Grey wolf optimization (GWO) algorithm, when applied to multidimensional and nonlinear optimization problems, often encounters the problems of getting stuck into local optimums and slow rate of convergence. Aiming at above two problems, we propose the grey wolf optimization algorithm on the basis of dynamic weights (DGWO) in this paper. This strategy can dynamically adjust the position updates of other individuals depending on the three best search agents' own locations, thus improving the search precision and speed of GWO. Additionally, to validate the usefulness of our proposed algorithm in treating multidimensional and nonlinear optimization projects, six classical benchmark functions are selected as test targets and some classical algorithms are compared with them in this paper. The results of simulation experiments indicate that DGWO exhibits superior optimization search performance compared with other competing algorithms.
In recent years, with the rapid growth of the number of cars, the problem of "difficult parking" has become increasingly prominent, especially in the parking lot. It often takes a long time for drivers to find a free parking space. Low parking efficiency seriously affects the driver's parking feeling, which is an urgent problem to be solved. In view of this phenomenon, based on the analysis of the internal structure of the parking lot, this paper abstractly establishes the parking guidance data model, comprehensively considers the restrictive factors affecting the driver's parking psychology, then uses the improved Dijkstra algorithm to optimize the guidance data model, and finally finds out the optimal parking path, which greatly improves the parking efficiency and parking feeling.
For the problem of modeling and optimization of biogas high pressure water scrubbing (HPWS) process, this paper proposes an adaptive online sequential extreme learning machine (AOS-ELM) modeling method and a novel multiobjective differential evolutionary (NMODE) optimization method. AOS -ELM established the adaptive models successfully for the product gas quality and equipment energy consumption. Compared with other four algorithms mentioned in this paper, the models obtained by our proposed AOS-ELM modeling method are of higher prediction accuracy. Furthermore, considering that the two goals of product gas quality and energy consumption are contradictory, the proposed NMODE outperforms the other two methods (NSGA-II and MODE) in terms of Pareto diversity and distribution, which means that our overall achievements are helpful and can provide enterprise decision makers with better operating conditions candidate sets in the HPWS process.
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