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A New Improved Model of Marine Predator Algorithm for Optimization Problems

  • Research Article-Computer Engineering and Computer Science
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Abstract

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies.

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Data Availability Statement

This manuscript has no associated data.

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Author information

Authors and Affiliations

  1. Department of Electrical and Control Engineering, Tafresh University, 39518 79611, Tafresh, Iran

    Mehdi Ramezani & Danial Bahmanyar

  2. Independent Researcher, Rooigemlaan 154/0201, Ghent, Belgium

    Navid Razmjooy

Authors
  1. Mehdi Ramezani
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  2. Danial Bahmanyar
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  3. Navid Razmjooy
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Correspondence to Navid Razmjooy.

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Appendices

Appendix

See Tables 7.

Table 7 Classical benchmark functions
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Appendix 2

See Table 8.

Table 8 CEC–C06 2019 Benchmarks
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Ramezani, M., Bahmanyar, D. & Razmjooy, N. A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arab J Sci Eng 46, 8803–8826 (2021). https://doi.org/10.1007/s13369-021-05688-3

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  • DOI: https://doi.org/10.1007/s13369-021-05688-3

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