Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74280
Title: A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch
Authors: Tan, L
Wang, H 
Yang, C
Niu, B
Issue Date: 2017
Source: Natural computing, 2017, p. 1-17
Abstract: Multi-objective optimization is an interesting and hot topic in the literature involving the conflicting objectives to be solved simultaneously. In this study, a new multiple optimization method based on a discrete bacterial algorithm is developed to address the multi-objective economic-environmental dispatch problem with non-linear, non-convex, and complexity constraints. In the proposed multi-objective bacterial based algorithm, the existence of bacteria complies with a fitness survival mechanism, in which a health sorting approach is operated to control the chances of reproduction as well as elimination. The performances of bacteria have been recorded and sorted for health evaluation, which can help to group the individuals according to their search capability and improve the overall quality of the population. To speed up the convergence rate and avoid local minima to some extent, a comprehensive learning strategy is embedded to enable the communication exchanges between the bacteria and external archive. The standard IEEE 30-bus, 6-generator test system is adopted to illustrate the efficiency of the proposed method by making the comparison with the other multiple bacterial-based algorithms as well as six other well developed evolutionary algorithms. The effectiveness of the propose method is well validated in experiments by providing similar or superior solutions to environmental/economic power dispatch issues considering the various constraints.
Keywords: Discrete bacterial-based algorithm
Environmental/economic power dispatch
Multi-objective optimization
Publisher: Springer
Journal: Natural computing 
ISSN: 1567-7818
EISSN: 1567-7818
DOI: 10.1007/s11047-017-9620-7
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