Chaotic binary bat algorithm for analog test point selection

A set of novel methods are proposed to optimize analog test point selection in this paper. We have introduced chaos and elitist strategy into the binary bat algorithm so as to increase its global search mobility and effectiveness for robust global optimization. In the present study, nine well known chaotic maps are introduced and used to construct the chaotic binary bat algorithm (CBBA) respectively. As a result, we have developed nine different CBBAs and for the first time applied them to deal with the analog test point selection problem. The attractiveness of our proposed CBBA mainly lies in two aspects: the utilization of chaotic maps to tune the BBA parameter and the application of elitist strategy to store all the possible global best solutions. These improvements obviously enhance the performance of BBA and make our new algorithms (CBBAs) get all the best solutions (usually more than one) easily and effectively, which will give us more possible choices in practice. Analog circuits’ examples and a group of statistical experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithms. The other reported algorithms are also used to do the comparison. The results indicate that the proposed algorithms have excellent performance in finding the optimum test point sets. Therefore, they are good solutions and applicable to actual circuits and engineering practice.

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