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Particle Swarm Optimization (PSO) has recently emerged as a nature inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by adding a new coefficient to the position updating equation and modulating the inertia weight. This work also mathematically analyzes the effect of this modification on the PSO algorithm...
In this article we consider a particle swarm optimization (PSO) algorithm in which the neighbors of the particles or basically the neighborhood topology dynamically changes with time. We consider probabilistic and distance based approaches for determining the neighbors of the particles and represent the dynamic neighborhood topology by a time varying graph. Simulations of several benchmark functions...
A study is presented on the application of particle swarm optimization (PSO) to design intelligent autopilots for ship steering. Two versions of PSO-conventional and anti-predatory (APSO) - have been used. The autopilot consists of a fuzzy logic controller (FLC) emulating the characteristics of manual ship steering. The parameters for the FLC are optimized using PSO and APSO. The robustness of the...
Particle swarm optimization (PSO) has been widely used to solve unconstrained optimization problems. However, problems in hyper dimensional spaces require the development of enhanced issues. For this, some variations of the original PSO form have been proposed, mainly concerning on the velocity update equation and sophisticated communication topologies of the swarm. In this paper, we propose a PSO...
This paper modified the structure of the original PSO algorithm. It proposes that the particles' position have relationship with the one particle's and the whole swarm's perceive extent in the processing of this time and last time, and presents the inertial weight based on simulated annealing temperature. So a new Particle Swarm Optimization algorithm (NPSO) is proposed. It can not improve the one...
Multilevel inverters supplied from equal and constant dc sources almost donpsilat exist in practical applications. The variation of the dc sources affects the values of the switching angles required for each specific harmonic profile, as well as increases the difficulty of the harmonic eliminationpsilas equations. This paper presents an extremely fast optimal solution of harmonic elimination of multilevel...
Based on the principle of the maximum economic benefits can be obtained by optimizing the project network schedule, the comprehensive optimization of the cost, the supply of resources and the influence of latent uncertainties are considered systematically, and a mathematical model under multi-goal conditions is proposed in this paper. Then by integrating niche technology and using dynamic inertia...
In this paper, a new kind of FPSO is proposed, called convergent fuzzy particle swarm optimization (CFPSO) to enhance the convergent performance. A convergent gene is introduced in the velocity equation of the FPSO, thus it turns to CFPSO. And it differs from convergent particle swarm optimization (CPSO) in that it employs the fuzzy membership function in the velocity equation. The CFPSO performance...
A preferable value for parameters proved to be crucial in enhancing the performance and efficiency of particle swarm optimization (PSO) algorithm. To provide good solution for reasonable choice of parameter values within fairly wide range for particle swarm optimization, this paper presents a novel parameter optimizing configuration strategy based on multi-order rhombus thought (MRT), which depends...
The excavation and construction of underground engineering is a dynamically adjusting process of system. The paper starts with the index of surrounding rock displacement which can reflect both observability and controllability of underground engineer system. The nonlinear machine learning tool - support vector machine (SVM) which based on statistic learning theory is utilized to construct the time...
The Software Development Project Scheduling Problem is similar to the well-known Resource-Constrained Multi-Project Scheduling Problem (RCMPSP). It consists in determining a schedule of tasks taking into consideration resource availabilities and precedence constraints, while optimizing an objective. Like RCMPSP, it is an NP-hard problem. In this paper, a task segmentation scheme to schedule a software...
This paper evaluates the performance of three Particle Swarm Optimization (PSO) algorithms, namely attraction-repulsion based PSO (ATREPSO), Quadratic Interpolation based PSO (QIPSO) and Gaussian Mutation based PSO (GMPSO). Whereas all the algorithms are guided by the diversity of the population to search the global optimal solution of a given optimization problem, GMPSO uses the concept of mutation...
Fuzzy particle swarm optimization (FPSO) has shown its great searching ability and high computing precision, while it can not assure the algorithm is convergent. In this paper, a new kind of FPSO is proposed, called convergent fuzzy particle swarm optimization (CFPSO), employing the convergent gene. It differs from normal FPSO in that a convergent gene is introduced in the velocity equation. And it...
As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for particle swarm optimizer (EPUS-PSO). This is achieved by using variable...
In order to overcome the disadvantage that only one solution can be found in particle swarm optimization (PSO), a novel niche particle swarm multi_optimizer (multi_PSOer) which combines two strategies is devised in this paper. Firstly, guaranteed convergence PSO (GCPSO) is adopted to guarantee the algorithm can converge on a local point. Secondly, niche technique is used to ensure the algorithm is...
This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until the stop condition is satisfied. At the same time,...
The particle swarm optimization algorithm (PSO) has successfully been applied to many engineering optimization problems. However, most of the existing improved PSO algorithms work well only for small-scale problems. In this new self-adaptive PSO, a special function, which is defined in terms of the particle fitness and swarm size, is introduced to adjust the inertia weight adaptively. In a given generation,...
PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation...
The difficulty for accurate determination of the angles of arrival (AOA) of signals arises from the optimization of likelihood functions of high dimension. Usually, a gradient-based technique is employed to find the optimum of the function. However, this method requires heavy computational work and the differentiability of the likelihood function. This paper presents two gradient-free methods: One...
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