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Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization algorithm. This paper introduces a composite particle swarm optimization CPSO based on the adaptive PSO and adaptive GA and applies CPSO in the BP network training of turbo-pump fault diagnosis. In addition, the classical test function Rastrigrin...
Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which proposes that the co-operation of individuals promotes the evolution of the swarm. Recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems with continuous design variable and competitive results were obtained. Since most of structural problems involve...
A Single U-shaped Assembly Line (SUAL) is a type of Just-In-Time (JIT) production system where a variety of product models with similar product characteristics are assembled. Worker allocation to the SUAL is crucial to achieve the main benefits of JIT with the minimum of number of workers, equity of workload and the shortest walking time. A novel algorithm, named Particle Swarm Optimization with Negative...
Particle Swarm Optimization (PSO) is a new random computational method for tackling optimization functions. However, it is easily trapped into the local optimum when solving the complexity and high-dimensional problems, which makes the performance of PSO greatly reduced. To overcome this shortcoming, the paper proposes an Improved Particle Swarm Optimization (IPSO), by adding the third particle of...
A two-dimension OTSU based on Simulated Annealing and Particle Swarm Optimization Algorithm is proposed for image segment and a novel contrast enhancement algorithm is presented based on it, and total variation denoising model is applied to transmission lines image denoising, aiming to the problems that the transmission lines image obtained in bad weather conditions by video monitoring system are...
Recently Particle Swarm Optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this...
PAM (Partitioning Around Medoids) was one of the first k-medoids algorithms. It attempts to determine k partitions for n objects. In the parallel particle swarm optimization, the number of particle is generally not too much. Therefore, PAM is used to divide the swarm is a best choises. This can make not only the location of particles within the same sub-swarm be in the relative concentrative, but...
The particle swarm optimization (PSO) algorithm is a swarm intelligence technique, which has exhibited good performance on finding optimal regions of complex search spaces. However, the basic PSO (bPSO) suffers from the premature convergence in multi-modal optimization. This is due to a decease of swarm diversity that leads to the global implosion and stagnation. It is an acceptable hypothesis that...
This article proposes a discrete particle swarm optimization (DPSO) for solution of the shortest path problem (SPP). The proposed DPSO adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Detailed descriptions of the new solution and...
To improve Particle swarm optimization (PSO) ability to explore new areas without delaying the algorithm convergence, a novel strategy is proposed which consists of choosing the best behavior while the new computed position of particle exceeds the search space. The strategy is tested and compared with conventional ones using adaptive PSO algorithm. Simulation results of benchmark functions are analyzed...
The particle swarm optimization (PSO) algorithmis a generally used optimal algorithm, which exhibits good performance on optimization problems in complex search spaces. However, traditional PSO model suffers from a local minima, and lacks of effective mechanism to escape from it. This is harmful to its overall performance. This paper presents an improved PSO model called the stochastic perturbing...
Particle swarm optimization is usually random, which leads to random distribution of search quality and search speed. So the general improved particle swarm optimization is difficult to meet fast optimization needs of some actual engineering. Stocks in the key generation of PSO algorithm generated by uniform design method can make particles in the population maintain a better uniform distribution...
To solve the problems correlated with fuzzy temporal parameter in real manufacture system, based on trapezoidal fuzzy number, a fuzzy single batch-processing machine with non-identical job sizes (NSBM) model for minimized make span and earliness/tardiness penalties which has fuzzy processing time and fuzzy due date is introduced in this paper firstly. After that, aiming at the problems of easily getting...
In this paper, the nonlinear constrained multi-objective environmental economic dispatch (EED) problem is solved using fast multi-objective evolutionary programming (FMOEP). Due to the global warming by fossil fuel, environmental concern becomes more and more important in recent years. The purpose of multi-objective optimization algorithm is minimizing all the different objectives simultaneously and...
For the discretization of particles in particle swarm optimization (PSO), we have proposed the family PSO (FPSO) previously. To further study the internal structure of FPSO, this paper defined two kinds of relationships between particles: equal relationship (ER) and generational relationship (GR). FPSO of equal relationship (ER-FPSO) and FPSO of generational relationship (GR-FPSO) were proposed. Simulations...
A novel particle swarm optimization algorithm for multi-objective optimization (MOO) based on fuzzy velocity updating strategy is developed and implemented in this paper. The proposed algorithm incorporates fuzzy velocity updating strategy, which can characterize to some extent the uncertainty on the true optimality of the global best position, into particle swarm optimization (PSO) so as to avoid...
A intrusion detection system model based on particle swarm reduction was proposed in this paper. Though the experiment of this model, it turns out that the improved algorithm of quantum particle swarm can get the minimal reduction, improve particle convergence and make particles trapped into local minima more difficult. The algorithm is faster than GA and has a high rate of network intrusion detection.
Swarm based intelligent search algorithms are heuristic search methods whose mechanics are inspired by the swarming or collaborative behavior of biological populations. This paper proposes a new swarm based algorithm called LBEST PSO with dynamically varying sub-swarms (LPSO_DVS). The performance of four swarm based intelligent search algorithms Particle Swarm Optimization (PSO), Fitness Distance...
Swarm Robotics is the study of simple, un-intelligent robots teaming up together to address complicated tasks using cooperation and knowledge/skills sharing factors. Particle Swarm Optimization (PSO) is an Evolutionary algorithm inspired by animals' social behaviors. PSO has been used in various problems due to its fast convergence capability. Area Extended PSO (AEPSO) is an enhanced version of PSO...
To advance efficiency of software test data generation automatically, based on traditional particle swarm optimizer (PSO) algorithm, we put forward an improved algorithm (APSO) in which inertia weight is adjusted according to the fitness value of particle. Experiment simulation result shows that APSO not only has better performance than immune genetic algorithm (IGA) but also better than PSO, and...
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