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Full-Text Articles in Engineering
Blended Biogeography-Based Optimization For Constrained Optimization, Haiping Ma, Daniel J. Simon
Blended Biogeography-Based Optimization For Constrained Optimization, Haiping Ma, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm …
Biogeography-Based Optimization With Blended Migration For Constrained Optimization Problems, Haiping Ma, Daniel J. Simon
Biogeography-Based Optimization With Blended Migration For Constrained Optimization Problems, Haiping Ma, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
Biogeography-based optimization (BBO) is a new evolutionary algorithm based on the science of biogeography. We propose two extensions to BBO. First, we propose blended migration. Second, we modify BBO to solve constrained optimization problems. The constrained BBO algorithm is compared with solutions based on a genetic algorithm (GA) and particle swarm optimization (PSO). Numerical results indicate that BBO generally performs better than GA and PSO in handling constrained single-objective optimization problems.
A Majorization Algorithm For Constrained Correlation Matrix Approximation, Daniel J. Simon, Jeff Abell
A Majorization Algorithm For Constrained Correlation Matrix Approximation, Daniel J. Simon, Jeff Abell
Electrical and Computer Engineering Faculty Publications
We desire to find a correlation matrix of a given rank that is as close as possible to an input matrix R, subject to the constraint that specified elements in must be zero. Our optimality criterion is the weighted Frobenius norm of the approximation error, and we use a constrained majorization algorithm to solve the problem. Although many correlation matrix approximation approaches have been proposed, this specific problem, with the rank specification and the constraints, has not been studied until now. We discuss solution feasibility, convergence, and computational effort. We also present several examples.
Distributed Fault Tolerance In Optimal Interpolative Nets, Daniel J. Simon
Distributed Fault Tolerance In Optimal Interpolative Nets, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
The recursive training algorithm for the optimal interpolative (OI) classification network is extended to include distributed fault tolerance. The conventional OI Net learning algorithm leads to network weights that are nonoptimally distributed (in the sense of fault tolerance). Fault tolerance is becoming an increasingly important factor in hardware implementations of neural networks. But fault tolerance is often taken for granted in neural networks rather than being explicitly accounted for in the architecture or learning algorithm. In addition, when fault tolerance is considered, it is often accounted for using an unrealistic fault model (e.g., neurons that are stuck on or off …