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An evolutionary algorithm for multi-criteria inverse optimal value problems using a bilevel optimization model

APPLIED SOFT COMPUTING 2014年 23卷 308-318页

作者： Li, Hecheng Qinghai Normal Univ Key Lab Tibetan Informat Proc Minist Educ Dept Math Xining 810008 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

Given a linear program, a desired optimal objective value, and a set of feasible cost vectors, one needs to determine a cost vector of the linear program such that the corresponding optimal objective value is closest to the desired value. The problem is always known as a standard inverse optimal value problem. When multiple criteria are adopted to determine cost vectors, a multi-criteria inverse optimal value problem arises, which is more general than the standard case. This paper focuses on the algorithmic approach for this class of problems, and develops an evolutionary algorithm based on a dynamic weighted aggregation method. First, the original problem is converted into a bilevel program with multiple upper level objectives, in which the lower level problem is a linear program for each fixed cost vector. In addition, the potential bases of the lower level program are encoded as chromosomes, and the weighted sum of the upper level objectives is taken as a new optimization function, by which some potential nondominated solutions can be generated. In the design of the evolutionary algorithm some specified characteristics of the problem are well utilized, such as the optimality conditions. Some preliminary computational experiments are reported, which demonstrates that the proposed algorithm is efficient and robust. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Inverse optimal value problem Bilevel program evolutionary algorithm Solutions

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Development and Evaluation of an evolutionary algorithm-Based Online Energy Management System for Plug-In Hybrid Electric Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2017年第8期18卷 2181-2191页

作者： Qi, Xuewei Wu, Guoyuan Boriboonsomsin, Kanok Barth, Matthew J. Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92507 USA Univ Calif Riverside Ctr Environm Res & Technol Coll Engn Riverside CA 92507 USA

Plug-in hybrid electric vehicles (PHEVs) have been regarded as one of several promising countermeasures to transportation-related energy use and air quality issues. Compared with conventional hybrid electric vehicles, developing an energy management system (EMS) for PHEVs is more challenging due to their more complex powertrain. In this paper, we propose a generic framework of online EMS for PHEVs that is based on an evolutionary algorithm. It includes several control strategies for managing battery state-of-charge (SOC). Extensive simulation testing and evaluation using real-world traffic data indicates that the different SOC control strategies of the proposed online EMS all outperform the conventional control strategy. Out of all the SOC control strategies, the self-adaptive one is the most adaptive to real-time traffic conditions and the most robust to the uncertainties in recharging opportunity. A comparison to the existing models also employing short-term prediction shows that the proposed model can achieve the best fuel economy improvement but requiring less trip information.

关键词： Plug-in hybrid electric vehicle intelligent transportation system energy management evolutionary algorithm

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A Subspace-Knowledge Transfer Based Dynamic Constrained Multiobjective evolutionary algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1500-1512页

作者： Chen, Guoyu Guo, Yinan Jiang, Min Yang, Shengxiang Zhao, Xiaoxiao Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China Xiamen Univ Dept Cognit Sci & Technol Xiamen 361005 Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

Dynamic constrained multiobjective optimization problems (DCMOPs) have gained increasing attention in the evolutionary computation field during the past years. Among the existing studies, it is a significant challenge to rationally utilize historical knowledge to track the changing Pareto optima in DCMOPs. To address this issue, a subspace-knowledge transfer based dynamic constrained multiobjective evolutionary algorithm is proposed in this article, termed SKTEA. Once a new environment appears, objective space is partitioned into a series of subspaces by a set of uniformly-distributed reference points. Following that, a subspace that has complete time series under certain number of historical environments is regarded as the feasible subspace by the subspace classification method. Otherwise, it is the infeasible one. Based on the classification results, a subspace-driven initialization strategy is designed. In each feasible subspace, Kalman filter is introduced to predict an individual in terms of historical solutions preserved in external storage. The predicted individuals of feasible neighbors are transferred into the infeasible subspace to generate the one, and then an initial population at the new time is formed by integrating predicted and transferred individuals. Intensive experiments on 10 test benchmarks verify that SKTEA outperforms several state-of-the-art DCMOEAs, achieving good performance in solving DCMOPs.

