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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)
—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 ini-tialization and standard genetic operators usually generate infea-sible networks. Another complication is that the(More)
Scope and Purpose-There are a vast number of practical design and resource-allocation problems, in many different fields, where the decision to be made is a matching (or assignment) of items in one set to items in another, disjoint set. If the costs associated are simply constants for each possible pairing, this is the classical "Assignment Problem", for(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)
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 describes a general approach to optimal design of communications networks when considering both economics and reliability. The approach uses a genetic algorithm to identify the best topology of network arcs to collectively meet cost and network reliability considerations. This approach is distinct because it is highly flexible and can readily(More)
The exact calculation of all-terminal network reliability is an NP-hard problem, with computational e!ort growing exponentially with the number of nodes and links in the network. During optimal network design, a huge number of candidate topologies are typically examined with each requiring a network reliability calculation. Because of the impracticality of(More)
This paper formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples from probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to(More)