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Evolutionary mating algorithm

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Abstract

This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields.

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Acknowledgements

This work was supported by the Ministry of Education Malaysia (MOE) under Fundamental Research Grant Scheme (FRGS/1/2022/ICT04/UMP/02/1) and Universiti Malaysia Pahang (#RDU220105).

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Authors and Affiliations

  1. Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang (UMP), 26600, Pekan, Pahang, Malaysia

    Mohd Herwan Sulaiman, Mohd Mawardi Saari & Hamdan Daniyal

  2. Faculty of Computing, Universiti Malaysia Pahang (UMP), 26600, Pekan, Pahang, Malaysia

    Zuriani Mustaffa

  3. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia

    Seyedali Mirjalili

  4. Yonsei Frontier Lab, Yonsei University, Seoul, South Korea

    Seyedali Mirjalili

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Sulaiman, M.H., Mustaffa, Z., Saari, M.M. et al. Evolutionary mating algorithm. Neural Comput & Applic 35, 487–516 (2023). https://doi.org/10.1007/s00521-022-07761-w

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