关键词： Dynamic constrained multiobjective optimization evolutionary algorithm objective subspace knowledge transfer

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Local Structure of Cobalt Tungstate Revealed by EXAFS Spectroscopy and Reverse Monte Carlo/evolutionary algorithm Simulations

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ZEITSCHRIFT FUR PHYSIKALISCHE CHEMIE-INTERNATIONAL JOURNAL OF RESEARCH IN PHYSICAL CHEMISTRY & CHEMICAL PHYSICS 2016年第4期230卷 551-568页

作者： Timoshenko, Janis Anspoks, Andris Kalinko, Aleksandr Kuzmin, Alexei Inst Solid State Phys Kengaraga 8 LV-1063 Riga Latvia

EXAFS spectroscopy is an element-specific method that can provide perhaps the most extensive information on the local atomic structure and lattice dynamics for a broad class of materials. Conventional methods of EXAFS data treatment are often limited to the nearest coordination shells of the absorbing atom due to the difficulties in accurate accounting for the large number of correlated structural parameters that have to be included in the analysis. In this study we overcome this problem by applying novel simulation-based method: reverse Monte Carlo simulations, coupled with the evolutionary algorithm and with a powerful signal processing technique - wavelet transform. This complex approach was applied to the analysis of the WL3 - edge and Co K-edge EXAFS spectra of crystalline CoWO4, which exists in antiferromagnetic state below 55 K. Temperature dependence of the local environment up to 4.3 angstrom around both metal ions was reconstructed in the range from 10 K to 300 K, and the rigidity of the tungstate structure due to zigzag chains of WO6 and CoO6 octahedra was analyzed.

关键词： EXAFS Reverse Monte Carlo evolutionary algorithm Wavelet Transform Cobalt Tungstate

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A Novel evolutionary algorithm Inspired by Beans Dispersal

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第1期6卷 79-86页

作者： Zhang, Xiaoming Sun, Bingyu Mei, Tao Wang, Rujing Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230031 Anhui Peoples R China

Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets' research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications.

关键词： evolutionary algorithm swarm intelligence particle swarm optimization bean optimization algorithm

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LOGIC GATE-BASED evolutionary algorithm FOR THE MULTIDIMENSIONAL KNAPSACK PROBLEM-WIRELESS SENSOR NETWORK APPLICATION

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RAIRO-OPERATIONS RESEARCH 2016年第4-5期50卷 825-843页

作者： Ferjani, Ayet Allah Liouane, Noureddine Borne, Pierre Natl Engn Sch Monastir Dept Elect Engn Monastir Tunisia Ecole Cent Lille BP 48 F-59651 Villeneuve Dascq France

evolutionary algorithms (EAs) are predominantly employed to find solutions for continuous optimization problems. As EAs are initially presented for continuous spaces, research on extending EAs to find solutions for binary spaces is in growing concern. In this paper, a logic gate-based evolutionary algorithm (LGEA) for solving some combinatorial optimization problems (COPs) is introduced. The proposed LGEA has the following features. First, it employs the logic operation to generate the trial population. Thereby, LGEA replaces common space transformation rules and classic recombination and mutation methods. Second, it is based on exploiting a variety of logic gates to search for the best solution. The variety among these logic tools will naturally lead to promote diversity in the population and improve global search abilities. The LGEA presents thus a new technique to combine the logic gates into the procedure of generating offspring in an evolutionary context. To judge the performance of the algorithm, we have solved the NP-hard multidimensional knapsack problem as well as a well-known engineering optimization problem, task allocation for wireless sensor network. Experimental results show that the proposed LGEA is promising.

关键词： evolutionary algorithm logic gate multidimensional knapsack problem task allocation wireless sensor network

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Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands

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SOFT COMPUTING 2016年第9期20卷 3443-3453页

作者： Gee, Sen Bong Arokiasami, Willson Amalraj Jiang, Jing Tan, Kay Chen Natl Univ Singapore 4 Engn Dr 3 Singapore 117576 Singapore

Vehicle routing problem with stochastic demands (VRPSD) is a famous and challenging optimization problem which is similar to many real world problems. To resemble the real world scenario, total traveling distance, total driver remuneration, the number of vehicles used and the difference between driver remuneration are considered and formulated in the multi-objective optimization perspective. This paper aims to solve multi-objective VRPSD under the constraints of available time window and vehicle capacity using decomposition-based multi-objective evolutionary algorithm (MOEA/D) with diversity-loss-based selection method incorporates with local search and multi-mode mutation heuristics. We have also compared the optimization performance of the decomposition-based approach with the domination-based approach to study the difference between these two well-known evolutionary multi-objective algorithm frameworks. The simulation results have showed that the decomposition-based approach with diversity-loss-based selection method is able to maintain diverse output solutions.

