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A multiobjective immune algorithm based on a multiple-affinity model

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第1期202卷 60-72页

作者： Hu, Zhi-Hua Shanghai Maritime Univ Logist Res Ctr Shanghai 200135 Peoples R China

This paper presents a new multiobjective immune algorithm based on a multiple-affinity model inspired by immune system (MAM-MOIA). The multiple-affinity model builds the relationship model among main entities and concepts in multiobjective problems (MOPs) and multiobjective evolutionary algorithms (MOEAs), including feasible solution, variable space, objective space, Pareto-optimal set, ranking and crowding distance. In the model, immune operators including clonal proliferation, hypermutation and immune suppression are designed to proliferate superior antibodies and suppress the inferiors. MAM-MOIA is compared with NSCA-II, SPEA2 and NNIA in solving the ZDT and DTLZ standard test problems. The experimental study based on three performance metrics including coverage of two sets, convergence and spacing proves that MAM-MOIA is effective for solving MOPs. (C) 2009 Elsevier B.V. All rights reserved.

关键词： multiobjective optimization multiobjective immune algorithm multiobjective evolutionary algorithm Crowding distance Multiple-affinity model

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multiobjective evolutionary-based multi-kernel learner for realizing transfer learning in the prediction of HIV-1 protease cleavage sites

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SOFT COMPUTING 2020年第13期24卷 9727-9751页

作者： Singh, Deepak Sisodia, Dilip Singh Singh, Pradeep Natl Inst Technol Dept Comp Sci & Engn Raipur CG India

Due to the unavailability of adequate patients and expensive labeling cost, many real-world biomedical cases have scarcity in the annotated data. This holds very true for HIV-1 protease specificity problem where only a few experimentally verified cleavage sites are present. The challenge then is to exploit the auxiliary data. However, the problem becomes more complicated when the underlying train and test data are generated from different distributions. To deal with the challenges, we formulate the HIV-1 protease cleavage site prediction problem into a bi-objective optimization problem and solving it by introducing a multiobjective evolutionary-based multi-kernel model. A solution for the optimization problem will lead us to decide the optimal number of base kernels with the best pairing of features. The bi-objective criteria encourage different individual kernels in the ensemble to mitigate the effect of distribution difference in training and test data with the ideal number of base kernels. In this paper, we considered eight different feature descriptors and three different kernel variants of support vector machines to generate the optimal multi-kernel learning model. Non-dominated sorting genetic algorithm-II is employed with bi-objective of achieving a maximum area under the receiver operating characteristic curve simultaneously with a minimum number of features. To validate the effectiveness of the model, the experiments were performed on four HIV-1 protease datasets. The performance comparison with fifteen state-of-the-art techniques on average accuracy and area under curve has been evaluated to justify the improvement of the proposed model. We then analyze Friedman and post hoc tests to demonstrate the significant improvement. The result obtained following the extensive experiment enumerates the bi-objective multi-kernel model performance enhancement on within and cross-learning over the other state-of-the-art techniques.

关键词： HIV-1 protease Multi-kernel multiobjective evolutionary algorithm Transfer learning

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A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing

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APPLIED SOFT COMPUTING 2013年第5期13卷 2960-2969页

作者： Kong, Weijian Chai, Tianyou Yang, Shengxiang Ding, Jinliang Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England

The supply trajectory of electric power for submerged arc magnesia furnace determines the yields and grade of magnesia grain during the manufacture process. As the two production targets (i.e., the yields and the grade of magnesia grain) are conflicting and the process is subject to changing conditions, the supply of electric power needs to be dynamically optimized to track the moving Pareto optimal set with time. A hybrid evolutionary multiobjective optimization strategy is proposed to address the dynamic multiobjective optimization problem. The hybrid strategy is based on two techniques. The first one uses case-based reasoning to immediately generate good solutions to adjust the power supply once the environment changes, and then apply a multiobjective evolutionary algorithm to accurately solve the problem. The second one is to learn the case solutions to guide and promote the search of the evolutionary algorithm, and the best solutions found by the evolutionary algorithm can be used to update the case library to improve the accuracy of case-based reasoning in the following process. Due to the effectiveness of mutual promotion, the hybrid strategy can continuously adapt and search in dynamic environments. Two prominent multiobjective evolutionary algorithms are integrated into the hybrid strategy to solve the dynamic multiobjective power supply optimization problem. The results from a series of experiments show that the proposed hybrid algorithms perform better than their component multiobjective evolutionary algorithms for the tested problems. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Dynamic multiobjective optimization Hybrid strategy multiobjective evolutionary algorithm Case-based reasoning Manufacture process of magnesia grain

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Surrogate-Based Stochastic multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty

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WATER RESOURCES MANAGEMENT 2021年第5期35卷 1479-1497页

作者： Han, Zheng Lu, Wenxi Fan, Yue Xu, Jianan Lin, Jin Jilin Univ Minist Educ Key Lab Groundwater Resources & Environm Changchun Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun Peoples R China Jilin Univ Coll New Energy & Environm Changchun Peoples R China Nanjing Hydraul Res Inst Nanjing Peoples R China

Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximizing the total pumping rate, minimizing the total injection rate, and minimizing the solute mass increase are considered in the optimization model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is utilized to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimization model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.

