Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires determination of parameter space confidence regions in addition to point estimates. In most practical applications, the relation of the parameters to model fitness is highly nonlinear and noisy data leads to further deviations. Thus the confidence regions obtained by using locally linearized models are often misleading. Uniform covering by probabilistic rejection (UCPR) is a robust technique that has been developed to solve this problem, and has been proven to be more efficient than other approximate random search techniques. In this paper, we propose a contour particle swarm optimization (C-PSO) technique and compare its performance against UCPR in predicting the confidence regions. Results indicate that for problems with low number of parameters, both the algorithms are quite comparable. However, real world models such as genetic networks have a large number of parameters and the UCPR fails in finding good convergence due to its limited search capabilities. In such problems, the C-PSO technique was able to find the confidence regions with better resolution and efficiency.