关键词： Vehicle routing problem Multi-objective optimization evolutionary algorithm

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A Two-Stage evolutionary algorithm With Balanced Convergence and Diversity for Many-Objective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第10期52卷 6222-6234页

作者： Ming, Fei Gong, Wenyin Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multiobjective optimization evolutionary algorithms (MOEAs) have received significant achievements in recent years. However, they encounter many difficulties in dealing with many-objective optimization problems (MaOPs) due to the weak selection pressure. One possible way to improve the ability of MOEAs for these MaOPs is to balance the convergence and diversity in the high-dimensional objective space. Based on this consideration, this article proposes a novel generic two-stage (TS) framework for MaOPs. The entire evolutionary search process is divided into two stages: in the first stage, a new subregion dominance and a modified subregion density-based mating selection mainly purse the convergence and in the second stage, a novel level-based Pareto dominance cooperates with the traditional Pareto dominance that mainly promotes diversity. Integrated into NSGA-II, the TS NSGA-II, referred to as TS-NSGA-II, is proposed. To extensively evaluate the performance of our approach, 29 benchmark problems were used as the test suite. The experimental results demonstrate our approach obtained superior or competitive performance compared with eight state-of-the-art many-objective optimization evolutionary algorithms. To study its generality, the proposed TS strategy was also combined with four other advanced methods for MaOPs. The results show that it can also improve the performance of these four methods in terms of convergence and diversity.

关键词： Convergence Optimization evolutionary computation Machine-to-machine communications Linear programming Indexes Visualization Convergence diversity evolutionary algorithm many-objective optimization two-stage (TS) strategy

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The partial solutions consideration based self-adaptive evolutionary algorithm: A learning structure of neuro-fuzzy networks

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10749-10763页

作者： Hung, Pei-Chia Lin, Sheng-Fuu Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu Taiwan

evolutionary computation has become a popular research field due to its global searching ability. Therefore, it raises a challenge to develop an efficient and robustness evolutionary algorithm to not only reduce the evolution process but also increase the chances to meet the global solution. To this end, this study aims to provide a novel evolutionary algorithm named the partial solutions consideration based self-adaptive evolutionary algorithm (PSC-SEA) to address this issue;the proposed algorithm is applied to adjust the parameters of the neuro-fuzzy networks. More specifically, different from the existing evolution algorithms, the partial solutions consideration (PSC) tends to consider both the specializations and complementary relationships of the partial solutions from the complete solution to prevent converging to suboptimal solution. Moreover, a self-adaptive evolutionary algorithm (SEA) is proposed to dynamically adjust the searching space according to the performance. By doing so, the proposed evolutionary algorithm can consider the influence of partial solutions and provide a suitable searching space to increase the chances to meet the global solution. As shown in the results, the proposed evolutionary algorithm obtains better performance and smoother learning curves than other existing evolutionary algorithms. In other words, the proposed evolutionary algorithm can efficient tune the parameters of the neuro-fuzzy networks to meet the global solution. Base on the results, a framework is proposed to build a benchmark for developing the evolutionary algorithms that can not only consider the influence of partial solutions but also provide a suitable searching space. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： evolutionary algorithm Self adaptive Global solution Neuro-fuzzy networks

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DNA sequences design under many objective evolutionary algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14167-14183页

作者： Guo, Huaiyu Zhu, Donglin Zhou, Changjun Zou, Chengye Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

DNA computing is conducted through reactions between DNA molecules, the quality of DNA sequences is directly influence on reactions. Following previous works, there are five metrics to estimate quality of DNA sequences and one constraint to follow. evolutionary algorithms are widely applied in this field, conventional frames are often using multi-objective strategies to solve this problem. However, multi-objective strategies loss its efficiency in solving high dimensional problems especially Pareto Front is irregular. In this article, a many-objective evolutionary algorithm, R2HCAEMOA, is introduced to tackle with increased objective dimension. To increase diversity from beginning, chaotic mapping is applied to initialize decision variables of population. Since purpose of many-objective optimization algorithms is to find evenly distributed solution set on Pareto Front, decision makers are faced difficulty in solution selection. A method for choosing the most interesting solution from solution set is determined. Besides, an incremental scheme to generate a DNA sequence set is applied to enforce stability of evolutionary environment. The average values on each metrics are {0, 0, 56.00, 42.85, 0.15}, which the metrics are {continuity, hairpin, H-measure, similarity, variance of melting temperature}. Running time of our frame is significantly reduced compared with previous works. The results have shown our work is competitive among previous works and incline balanced value on each objective dimension.

关键词： DNA sequence design DNA computing evolutionary algorithm Many-objective optimization

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