关键词： Seawater intrusion Uncertainty Simulation-optimization Multigene genetic programming Groundwater management multiobjective evolutionary algorithm

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multiobjective flow shop deteriorating scheduling problem via an adaptive multipopulation genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2018年第14期232卷 2641-2650页

作者： Fu, Yaping Wang, Hongfeng Huang, Min Ding, Jinliang Tian, Guangdong Qingdao Univ Coll Automat Engn Inst Complex Sci Qingdao Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China Jilin Univ Coll Transportat Changchun Jilin Peoples R China

Recently, the ow shop scheduling problem with deteriorating jobs has gained increasing concern from academic communities and industrial areas. The de- teriorating job is that the processing time of the jobs depends on their starting time. Due to the essential complexity of this problem, most studies focus on the two and three stages setting, and there are few studies that have considered a multiple stage setting. In this paper, a multiobjective ow shop deteriorating scheduling problem is considered, where the objectives are to minimize the makespan and the total tardiness, simultaneously. In order to solve it e_ciently, a novel adaptive multipopulation mul- tiobjective genetic algorithm is proposed. In the proposed algorithm, multiple scalar optimization problems of the multiobjective ow shop deteriorating scheduling prob-lem are developed, which will be introduced into the iteration course in multiple stages. An adaptive multipopulation strategy is designed to construct multiple subpopulations to search the optimal solutions of several scalar optimization problems in parallel. In addition, some special strategies, i.e. selection, crossover, mutation and the evolution of external archives are designed to improve the performance of the adaptive multi-population multiobjective genetic algorithm for the investigated multiobjective ow shop deteriorating scheduling problem. Based on a set of test instances on the multiob-jective ow shop deteriorating scheduling problem, simulation experiments are carried out to examine the performance of adaptive multipopulation multiobjective genetic al-gorithm in comparison with several peer multiobjective evolutionary algorithms. The experimental results show that the proposed adaptive multipopulation multiobjective genetic algorithm performs well when solving the multiobjective ow shop deteriorating scheduling problem.

关键词： Flow shop deteriorating scheduling problem deteriorating effect multiobjective evolutionary algorithm genetic algorithm multipopulation

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multiobjective optimisation design for enterprise system operation in the case of scheduling problem with deteriorating jobs

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ENTERPRISE INFORMATION SYSTEMS 2016年第3期10卷 268-285页

作者： Wang, Hongfeng Fu, Yaping Huang, Min Wang, Junwei Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

The operation process design is one of the key issues in the manufacturing and service sectors. As a typical operation process, the scheduling with consideration of the deteriorating effect has been widely studied;however, the current literature only studied single function requirement and rarely considered the multiple function requirements which are critical for a real-world scheduling process. In this article, two function requirements are involved in the design of a scheduling process with consideration of the deteriorating effect and then formulated into two objectives of a mathematical programming model. A novel multiobjective evolutionary algorithm is proposed to solve this model with combination of three strategies, i.e. a multiple population scheme, a rule-based local search method and an elitist preserve strategy. To validate the proposed model and algorithm, a series of randomly-generated instances are tested and the experimental results indicate that the model is effective and the proposed algorithm can achieve the satisfactory performance which outperforms the other state-of-the-art multiobjective evolutionary algorithms, such as nondominated sorting genetic algorithm II and multiobjective evolutionary algorithm based on decomposition, on all the test instances.

关键词： Operation process design enterprise system multiobjective scheduling deteriorating effect multiobjective evolutionary algorithm

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multiobjective optimization algorithm with objective-wise learning for continuous multiobjective problems

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2015年第5期6卷 571-585页

作者： Wang, Jiahai Zhong, Chenglin Zhou, Ying Zhou, Yalan Sun Yat Sen Univ Dept Comp Sci Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Finance & Econ Informat Sci Sch Guangzhou 510320 Guangdong Peoples R China

