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Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm

MEMETIC COMPUTING 2024年第1期16卷 29-44页

作者： Han, Ning Chen, Yinnan Ye, Lingjuan Zhao, Xinchao Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China

Portfolio optimization will apply the concept of diversification across asset classes, which means investing in a wide variety of asset types and classes for a risk-mitigation strategy. Portfolio optimization is a way to maximize net gains in a portfolio while minimizing risk. A portfolio means investing in a wide variety of asset types and classes for a risk-mitigation strategy by the investor. In this paper, factor analysis and cluster algorithm are used to screen stocks and an improved differential evolution algorithm for solving portfolio optimization model is proposed. By comprehensively analyzing the stock data with factor analysis and k-means clustering algorithm, it has found that important factors have important effect on stock price movement, and finally 10 stocks are selected with investment value. Besides, a Mean-Conditional Value at Risk (CVaR) model is constructed, which takes into account both the cost function and the diversification constraint. Finally, a second-order memetic differential evolution (SOMDE) algorithm is presented for solving the proposed model. The experiments show that the proposed SOMDE algorithm is valid for solving the Mean-CVaR model and that factor analysis for stock selection can benefit portfolio with higher return and less risk greatly.

关键词： Portfolio optimization Factor analysis Mean-CVaR model differential evolution algorithm Memetic algorithm

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Optimized differential evolution algorithm for Software Testing

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第1期12卷 215-226页

作者： Gou, Xiaodong Huang, Tingting Yang, Shunkun Su, Mengxuan Zeng, Fuping Beihang Univ Sch Reliabil & Syst Engn Beijing 100083 Peoples R China Univ Melbourne Melbourne Vic 3010 Australia

differential evolution (DE) algorithms for software testing usually exhibited limited performance and stability owing to possible premature-convergence-related aging during evolution processes. This paper proposes a new framework comprising an antiaging mechanism, that is, a rebirth strategy with partial memory against aging, for the existing DE algorithm and a specialized fitness function. The results of application of the proposed framework to instantiate three DE algorithms with different mutation schemas indicate that it significantly improved their effectiveness, performance, and stability. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Software testing Test data generation differential evolution algorithm Premature convergence Anti-aging Rebirth strategy

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Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3923-3937页

作者： Civicioglu, Pinar Besdok, Erkan Gunen, Mehmet Akif Atasever, Umit Haluk Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Engn Fac Dept Geomat Engn Kayseri Turkey

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

关键词： Cuckoo search algorithm Artificial bee colony algorithm differential evolution algorithm Backtracking search optimization Particle swarm optimization

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Modelling the influence of pH, fluoride, and caffeine on the corrosion resistance of TiMo alloys by artificial neural networks developed with differential evolution algorithm

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MATERIALS AND CORROSION-WERKSTOFFE UND KORROSION 2015年第9期66卷 982-994页

作者： Mareci, D. Dragoi, E. -N. Bolat, G. Chelariu, R. Gordin, D. Curteanu, S. Gheorghe Asachi Tech Univ Iasi Fac Chem Engn & Environm Protect Iasi 700050 Romania Gheorghe Asachi Tech Univ Iasi Fac Mat Sci & Engn Iasi 700050 Romania INSA Rennes F-35708 Rennes France

Titanium (Ti) and its alloys are widely used in dental applications due to the excellent corrosion resistance and mechanical properties. However, it has been reported that Ti is sensitive to fluor ions (F-) and lactic acid. Corrosion behaviour of the TiMo alloys, together with the currently used metallic biomaterial commercial pure titanium (Cp-Ti), was investigated considering the use of alloys for dental applications. All the samples were examined using electrochemical impedance spectroscopy (EIS) in acidic artificial saliva with NaF and/or caffeine, at 37 degrees C. Equivalent circuits were used for modeling EIS data, in order to characterize samples surface and better understanding the effect of Mo addition on Cp-Ti. The TiMo alloys appear to possess superior corrosion resistance than Cp-Ti in all electrochemical media. In addition, a modelling technique based on differential evolution and artificial neural network was applied. The scope of this procedure was to determine an efficient model of the process and to eliminate the need for new experiments based on the predictions provided by the developed models.

