Journal
IEEE ACCESS
Volume 10, Issue -, Pages 49445-49473Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3172789
Keywords
Metaheuristics; Mathematical models; Linear programming; Statistics; Sociology; Search problems; Biological system modeling; Bio-inspired; exploitation; exploration; engineering design; optimization; zebra
Categories
Funding
- Faculty of Science, University of Hradec Kralove [2104/2022]
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In this paper, a new bio-inspired metaheuristic algorithm called Zebra Optimization Algorithm (ZOA) is developed, which simulates the foraging behavior and defense strategy of zebras. Extensive evaluations on benchmark functions and engineering design problems demonstrate that ZOA outperforms nine competitor algorithms in terms of optimization performance.
In this paper, a new bio-inspired metaheuristic algorithm called Zebra Optimization Algorithm (ZOA) is developed; its fundamental inspiration is the behavior of zebras in nature. ZOA simulates the foraging behavior of zebras and their defense strategy against predators' attacks. The ZOA steps are described and then mathematically modeled. ZOA performance in optimization is evaluated on sixty-eight benchmark functions, including unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC2015, and CEC2017. The results obtained from ZOA are compared with the performance of nine well-known algorithms. The simulation results show that ZOA can solve optimization problems by creating a suitable balance between exploration and exploitation and has a superior performance compared to nine competitor algorithms. ZOA's ability to solve real-world problems has been tested on four engineering design problems, namely, tension/compression spring, welded beam, speed reducer, and pressure vessel. The optimization results show that ZOA is an effective optimizer in determining the values of the design variables of these problems compared to the nine competitor algorithms.
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