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Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey

[Display omitted] •Authors introduce review on hybrid bio-Inspired computational intelligence (CI) for power system.•180 suitable articles are identified and classified according to defined methodology.•Main focus on the growth, variants, applications and modifications of the Hybrid CI techniques.•P...

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Bibliographic Details
Published in:Applied soft computing 2018-08, Vol.69, p.72-130
Main Authors: Rahman, Imran, Mohamad-Saleh, Junita
Format: Article
Language:English
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Summary:[Display omitted] •Authors introduce review on hybrid bio-Inspired computational intelligence (CI) for power system.•180 suitable articles are identified and classified according to defined methodology.•Main focus on the growth, variants, applications and modifications of the Hybrid CI techniques.•Possible ways to use the hybrid bio-Inspired CI Techniques in the future work are mentioned. Optimization problems of modern day power system are very challenging to resolve because of its design complexity, wide geographical dispersion and influence from many unpredictable factors. For that reason, it is essential to apply most effective optimization techniques by taking full benefits of simplified formulation and execution of a particular problem. This study presents a summary of significant hybrid bio-inspired computational intelligence (CI) techniques utilized for power system optimization. Authors have reviewed an extensive range of hybrid CI techniques and examined the motivations behind their improvements. Various applications of hybrid bio-inspired CI algorithms have been highlighted in this paper. In addition, few drawbacks regarding the hybrid CI algorithms are explained. Current trends in CI techniques from the past researches have also been discussed in the domain of power system optimization. Lastly, some future research directions are suggested for further advancement of hybrid techniques.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.04.051