An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems
Mathematical Biosciences and Engineering, ISSN: 1551-0018, Vol: 19, Issue: 1, Page: 473-512
2022
- 45Citations
- 35Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations45
- Citation Indexes45
- 45
- Captures35
- Readers35
- 35
Article Description
Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (RMOP) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85119968372&origin=inward; http://dx.doi.org/10.3934/mbe.2022023; http://www.ncbi.nlm.nih.gov/pubmed/34903000; http://www.aimspress.com/article/doi/10.3934/mbe.2022023; http://www.aimspress.com/article/doi/10.3934/mbe.2022023?viewType=html; https://dx.doi.org/10.3934/mbe.2022023
American Institute of Mathematical Sciences (AIMS)
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