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In recent years, many intelligent optimization algorithms are applied to the class integration and test order problem and it has been proved that they can efficiently solve this problem. In this paper, the particle swarm optimization algorithm is applied to the class integration and test order problem. First, the initial population (test orders) is generated randomly and each test order is taken as...
Particle swarm optimization (PSO) has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. We argue that it can be empowered by adaptively adjusting its convergence speed for the problems. In this paper, a convergence speed controller is proposed to improve the performance of PSO for large-scale optimization. As an additional operator...
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