It is widely used in the structural form of point support glass curtain wall with large grid and large plate. The connection claw jig is a common point support device for glass curtain wall, which meets the requirements of high permeability of glass curtain wall. Finally, the actual motor model and complex function are used to verify the performance of the optimization algorithm. Finally, the actual motor model and complex function are used to test the performance of the optimization algorithm. in practical application, the design of motor is often affected by manufacturing error, so random noise is added to the selection of optimization results, which can be more in line with the practical application. A new file management strategy is used to make the optimization results more global and have better generalization ability. The random search method is used in the global search, and a momentum gradient method is used in the local search, which makes the search results have faster convergence rates and easier convergence to the global optimal. Using the combination of global search and local search, the continuous search area is better. In this paper, we mainly study the application of a multi-objective optimization algorithm based on the black hole algorithm in motor optimization. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. The framework of AMOBH can be divided into three steps. Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called “adaptive multiobjective black hole algorithm” (AMOBH). The empirical results indicate the effectiveness of the proposed algorithm. The proposed algorithm is applied to six benchmark IMOPs and an uncertain optimization problem of solar desalination, and compared with a typical IMOEA without the local search. Finally, an initial population of the local search is created by taking the individuals with a large contribution to hypervolume and a small imprecision as the center, and the local search is implemented by taking the contribution to hypervolume as its fitness function. The existing IMOEA is first employed to search the entire search space, and then the rate of changes of hypervolume is utilized to design an activation mechanism to specify when to conduct the local search. In this paper, a local search is embedded into an existing IMOEA, and a memetic algorithm for IMOPs is developed. The existing evolutionary algorithms for IMOPs (IMOEAs) require a large amount of function evaluations to generate an approximate Pareto front which is well converged and evenly distributed, and the generated front has uncertainties to a large extent.
Multi-objective optimization problems with interval parameters (IMOPs) are ubiquitous in real-world applications.
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