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Clustering cuckoo search optimization for economic load dispatch problem

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Abstract

In this paper, a clustering cuckoo search optimization (CCSO) is proposed. Different from the randomly generated step size in CSO, the step size in CCSO is generated by a clustering mechanism, and the value is updated according to the average fitness value difference between each cluster and the whole swarm, thereby improving the searching balance between exploration and exploitation of each solution. The effectiveness of CCSO has been validated by six typical benchmark functions and economic load dispatch problems with 6, 10, 13, 15 and 40 generators. The results of CSO and CCSO are displayed and compared in aspects of convergence rate, objective function value and robustness. Moreover, the influences of parameters as step size \(\delta \), solution number P, egg abandon fraction \(p_a\) and cluster number K are all analyzed comprehensively in this study. The conclusion is that, in all the tested cases, CCSO behaves much more competitive than CSO under the same parameter setting conditions.

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Acknowledgements

This research was supported by Korea Electric Power Corporation (grant number R17XA05-38).

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Authors and Affiliations

  1. Department of Electronic Information and Electrical Engineering, Anyang Institute of Technology, Anyang, 455000, China

    Jiangtao Yu

  2. Department of Electrical Engineering, Yeungnam University, Kyonsan, Gyeongsan-si, 38541, Republic of Korea

    Jiangtao Yu, Chang-Hwan Kim & Sang-Bong Rhee

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Correspondence to Sang-Bong Rhee.

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Appendix A

Appendix A

See Tables 11, 12, 13, 14, 15.

Table 11 Outputs of \(F\_min\) by CSO and CCSO in Case 1
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Table 12 Outputs of \(F\_min\) by CSO and CCSO in Case 2
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Table 13 Outputs of \(F\_min\) by CSO and CCSO in Case 3
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Table 14 Outputs of \(F\_min\) by CSO and CCSO in Case 4
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Table 15 Outputs of \(F\_min\) by CSO and CCSO in Case 5
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Yu, J., Kim, CH. & Rhee, SB. Clustering cuckoo search optimization for economic load dispatch problem. Neural Comput & Applic 32, 16951–16969 (2020). https://doi.org/10.1007/s00521-020-05036-w

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