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A hybrid capuchin search algorithm with gradient search algorithm for economic dispatch problem

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Abstract

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems.

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Data availability

The data that support the findings of this study are publicly available in Appendix A.

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Authors and Affiliations

  1. Department of Computer Science, Al-Balqa Applied University, Salt, Jordan

    Malik Braik & Abdelaziz I. Hammouri

  2. Department of Computer Science, Al-Aqsa University, Gaza, Palestine

    Mohammed A. Awadallah

  3. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates

    Mohammed A. Awadallah

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar

  5. Department of Information Technology, Al Hosn University College, Al Hosn, Irbid, Jordan

    Mohammed Azmi Al-Betar

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  1. Malik Braik
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  2. Mohammed A. Awadallah
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Contributions

MB contributed to conceptualization, methodology, and writing review and editing and provided software. MAA was involved in formal analysis, investigation, and data curation. MAAB contributed to writing original draft. AIH was involved in visualization and validation.

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Correspondence to Malik Braik.

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Appendix A

Appendix A

See Tables 15, 16, 17, 18 and 16.

Table 15 Generating unit data for Test System 1 (3-unit generator system)
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Table 16 Generating unit data for Test Systems 2 and 3 (13-unit generator system)
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Table 17 Generating unit data for Test System 4 (40-unit generator system)
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Table 18 Generating unit data for Test System 5 (80-unit generator system)
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Table 19 Generating unit data for Test System 6 (140-unit generator system)
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Braik, M., Awadallah, M.A., Al-Betar, M.A. et al. A hybrid capuchin search algorithm with gradient search algorithm for economic dispatch problem. Soft Comput 27, 16809–16841 (2023). https://doi.org/10.1007/s00500-023-09019-6

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