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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. [ABSTRACT FROM AUTHOR]

Brain storm optimization algorithm (BSO), which is inspired by brain storm process of human, has been adopted as an efficient optimizer for various complex problems. A reinforcement learning brain storm optimization algorithm (RLBSO) to improve the performance of BSO is proposed in this paper. Four mutation strategies are designed to enhance the search capability of the algorithm in different stages. Elites are adopted as the guidance to ensure the quality of the population. The global best is utilized to guide the search direction of individuals. The cluster centers are employed to improve the exploitation ability of the algorithm. The historical individuals are utilized to the increase the diversity of the population. The Q-learning mechanism is introduced to guide the selection of strategies according to the historical information fed back by the corresponding strategies. A self-learning mechanism, which is based on the evolutionary state of the population and the experience of previous successful individuals, is utilized to determine the update method of individual. The RLBSO algorithm is tested on the CEC 2017 benchmark test suite and a practical engineering problem. The results show that RLBSO has better performance than the other state of the arts algorithms. • A reinforcement learning brain storm optimization algorithm (RLBSO) is proposed. • Four mutation strategies are introduced to increase the ability of global search. • Q-learning is utilized to guide the algorithm to the appropriate evolution strategy. • A self-learning strategy is utilized to determine the update method of the individual. • The RLBSO algorithm is tested on the CEC 2017 benchmark test suite. [ABSTRACT FROM AUTHOR]