
As a novel evolutionary technique, particle swarm optimization (PSO) has received increasing attention and
wide applications in a variety of fields. To our knowledge this paper investigates the first application of PSO
algorithm to tackle the parallel machines scheduling problem. Proposing equations analogous to those of
the classical PSO equations, we present a discrete PSO algorithm (DPSO) to minimize makespan (Cmax) criterion.
We also investigate the effectiveness of DPSO algorithm through hybridizing it with an efficient local
search heuristic. To verify the performance of DPSO algorithm and its hybridized version (HDPSO), comparisons
are made through using a recently proposed simulated annealing algorithm for the problem,
addressed in the literature, as a comparator algorithm. Computational results signify that the proposed DPSO
algorithm is very competitive and can be rapidly guided when hybridizing with a local search heuristic.
2010-05-09
A co-evolving framework for robust particle swarm optimization
Particle swarm optimization (PSO) as an efficient and powerful problem-solving strategy has been widely used, but
appropriate adjustment of its parameters usually requires a lot of time and labor. So a co-evolving framework is proposed
to improve the robustness of the PSO. In this paper, within this framework the fuzzy rules for the manipulation of the
inertia weights are co-evolved with the particles. And the simulation results on a suite of test functions show that the
use of this co-evolving framework improves the performance of the PSO, especially the robustness against the dimensional
variation of the test functions.
2010-05-09
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