To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial populatio...To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.展开更多
Basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and environment.However,the high-dimensional and redundant data of the BOF collected by the sensors ...Basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and environment.However,the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of models.The traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial applications.In order to eliminate the negative impact of feature selection application in the BOF steelmaking,an improved grey wolf optimizer(IGWO)for feature selection was proposed,and it was applied to the BOF data set.Firstly,the proposed algorithm preset the size of the feature subset based on the new encoding scheme,rather than the traditional uncertain number strategy.Then,opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal solution.In addition,a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random methods.These methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking data.Finally,the proposed algorithm was compared with other feature selection algorithms on the BOF data sets.The results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content,which can improve the performance of the BOF steelmaking.展开更多
文摘To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.
基金supported by the National Natural Science Foundation of China(Grant No.61863018)the Applied Basic Research Programs of Yunnan Science and Technology Department(Grant No.202001AT070038).
文摘Basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and environment.However,the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of models.The traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial applications.In order to eliminate the negative impact of feature selection application in the BOF steelmaking,an improved grey wolf optimizer(IGWO)for feature selection was proposed,and it was applied to the BOF data set.Firstly,the proposed algorithm preset the size of the feature subset based on the new encoding scheme,rather than the traditional uncertain number strategy.Then,opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal solution.In addition,a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random methods.These methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking data.Finally,the proposed algorithm was compared with other feature selection algorithms on the BOF data sets.The results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content,which can improve the performance of the BOF steelmaking.
