A novel approach for combinatorial test case generation using multi objective optimization

Surveillance Test Interval is an important parameter for the standby systems of a plant with respect to its availability, cost and other issues. Stand-by systems are not required during normal operations but are essentially required when demanded by the plant operations. Stand-by systems are tested and maintained periodically in order to ensure their serviceability. In this paper an attempt has been done to optimize the unavailability, cost and manrem consumption with respect to Surveillance Test Interval for the standby systems of nuclear plants. In this work a novel approach has been introduced where Real Parameter Genetic Algorithm (GA) has been used for the multi-objective optimization problem in hand. Application of Genetic Algorithms in similar problems has been reported in literature. But the approach proposed in this paper differs from the exixting methods significantly. In this work real-parameter GA has been used which makes the algorithm simple by not having the overhead of encoding and decoding of solutions. More-over a multi-objective optimization method has been proposed that not only takes care of optimizing the unavailability but also cost and manrem consumption. Here we have mainly concentrated on the Emergency Core Cooling System of a Research Reactor, but the same idea can easily be extended for the other standby systems.

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Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization

Evolutionary Computation ◽

10.1162/evco_a_00076 ◽

2013 ◽

Vol 21 (2) ◽

pp. 261-291 ◽

Cited By ~ 30

Author(s):

Matjaž Depolli ◽

Roman Trobec ◽

Bogdan Filipič

Keyword(s):

Computer Architecture ◽

Optimization Problem ◽

Computer Architectures ◽

Parallel Computer ◽

Test Case ◽

Multi Objective Optimization ◽

Multi Objective ◽

Performance Results ◽

Insight Into ◽

Industrial Optimization

In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed for solving time-intensive problems efficiently on both homogeneous and heterogeneous parallel computer architectures. The algorithm is used as a test case for the asynchronous master-slave parallelization of multi-objective optimization that has not yet been thoroughly investigated. Selection lag is identified as the key property of the parallelization method, which explains how its behavior depends on the type of computer architecture and the number of processors. It is arrived at analytically and from the empirical results. AMS-DEMO is tested on a benchmark problem and a time-intensive industrial optimization problem, on homogeneous and heterogeneous parallel setups, providing performance results for the algorithm and an insight into the parallelization method. A comparison is also performed between AMS-DEMO and generational master-slave DEMO to demonstrate how the asynchronous parallelization method enhances the algorithm and what benefits it brings compared to the synchronous method.

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