9,388 research outputs found
A review of velocity-type PSO variants
This paper presents a review of the particular variants of particle swarm optimization, based on the velocity-type class.
The original particle swarm optimization algorithm was developed as an unconstrained optimization technique, which
lacks a model that is able to handle constrained optimization problems. The particle swarm optimization and its
inapplicability in constrained optimization problems are solved using the dynamic-objective constraint-handling
method. The dynamic-objective constraint-handling method is originally developed for two variants of the basic particle
swarm optimization, namely restricted velocity particle swarm optimization and self-adaptive velocity particle swarm
optimization. Also on the subject velocity-type class, a review of three other variants is given, specifically: (1) vertical
particle swarm optimization; (2) velocity limited particle swarm optimization; and (3) particle swarm optimization with
scape velocity. These velocity-type particle swarm optimization variants all have in common a velocity parameter which
determines the direction/movements of the particles.info:eu-repo/semantics/publishedVersio
A Practical Approach for the Auto-tuning of PD Controllers for Robotic Manipulators using Particle Swarm Optimization
An auto-tuning method of PD controllers for robotic manipulators is proposed. This method suggests a practical implementation of the particle swarm optimization technique in order to find optimal gain values achieving the best tracking of a predefined position trajectory. For this purpose, The integral of the absolute error IAE is used as a cost function for the optimization algorithm. The optimization is achieved by performing the desired movement of the robot iteratively and evaluating the cost function for every iteration. Therefor, the necessary constraints that guarantee a safe and stable movement of the robot are defined, which are: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. A constraint handling approach is suggested for the optimization algorithm in order to adapt it to the problem in hand. Finally, the efficiency of the proposed method is verified through a practical experiment on a real robot
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Gravitational Swarm Optimizer for Global Optimization
In this article, a new meta-heuristic method is proposed by combining particle swarm optimization (PSO)
and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the article. The e�fficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs e�fficiently with good results as a constrained optimizer
A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems
Distributed Constraint Optimization Problems (DCOPs) are a widely studied
constraint handling framework. The objective of a DCOP algorithm is to optimize
a global objective function that can be described as the aggregation of a
number of distributed constraint cost functions. In a DCOP, each of these
functions is defined by a set of discrete variables. However, in many
applications, such as target tracking or sleep scheduling in sensor networks,
continuous valued variables are more suited than the discrete ones. Considering
this, Functional DCOPs (F-DCOPs) have been proposed that is able to explicitly
model a problem containing continuous variables. Nevertheless, the
state-of-the-art F-DCOPs approaches experience onerous memory or computation
overhead. To address this issue, we propose a new F-DCOP algorithm, namely
Particle Swarm Based F-DCOP (PFD), which is inspired by a meta-heuristic,
Particle Swarm Optimization (PSO). Although it has been successfully applied to
many continuous optimization problems, the potential of PSO has not been
utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution
construction while significantly reducing the computation and memory
requirements. Moreover, we theoretically prove that PFD is an anytime
algorithm. Finally, our empirical results indicate that PFD outperforms the
state-of-the-art approaches in terms of solution quality and computation
overhead
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