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This paper considers finite sense ability and finite motion ability into swarm system. We first construct a model of local swarms with a class of attraction and repulsion. Then we analyze their aggregation motion on the basis of the model. It is shown that the individuals of the two swarms will aggregate and eventually enter into a bounded hypeball around the swarm center in finite time. We finally...
Sequence planning of models for a mixed-model assembly line is crucial for the line efficiency. This paper formalized this model sequencing problem based on minimizing the total cost of idle time and overtime. An adapted Particle Swarm Optimization (PSO) algorithm was proposed to optimize the problem. To avoid early convergence of the particles, an immunity mechanism was introduced into the algorithm...
Studies on content-based music retrieval (CBMR) which search music by analyzing their acoustic features and defining their similarity, have been conducted actively. However, it is desirable that the similarity evaluation be adaptive to each user's demand, because the search criteria differs user by user. In this paper, we propose a framework of CBMR that tries to satisfy the various demands of different...
This paper proposed an improved particle swarm optimization (PSO) algorithm called redistributing PSO (RPSO) for designing IIR digital filters. The proposed RPSO avoids the stagnation problem by automatically triggering particles redistributing when premature convergence is detected. Every particle is redistributed either within the whole problem space or around the mean between the global best and...
Genetic algorithm and particle swarm optimization are two methods which can be used to find the global extremum of cost functions. The solely performance of each method and their specific characteristics in finding the global extremum have been giving the idea of hybridization of these two methods to many researchers. In this paper a new hybrid algorithm named Serial Genetic Algorithm and Particle...
With the application of article swarm optimization algorithms (PSO), the function optimization problem of analyzing pumping test data in aquifer to estimate such parameters as transmissivity and storage coefficient was to be solved. With the different number of particles and the initial guessed values of transmissivity, the numerical experiments were conducted to explore the effect of these factors...
This paper describes a method of synthesis of uniform circular array with optimized spacing using three recent search heuristics: Modified Particle Swarm Optimization (MPSO), Differential Evolution (DE) and Artificial Bees Colony (ABC) algorithms. The objective of the work is to generate a pencil beam with minimum Side Lobe Level (SLL) and maximum possible Directivity for fixed half power beam width...
A new hybrid algorithm is introduced into solving job shop scheduling problems, which combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm. By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. Through the combination, better...
The optimal placement of Distributed Generation (DG) has attracted many researchers' attention recently due to its ability to obviate defects caused by improper installation of DG units, such as rise in system losses, decline in power quality, voltage increase at the end of feeders and etc. This paper presents a new advanced method for optimal allocation of DG in distribution systems. In this study,...
The main goal of Transmission Network Expansion Planning (TNEP) is determination of the number, time and location of new lines to be added to transmission network. Up to now, different methods have been used to solve the static TNEP (STNEP). In most of them, this problem is implemented regardless of power loss and the uncertainty in the load demand. With respect to the importance of these two parameters...
In this paper, optimal designs of three-ring concentric circular antenna arrays (CCAA) without and with central element feeding are reported. For each optimal design, optimal current excitation weights and optimal radii are determined having the objective of maximum Sidelobe Level (SLL) reduction. The optimization technique adopted is Particle swarm optimization with constriction factor and inertia...
When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation...
Particle swarm optimization (PSO) is a new swarm intelligence algorithm, derived from artificial life and evolutionary computation theory. It makes full use of the information-sharing particles of the cluster to obtain the optimal solution of the evolution from disorder to orderliness. It has received great concern because of its simple calculation forms, parameter settings and a good convergence...
In this paper, a new kind of quantum particle swarm algorithm for continuous space optimization is proposed, in which quantum computation is introduced into PSO. The particles can be described as superposition of multiple states. Quantum bits are updated by quantum rotation gates, and mutated by quantum non-gates. The numerical simulation results show that the new algorithm has better stability, global...
One of the main obstacles to reliable communications is the inter symbol interference. An adaptive equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. The conventional way to combat with ISI is to include an equalizer in the receiver. This paper presents a new approach to equalization of communication channels...
With the advent of an era of the high fossil energy cost and growing environment consciousness, wind power is gaining great favor over other traditional energy sources. Large scale usage of wind energy can significantly reduce the pollutions and carbon emission otherwise caused by fossil fuel. In many cases, with tax incentive, investment in wind energy can also reduce the operational cost of generating...
To solve the problems of slow convergence and low computational precision of blind source separation(BSS) based on traditional particle swarm optimization(PSO), a novel approach-based adaptive particle swarm optimization for real-time blind source separation is proposed, in which the observations are linear convolutive mixtures of statistically independent speech sources. It combines the independent...
Based on the analysis of inertia weight of the standard PSO, a PSO method is described with self-adaptive stochastic inertia weight based on diversity of individual location and fitness value. Position and fitness value correspond to the axis, based on the difference of location and fitness value from the generation and the current generation to construct a right triangle. It is to modify the inertia...
A new particle swarm optimization (PSO) algorithm is presented based on three methods of improvement in original PSO. First, the iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution. Second, the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm. Finally,...
The purpose of this paper is enhancing the quality of credit rating in e-business environment and reducing credit risk. The principle of the particle swarm optimization(PSO) algorithm and wavelet networks(WN) model, propose implementation steps of the WN based PSO. The algorithm is applied to the credit risk evaluating for bank, and its result is compared with conventional wavelet networks. The comparing...
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