Most of multiobjective optimization algorithms consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, in MOPs, different objective functions may possess different properties. Hence, it can be beneficial to build objective-wise optimization strategy for each objective separately. In this paper, we firstly propose a single objective guided multiobjective optimization (SOGMO) framework to solve continuous MOPs. In SOGMO framework, a solution is selected from archive, and then objective-wise learning strategy is developed to promote the evolution of each objective of the selected solution. Thus, all the objectives of the considered solution can be simultaneously optimized in parallel by the cooperation of objective-wise learning process. A specific instantiation of the SOGMO framework is implemented, where a neighborhood field optimization (NFO) algorithm, as objective-wise learning strategy, and dominance archive are designed. The proposed SOGMO implementation, called SOGMO-NFO, is systematically compared with several state-of-the-art multiobjective evolutionary algorithms (MOEA). Simulation results on 13 benchmark problems from CEC 2009 competition show that SOGMO-NFO is better than the compared MOEAs.

关键词： multiobjective optimization multiobjective evolutionary algorithm Objective-wise learning strategy

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multiobjective Groundwater Management Using evolutionary algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2009年第2期13卷 229-242页

作者： Siegfried, Tobias Bleuler, Stefan Laumanns, Marco Zitzler, Eckart Kinzelbach, Wolfgang Columbia Univ Earth Inst New York NY 10027 USA ETH Swiss Fed Inst Technol Comp Engn & Networks Lab TIK CH-8092 Zurich Switzerland ETH Swiss Fed Inst Technol IFOR CH-8092 Zurich Switzerland ETH Swiss Fed Inst Technol Inst Environm Engn IFU CH-8092 Zurich Switzerland

Sustainable management of groundwater resources is of crucial importance for regions where freshwater supply is naturally limited. Long-term planning of groundwater usage requires computer-based decision support tools: on the one hand, they must be able to predict I he complex system dynamics with sufficient accuracy, on the other, they must: allow exploring management scenarios with respect: to different criteria such as sustainability, cost, etc. In this paper, we present a multiobjective evolutionary algorithm for groundwater management that optimizes the placement and the operation of pumping facilities over time, while considering multiple neighboring regions which are economically independent. The algorithm helps in investigating the cost tradeoffs;between the different regions by providing an approximation of the Pareto-optimal set, and its capabilities are demonstrated on a three-region problem. The application of the proposed methodology can also serve as a benchmark problem as shown in this paper. The corresponding implementation is freely available as a precompiled module at http://***/pisa.

关键词： Benchmark application economic externalities groundwater management multiobjective evolutionary algorithm Pareto set approximation PISA

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Issues in parallelizing multiobjective evolutionary algorithms for real world applications 02

Issues in parallelizing multiobjective evolutionary algorith...

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Proceedings of the 2002 ACM symposium on Applied computing

作者： David A. Van Veldhuizen Jesse B. Zydallis Gary B. Lamont Air Force Materiel Command Wright-Patterson AFB OH Air Force Institute of Technology Wright-Patterson AFB (Dayton) OH

ISBN: (纸本)9781581134452

The concepts of efficiency and effectiveness must be addressed in conducting research into using a evolutionary algorithm (EA) for optimization problems. The increased use of evolutionary approaches for real-world applications, containing multiple objectives and high dimensionality, has led to the design and generation of a number of multiobjective evolutionary algorithms (MOEA). When analyzing these algorithms, the issues of effectiveness and efficiency are extremely important and typically drive the urge to parallelize these algorithms. The parallelization of MOEAs is a relatively new concept, with few researchers contributing work in this area. This parallelization process is not a simple task and involves the analysis of various parallel models and the parameters associated with these models. This paper presents a thorough analysis of the various parallel MOEA models, the issues associated with these models and recommendations for using these models in MOEAs. In particular, these parallelization concepts are applied to the multiobjective Messy Genetic algorithm II.

关键词： parallel algorithms multiobjective evolutionary algorithm

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Research on multiobjective capacity configuration optimization of grid-connected wind-solar-storage microgrid system based on improved BWO algorithm

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ENERGY SCIENCE & ENGINEERING 2024年第6期12卷 2365-2384页

作者： Wang, Ziheng Wang, Tao Niu, Qunfeng Wu, Jianfeng Li, Mingwei Zhu, Shuaiqi Henan Univ Technol Sch Elect Engn Zhengzhou 450001 Peoples R China Beijing Boshenkang Technol Co Ltd Beijing Peoples R China Henan Univ Technol Logist Serv Ctr Zhengzhou Peoples R China

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind-solar-storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition-based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision-makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid-connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind-solar-storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low-carbon power and regional economic development. The best-found levelized cost of energy for the wind-solar-storage microgrid system is 0.192 yuan/kWh.

关键词： beluga whale optimization algorithm capacity optimization microgrid multiobjective evolutionary algorithm renewable energy system

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