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An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system

  • Optimization
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Abstract

Optimization algorithms are widely used to solve large and complex optimization problems. In this paper, a human intelligence-based optimization technique, an adaptive chaotic class topper optimization (AC-CTO), is proposed to solve well-known optimization problems related to the power system, i.e., economic load dispatch and combined emission economic dispatch problem. In the proposed AC-CTO scheme, the performance of the classical class topper optimization (CTO) is improved. AC-CTO includes chaotic local search (CLS), adaptive improvement factor (AIF) and adaptive acceleration coefficient (ACC) so that searching and local minima avoidance ability of the proposed algorithm is improved. To validate the exploration, exploitation and local optimal avoidance capabilities of the proposed algorithm, twenty-nine benchmark functions are used. Further, AC-CTO is used to solve five test cases of an ELD problem. To show the effectiveness of AC-CTO, results obtained are compared with the existing results obtained using other well-known techniques.

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Data Availability Statement

The data used to support the finding are cited within the article.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, India

    Abhishek Srivastava & Dushmanta Kumar Das

Authors
  1. Abhishek Srivastava
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  2. Dushmanta Kumar Das
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Appendix A

Appendix A

To test the effectiveness of proposed AAC, seven unimodal benchmark functions are tested with three conditions which are given as follows:

Condition 1::

All students learning with equal acceleration coefficient.

Condition 2::

Students performing better than average performance of a section learn with a higher acceleration coefficient, whereas students performing below average performance learn with a small acceleration coefficient.

Condition 3::

Students performing better than average performance of a section learn with a small acceleration coefficient, whereas students performing below average performance learn with a higher acceleration coefficient.

The results obtained are presented in Table 13. It is observed that using condition 2, the AC-CTO provides better result. Hence, ACC is modeled using condition 2.

Table 13 Comparison with different strategies with ACC
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Srivastava, A., Das, D.K. An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system. Soft Comput 26, 2913–2934 (2022). https://doi.org/10.1007/s00500-021-06644-x

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