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In order to develop the global performance of particle swarm optimization (PSO), the paper proposes a bi-swarm particle swarm optimization with cooperative co-evolution (BPSO-CC). BPSO-CC adopts two swarms to go on the search, the first swarm is designated to conduct the coarse search in the whole space, while the second swarm is generated periodically surround the first swarm and designated to make...
Enlightened by the properties of scale-free network model, BA model is extended and introduced into particle swarm optimization, and a novel two-phase particle swarm optimization with scale-free network model (TPSO-SNM) is proposed. At the early stages of the algorithm, particles are randomly distributed in a ring, new particles are continuously added into the structure based on the node degree and...
As a nonlinear time-varying system, it is hard to make the stability analysis for particle swarm optimization (PSO). In this paper, geometric speed stability analysis is introduced to analyze the stability condition of PSO. According to the condition, a new inertia weight selection strategy is proposed. Simulation results show it is effective.
This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied...
PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the...
This paper mainly proposes a neural networks model with hybrid algorithm, named HAENN (Hybrid Algorithm Elman Neural Network). This model is based on Elman neural network, using an improved algorithm instead the standard BP training algorithm. This improved algorithm is combined Particle Swarm Optimization algorithm with Simulated Annealing's idea, which has faster convergence speed and better solution...
Linear parameter adjustment (LPA) schemes had been widely used in particle swarm optimization (PSO). In this paper, we develop a novel PSO algorithm with nonlinear parameter adjustment (NLPA) called nonlinear PSO and present its convergence analysis. Simulations on five standard test functions confirm the validity of the nonlinear parameter adjustment methods.
In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baldwinian operator to simulate the learning mechanism among the particles and employs the information of the swarm to alter the search space adaptively. Secondly, a mutation operation is introduced...
Multi-mode project scheduling problem is a complex and confirmed to be NP-hard problem. Many researchers have devoted themselves for solving a variety of scheduling problems. Meta-heuristic is a promoting scheme. Among them, particle swarm optimization (PSO) has been well applied for solving different problems. However, PSO usually leads to premature convergence and trapped on local optimal. Hence,...
The behavior of the particle swarm optimization (PSO) algorithm is analyzed by regarding its dynamics as a system with multiplicative noise and applying control-theoretic analysis methods. In order to evaluate the convergence and diversity of the PSO algorithm, two measures related to the decay rate and l2 gain of the PSO dynamics are used. These measures are characterized by linear matrix inequalities...
Regression is one of the effective techniques for data analysis in a WSN. Besides distributed data, the limited power supply and bandwidth capacity of nodes makes doing regression difficult in WSNs. Conventional methods, which employ some numerical optimization techniques such as Nelder-Mead simplex and gradient descent, generally work in a pre-established Hamiltonian path among the nodes. Low estimation...
To the problem of the premature convergence and lower searching precision of the standard particle swarm optimization (PSO), this paper provides niche chaotic mutation quantum-behaved particle swarm optimization (NCQPSO) algorithm for image elastic registration, through maximizing the value of JS measure to achieve. In this algorithm, niche methods and eliminating strategy are introduced to improve...
In this paper, we propose a method of obtaining the sense of touch by using the EEG. Multimodal device based on human sensory systems have obtained a lot of attention in the fields of human interface. Among the human sensory systems, especially, the sense of touch is important form of social interaction and it can have powerful emotional consequences. Therefore, it is important to improve tactile...
The Frequency Assignment Problem (FAP) is considered in this paper. As the co-site constraint (CSC) may cause more interference in the real-world situation, we have paid more attention on CSC. The algorithm proposed here is a metaheuristic approach, which uses heuristic information combined with a modified PSO (Particle Swarm Optimization) algorithm to solve the FAP problem. Simulation results show...
To overcome the premature convergence of particle swarm optimization (PSO), we introduce a sociological conception, called family, into the PSO. Family is a common activity form of life. Each family in a population usually competes for the resource with other family and enhances collaboration among family members. We introduced this sociological conception into PSO and proposed the family PSO(F-PSO),...
To choose the appropriate value of inertia weight can improve the performance of PSO by means of making a good balance between exploration and exploitation in search process. This paper presents a novel inertia weight variation method based on a piecewise function, in which there are two parts: one is nonlinear decreasing to enhance the explorative ability; the other is linear decreasing just as standard...
This paper proposes a method of target location based on the adaptive particle swarm optimization algorithm in view of the shortcoming that the current localization solution is complex, and the standard particle swarm optimizer algorithm has low convergence rate and is easy to be trapped in local optimum. In the algorithm, the adaptive inertia weight can balance global and local search ability, and...
An improved algorithm based on comprehensive learning and adaptive mutation is proposed in view of the shortcoming of multi-swarm particle swarm optimization (MCPSO), which still has low convergence speed and bad solution accuracy. The method quickens the convergence rate by sharing the best information of all swarms, and improves convergence accuracy by adaptive mutation. The simulation results indicate...
In this paper, an assembly sequence planning (ASP) approach is proposed with a multi-objective hybrid evolutionary search algorithm, which combines a discrete particle swarm optimization (DPSO) algorithm and a simulated annealing (SA) algorithm. Based on a special assembly sequence coding method and corresponding update strategy, the effects caused by the changes of parameters in the hybrid DPSO and...
The artificial fish swarm algorithm is a new kind of optimizing method based on the model of autonomous animals. After analyzing the disadvantages of AFSA, an improved artificial fish swarm algorithm is presented. According to the ergodicity and stochasticity of chaos, the basic AFSA is combined with chaos in order to initialize the fish school. The improvement of the swarming behavior increased the...
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