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This paper proposes a novel scheme that we call the opposition based comprehensive learning particle swarm optimizers (OCLPSO), which employs opposition based learning (OBL) for population initialization and also for exemplar selecting. This scheme enables the swarm to explore and exploit with the more diversity and not to be premature convergence. Experiments were conducted on benchmark functions...
Floorplanning is an important problem in the very large integrated circuit (VLSI) design automation. It??s an NP-hard combinatorial optimization problem. The particle swarm optimization (PSO) has been proved to be a good optimization algorithm with outstanding global performance. However, PSO cannot be directly used in the combinatorial optimization problem due to its continuous characteristic. In...
Feature extraction or feature subset selection is an important preprocessing task for pattern recognition, data mining or machine learning application. Feature subset selection basically depends on selecting a criterion function for evaluation of the feature subset and a search strategy to find the best feature subset from a large number of feature subsets. Lots of techniques have been developed so...
Bacterial foraging optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO). This paper first analyzes...
The paper presents a modified particle swarm optimization (PSO) for the dynamic environment. The modified method provides a detected position for each particle, and applies the detected positions of some randomly sampled particles in the swarm to detect the dynamic change of the environment. If the environment has been detected to change, chaos mutation technology will be introduced to respond to...
Linguistic dynamic systems (LDS) are dynamic processes involving computing with words instead of numbers for modeling and analysis of complex systems. In this paper, a fuzzy neural network (FNN) structure of LDS base on nonlinear particle swarm optimization was proposed. Finally, experiment results on logistics formulation demonstrated the feasibility and the efficiency of the proposed FNN model.
Pendulum, a natural unstable nonlinear system, is a powerful tool to check the control theory and control algorithm. Fuzzy logic can control nonlinear systems that would be difficult or impossible to model mathematically, which opens the door for controlling systems that would normally be deemed unfeasible for automation. Note that if too many inputs and outputs are chosen for a single implementation...
The blind multi-channels identification problem is studied in this paper. A cost function based on the orthogonal property between the output autocorrelation matrix and the channels parameter matrix is first constructed for a signal-input multiple-output FIR system. Then, an improved particle swarm optimizer, in which the personal best particle is replaced with the weight average of personal best...
Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM).In this paper, we propose an advanced Particle swarm optimization (PSO) and differential evolution (DE) method for SCOC. In the process of doing so,we first developed a novel spatial obstructed distance using PSO-DV(particle swarm optimization with differentially perturbed Velocity) based on grid...
Under the application background of network security evaluation, a mechanism for situation element extraction based on Particle Swarm Optimization (PSO) and Fuzzy Neural Network (FNN) is proposed. Firstly, the input dataset of historical situation element is pre-fuzzed and then transformed into fuzzy logic rule which can be mapped between neural network layers. Meanwhile, PSO is used to achieve global...
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