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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection
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作者 Qasem Al-Tashi Tareq M Shami +9 位作者 Said Jadid Abdulkadir Emelia Akashah Patah Akhir Ayed Alwadain Hitham Alhussain Alawi Alqushaibi Helmi MD Rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期1937-1966,共30页
The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective ... The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost. 展开更多
关键词 Feature selection multi-objective optimization grey wolf optimizer lévy flight mutation classification
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ON DE FINETTI'S OPTIMAL IMPULSE DIVIDEND CONTROL PROBLEM UNDER CHAPTER 11 BANKRUPTCY
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作者 王文元 明瑞星 胡亦钧 《Acta Mathematica Scientia》 SCIE CSCD 2024年第1期215-233,共19页
Motivated by recent advances made in the study of dividend control and risk management problems involving the U.S.bankruptcy code,in this paper we follow[44]to revisit the De Finetti dividend control problem under the... Motivated by recent advances made in the study of dividend control and risk management problems involving the U.S.bankruptcy code,in this paper we follow[44]to revisit the De Finetti dividend control problem under the reorganization process and the regulator's intervention documented in U.S.Chapter 11 bankruptcy.We do this by further accommodating the fixed transaction costs on dividends to imitate the real-world procedure of dividend payments.Incorporating the fixed transaction costs transforms the targeting optimal dividend problem into an impulse control problem rather than a singular control problem,and hence computations and proofs that are distinct from[44]are needed.To account for the financial stress that is due to the more subtle concept of Chapter 11 bankruptcy,the surplus process after dividends is driven by a piece-wise spectrally negative Lévy process with endogenous regime switching.Some explicit expressions of the expected net present values under a double barrier dividend strategy,new to the literature,are established in terms of scale functions.With the help of these expressions,we are able to characterize the optimal strategy among the set of admissible double barrier dividend strategies.When the tail of the Lévy measure is log-convex,this optimal double barrier dividend strategy is then verified as the optimal dividend strategy,solving our optimal impulse control problem. 展开更多
关键词 spectrally negative lévy process Chapter 11 bankruptcy De Finetti's dividend problem double barrier strategy impulse control
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变异蝙蝠算法求解折扣{0-1}背包问题 被引量:19
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作者 吴聪聪 贺毅朝 +2 位作者 陈嶷瑛 刘雪静 才秀凤 《计算机应用》 CSCD 北大核心 2017年第5期1292-1299,共8页
针对确定性算法难于求解规模大、数据范围广的折扣{0-1}背包问题(D{0-1}KP),提出了基于蝙蝠算法的快速求解D{0-1}KP的变异蝙蝠算法(MDBBA)。首先,利用双重编码解决D{0-1}KP的编码问题;其次,将贪心修复与优化算法(GROA)应用于蝙蝠个体适... 针对确定性算法难于求解规模大、数据范围广的折扣{0-1}背包问题(D{0-1}KP),提出了基于蝙蝠算法的快速求解D{0-1}KP的变异蝙蝠算法(MDBBA)。首先,利用双重编码解决D{0-1}KP的编码问题;其次,将贪心修复与优化算法(GROA)应用于蝙蝠个体适应度计算中,使算法快速得到有效解;然后,选择使用差分演化(DE)的变异策略提高算法的全局寻优能力;最后,蝙蝠个体按一定概率进行Lévy飞行,增强算法探索能力和跳出局部极值的能力。对四类大规模实例的仿真计算表明:MDBBA非常适于求解大规模的D{0-1}KP,比第一遗传算法(FirEGA)和双重编码蝙蝠算法(DBBA)求得的最优值和平均值都更优,MDBBA收敛速度明显快于DBBA。 展开更多
关键词 折扣{0-1}背包问题 蝙蝠算法 差分演化 lévy飞行 贪心策略 非正常编码
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Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight 被引量:1
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作者 Gang Hu Liuxin Chen +1 位作者 Xupeng Wang Guo Wei 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第6期1850-1885,共36页
Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low... Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization problems.To ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this paper.The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima.Furthermore,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO.Finally,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems respectively.The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems. 展开更多
关键词 Golden eagle optimizer lévy flight Sine cosine algorithm Differential evolution strategy Engineering design Bionic model
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米黑根毛霉来源L-天冬酰胺酶的分子改造及高效表达 被引量:1
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作者 朱曼迟 张显 +5 位作者 王志 林文萱 徐美娟 杨套伟 邵明龙 饶志明 《生物工程学报》 CAS CSCD 北大核心 2021年第9期3242-3252,共11页
L-天冬酰胺酶能够水解L-天冬酰胺生成L-天冬氨酸和氨,广泛存在于微生物、植物和部分啮齿类动物的血清中,在医药和食品行业中都具有重要应用。然而无论是在医药还是在食品行业中,L-天冬酰胺酶依然存在一些问题,如催化效率低、热稳定性差... L-天冬酰胺酶能够水解L-天冬酰胺生成L-天冬氨酸和氨,广泛存在于微生物、植物和部分啮齿类动物的血清中,在医药和食品行业中都具有重要应用。然而无论是在医药还是在食品行业中,L-天冬酰胺酶依然存在一些问题,如催化效率低、热稳定性差、产量低等。文中通过理性设计及5′非翻译区(5′untranslated region,5′UTR)改造提高米黑根毛霉Rhizomucor miehei来源的L-天冬酰胺酶(RmAsnase)的酶活及蛋白表达量。结果显示,通过同源建模结合序列比对分析构建的6个突变菌株中,突变酶A344E比酶活较野生酶提高了1.5倍。继而构建食品安全菌株枯草芽孢杆菌Bacillus subtilis 168/pMA5-A344E,对其进行UTR改造,获得重组菌株B.subtilis168/pMA5 UTR-A344E,其酶活较原始菌提高了7.2倍,对重组菌B.subtilis 168/pMA5 UTR-A344E进行5 L罐研究,最终产量为489.1 U/mL。该酶活提高的重组菌株对L-天冬酰胺酶的工业化应用具有重要价值。 展开更多
关键词 l-天冬酰胺酶 定点突变 比酶活 UTR改造
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多目标强度Pareto混沌差分进化算法 被引量:19
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作者 章萌 章卫国 孙勇 《控制与决策》 EI CSCD 北大核心 2012年第1期41-46,52,共7页
提出一种多目标强度Pareto混沌差分进化算法(SPCDE).首先利用Tent映射进行种群的混沌初始化,采用一种基于均匀排挤机制的截断排挤操作和混沌替换操作进行种群的环境选择操作;然后基于一种变缩放因子的差分变异策略进行变异操作,通过计... 提出一种多目标强度Pareto混沌差分进化算法(SPCDE).首先利用Tent映射进行种群的混沌初始化,采用一种基于均匀排挤机制的截断排挤操作和混沌替换操作进行种群的环境选择操作;然后基于一种变缩放因子的差分变异策略进行变异操作,通过计算支配关系得到变异个体;最后通过支配关系的计算和环境选择操作进行进化选择操作并得到子代个体.以上操作不仅提高了算法的收敛性能,而且保证了Pareto最优解的均匀分布性.数值实验结果表明了该算法的有效性. 展开更多
关键词 多目标优化 强度Pareto 差分进化 混沌Tent映射 DE/current-to—best/1/bin变异策略
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