Abstract
This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.
Highlights
In the past few decades, based on linear and non-linear programming methods optimization algorithms have been used to solve various practical problems in engineering, science, business, economics, etc
The fact has proved that based on the "No Free Lunch" (NFL) theorem [16], there is no universal optimization algorithm inspired by nature that can solve all real-world optimization problems in the best way [17]
The membrane search algorithm (MSA) is based on 19 benchmark functions
Summary
In the past few decades, based on linear and non-linear programming methods optimization algorithms have been used to solve various practical problems in engineering, science, business, economics, etc. The fact has proved that based on the "No Free Lunch" (NFL) theorem [16], there is no universal optimization algorithm inspired by nature that can solve all real-world optimization problems in the best way [17]. It means that a certain type of membrane calculation is suitable for solving a specific set of problems, but it cannot effectively solve all types of problems. The group individuals imitate the behavior of cell production of protein, and the resulting multi-dimensional solution is used as the candidate solution generated by the algorithm as a function
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