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A brain storm optimization algorithm with feature information knowledge and learning mechanism
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DOI：10.1007/s10489-022-03762-3.png)
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Various optimization problems with multiple decision variables and complex constraints, which exist widely in the real world, are difficult to be solved by traditional methods. Brain storm optimization (BSO) algorithm, an advanced swarm intelligence optimization method, has high efficiency and flexibility in solving large-scale problems independent of problem characteristics. The essence of swarm intelligence optimization algorithm is that a population iteratively searches for the optimal solution in the solution space, and the process has randomness and blindness. To enhance the searching ability of BSO and strengthen the theoretical guidance of the algorithm, a brain storm optimization algorithm with feature information knowledge and learning mechanism (FIBSO) is proposed in this paper. In the process of BSO iteration, information interaction exists between individuals of each generation. The new individual is generated from the old individual, and the dominant individual contributes to the new individual. Theoretically, using the knowledge of characteristic information between individuals guides the evolution of the population in the dominant direction. Moreover, three search strategies guided by global and local optimal individuals are presented to balance the global and local search capabilities of the algorithm. The results of FIBSO and several comparison algorithms on the CEC2017 test suite indicate that FIBSO has superior performance to the state-of-the arts algorithms. The FIBSO is introduced to the no-wait flow shop scheduling problem, and the results show that FIBSO has the significant ability to solve practical engineering problems.
Keyword:
Brain storm optimization
Feature information
Interactive learning
Search strategies
Flow shop scheduling problem
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3.5
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7.6K
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