Alice E. Smith

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Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a(More)
& Conclusions-A problem specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and levels of redundancy to optimize some objective(More)
The paper shows that the use of a memetic algorithm (MA), a genetic algorithm (GA) combined with local search, synergistically combined with Lagrangian relaxation is effective and efficient for solving large unit commitment problems in electric power systems. It is shown that standard implementations of GA or MA are not competitive with the traditional(More)
This paper presents a genetic algorithm (GA) with specialized encoding, initialization, and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the(More)
This paper uses an ant colony meta-heuristic optimization method to solve the redundancy allocation problem (RAP). The RAP is a well known NP-hard problem which has been the subject of much prior work, generally in a restricted form where each subsystem must consist of identical components in parallel to make computations tractable. Meta-heuristic methods(More)
SADAN KULTUREL-KONAK1, ALICE E. SMITH2 and DAVID W. COIT3 1Management Information Systems, Penn State Berks-Lehigh Valley College, Tulpehocken Road, P.O. Box 7009, Reading, PA 19610-6009, USA E-mail: sadan@psu.edu 2Department of Industrial and Systems Engineering, Auburn University, 207 Dunstan Hall, Auburn, AL 36849, USA E-mail: aesmith@eng.auburn.edu(More)