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Artificial locust swarm optimization algorithm

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Abstract

This study proposes a new metaheuristic algorithm, which is called Artificial Locust Swarm Optimization (ALSO), inspired by random jumping and plant invasion behavior of locust swarms. Locusts interact in two different ways of searching for food: social and familial. In the familial phase, small locust groups search foods in a local area and the locusts share their information in the social phase. The proposed algorithm is less likely to trap into the local solution than other methods and has high performance in the sensitivity of the global solution. In addition, it is effective not only for the solution of black-box optimization problems but also for the solution of problems with an irregular objective function. The ALSO algorithm is compared with other recent and well-known optimization algorithms on 22 benchmark functions and 3 real engineering design problems. Simulation results prove that the ALSO algorithm is very competitive when compared to the other algorithms. Moreover, it even requires the less runtime and memory space under the same conditions.

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Data availability

The authors declare that they have all the data used in the simulations.

Code availability

The authors declare that they have pseudo code in manuscript. Also, they have source code for proposed and compared algorithm.

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Authors and Affiliations

  1. Department of Statistics and Computer Sciences, Karadeniz Technical University, 61080, Trabzon, Turkey

    Orhan Kesemen, Eda Özkul, Özge Tezel & Buğra Kaan Tiryaki

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  1. Orhan Kesemen
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  2. Eda Özkul
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  3. Özge Tezel
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  4. Buğra Kaan Tiryaki
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Appendix

Appendix

See to Tables 16 , 17 and 18

Table 16 Pair-wise statistical comparison between the ALSO and all compared algorithms by Wilcoxon rank-sum test for \(D=30\)
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Table 17 Pair-wise statistical comparison between the ALSO and all compared algorithms by Wilcoxon rank-sum test for \(D=50\)
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Table 18 Pair-wise statistical comparison between the ALSO and all compared algorithms by Wilcoxon rank-sum test for \(D=100\)
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Kesemen, O., Özkul, E., Tezel, Ö. et al. Artificial locust swarm optimization algorithm. Soft Comput 27, 5663–5701 (2023). https://doi.org/10.1007/s00500-022-07726-0

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