Backtracking Search Optimization Algorithm (BSA) is one of the recent meta-heuristic algorithms. In spite of its success, it might have some drawbacks. What are the main drawbacks of BSA?
Looking at the text at
https://www.mathworks.com/matlabcentral/fileexchange/44842-backtracking-search-optimization-algorithm
it's clear that the method is just like every other metaheuristic: it has some strategies built in, with fancy names, but it's all the same routine as most other metaheuristics - no guarantees. Typically, numerical tests on these methods are not as serious as in papers in hard-core mathematical programming journals. I am not surprised about that - these methods are not to be trusted. Just another "emperor's new clothes" story then.
As any metaheuristic algorithms. BSA has advantages and disadvantages. Based on your request, the drawbacks of BSA are:
Being easy to fall into local optimum, lacking the learning from the optimal individual, and being difficult to adjust the control parameter F.
Dear Mohammad Shebad.
Based on the Pinar's research paper (the inventor of BSA), it claims that BSA gets balance between local and global search. Is there any article concluded that point?
Also, could your please more elaborate on this point (lacking hte learning from the optimal individual)?
Regards @Mohammad Shehab
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