关键词： acidic artificial saliva artificial neural networks caffeine differential evolution algorithm TiMo alloys

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A Non-Dominated Sorting Cooperative Co-evolutionary differential evolution algorithm for Multi-Objective Layout Optimization

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IEEE ACCESS 2017年 5卷 14468-14477页

作者： Zhang, Zi-Hui Zhong, Chong-Quan Xu, Zhi-Zheng Teng, Hong-Fei Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Different from traditional multi-objective evolutionary algorithms (MOEAS), multi-objective cooperative co-evolutionary algorithms (MOCCEAs) divide the decision variables into several subproblems to optimize. Solutions of each subproblem are evaluated by complete solutions formed through combining representative solutions from all subproblems. Therefore, the combination of representative solutions is a key issue in MOCCEAs. To improve the capability of MOCCEAs to complex multi objective optimization problems, we propose a non-dominated sorting cooperative co-evolutionary differential evolution algorithm (NSCCDE). The proposed NSCCDE uses an external archive for storing complete solutions to establish a new collaboration mechanism, which forms a complete solution by combining collaborators from each subpopulation as well as from the external archive. On the one hand, the external archive is updated continuously through non-dominated sorting of complete solutions, which is conducive to speeding up the convergence. On the other hand, the external archive evolves itself through spatial dispersal and mutation operation to increase the diversity. The performance of proposed NSCCDE is then evaluated on a suite of satellite module layout optimization problem. Experimental results demonstrate that the proposed algorithm outperforms NSCCGA and NSGA-II.

关键词： Multi-objective layout optimization cooperative co-evolution differential evolution algorithm satellite module

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An effective and efficient differential evolution algorithm for the integrated stochastic joint replenishment and delivery model

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KNOWLEDGE-BASED SYSTEMS 2012年 36卷 104-114页

作者： Wang, Lin Dun, Cai-Xia Bi, Wen-Jie Zeng, Yu-Rong Cent S Univ Sch Business Changsha 410083 Hunan Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Sch Informat Management Wuhan 430205 Hubei Peoples R China

As an important managerial problem, the practical joint replenishment and delivery (JRD) model under stochastic demand can be regarded as the combination of a joint replenishment problem and traveling salesman problem, either one is an NP-hard problem. However, due to the JRD's difficult mathematical properties, high quality solutions for the problem have eluded researchers. This paper firstly proposes an effective and efficient hybrid differential evolution algorithm (HDE) based on the differential evolution algorithm (DE) and genetic algorithm (GA) that can solve this NP-hard problem in a robust and precise way. After determining the appropriate parameters of the HDE by parameters tuning test, the effectiveness and efficiency of the HDE are verified by benchmark functions and numerical examples. We compare the HDE with the available best approach and find that the HDE can always obtain the slightly lower total costs under some situations. Compared with another popular evolutionary algorithm, results of numerical examples also show HDE is faster than GA and the convergence rate of HDE is higher than GA. HDE is a strong candidate for the JRD under stochastic demand. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Joint replenishment-delivery Stochastic demand Periodic review differential evolution algorithm Genetic algorithm

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Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm

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ENERGY CONVERSION AND MANAGEMENT 2011年第2期52卷 1175-1183页

作者： Lu, Youlin Zhou, Jianzhong Qin, Hui Wang, Ying Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

enhanced multi-objective differential evolution algorithm (EMODE) is proposed in this paper to solve environmental/economic dispatch (EED) problem by considering the minimal of fuel cost and emission effects synthetically. In the proposed algorithm, an elitist archive technique is adopted to retain the non-dominated solutions obtained during the evolutionary process, and the operators of DE are modified according to the characteristics of multi-objective optimization problems. Moreover, in order to avoid premature convergence, a local random search (LRS) operator is integrated with the proposed method to improve the convergence performance. In view of the difficulties of handling the complicated constraints of EED problem, a new heuristic constraints handling method without any penalty factor settings is presented. The feasibility and effectiveness of the proposed EMODE method is demonstrated for a test power system. Compared with other methods, EMODE can get higher quality solutions by reducing the fuel cost and the emission effects synthetically. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Environment/economic dispatch differential evolution algorithm Multi-objective optimization Local random search Constraint handle

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Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling

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COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 714-730页

作者： Wang, Tiejun Wu, Kaijun Du, Tiaotiao Cheng, Xiaochun Northwest Minzu Univ Sch Math Lanzhou 730030 Peoples R China Northwest Minzu Univ Comp Sci Inst Lanzhou 730030 Peoples R China Lanzhou Jiao Tong Univ Sch Elect & Informat Engn Lanzhou Peoples R China Middlesex Univ Dept Comp Sci London NW4 4BT England

Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non-deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high-dimensional and multi-modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two-stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.

关键词： adaptive dynamic weighting differential evolution algorithm emergency material allocation and scheduling smart cities research

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Exergoeconomic analysis and optimization of a solar based multigeneration system using multiobjective differential evolution algorithm

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JOURNAL OF CLEANER PRODUCTION 2018年 170卷 978-990页

作者： Rashidi, Halimeh Khorshidi, Jamshid Univ Hormozgan Mech Engn Dept Bandar Abbas Iran

This paper presents and analyzes a multigeneration energy system that consists of a reverse osmosis desalination unit, water heater, organic Rankine cycle, photovoltaic solar collectors, and a single effect absorption chiller. In doing so, energy and exergy analysis are first performed to evaluate the performance of the system and determine the irreversibility of each component. Next, considering minimizing total cost rate and maximizing exergy efficiency as two objective functions, a multiobjective optimization approach based on differential evolution algorithm is proposed to determine the best design parameters. A self-adaptive technique is utilized to deal with the search capability, population diversity, and convergence speed of the proposed optimization algorithm. An external archive list is used to save all nondominated optimal solutions during the optimization. Dynamic crowding distance approach is employed to decrease archiving size without losing its characteristics. Furthermore, a fuzzy clustering approach is used to select the desired solution among the Pareto-optimal solutions. Simulation results are compared with two other multiobjective optimization algorithms and effectiveness of the proposed optimization method is verified using various indices. Finally, a sensitivity analysis is employed to evaluate effects of design parameters on exergy efficiency and total cost rate of the system. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multigeneration energy Photovoltaic solar system Exergy analysis Multiobjective optimization differential evolution algorithm Sensitivity analysis Pareto frontier

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EEFCM-DE: energy-efficient clustering based on fuzzy C means and differential evolution algorithm in WSNs

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IET COMMUNICATIONS 2019年第8期13卷 996-1007页

作者： Sharma, Richa Vashisht, Vasudha Singh, Umang Amity Univ Amity Inst Informat Technol Noida India Amity Univ Amity Sch Engn & Technol Noida India Inst Technol & Sci Ghaziabad India

Efficient energy utilisation is a fundamental challenge that needs to be dealt with while deploying a wireless sensor network (WSN). These networks consist of thousands of small-sized battery-operated devices called sensors. Sensors are resource-constrained devices, and hence have very limited energy available with them. By lessening the energy usage of these nodes, the life span of the whole network can be enhanced up to a great extent. Clustering of WSNs is a speedily flourishing research area. In clustered WSNs, the major concerns are choosing an appropriate number of clusters and then selecting a coordinator node called cluster head (CH) within each formed cluster. This study introduces a hybrid energy-efficient clustering based on fuzzy C means and differential evolution algorithm (EEFCM-DE) based on fuzzy clustering and fuzzy-based evolutionary technique. The idea is to use FCM for the cluster creation and then selecting the best node as a CH within each cluster formed, by using an evolutionary technique DE. For the CH selection, the fitness of each node is calculated through a designed fuzzy inference system. The simulation results validate the energy-efficient performance of EEFCM-DE in comparison with the other existing algorithms.

关键词： fuzzy reasoning wireless sensor networks evolutionary computation energy conservation telecommunication power management sensor placement coordinator node cluster head hybrid energy-efficient clustering fuzzy C means differential evolution algorithm fuzzy clustering fuzzy-based evolutionary technique CH selection EEFCM wireless sensor network small-sized battery-operated devices resource-constrained devices energy usage clustered WSN fuzzy inference system energy utilisation

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