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Study on Optimization Parameters of Solid-Liquid Separator

IEEE ACCESS 2020年 8卷 85051-85066页

作者： Shi, Mingjiang Huang, Dongmei Zhang, Mengfei Huang, Zhiqiang Guo, Shun Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China

In order to achieve solid-liquid separation and recycling of drilling fluid during petroleum drilling, a new type of solid-liquid separation device was designed and developed to achieve high-efficiency solid-liquid separation and low cost. The working principle of the equipment was introduced, the mechanical model was established, and the sieve flow of drilling fluid and the movement of wet particles in the real situation were analyzed. The relationship between the processing capacity of the solid-liquid separator and the speed of the solid phase in the drilling fluid and the speed of the excitation motor and the rotary drum were given. multi-objective evolutionary algorithm was used to solve the multi-objective optimization model. Two typical evolutionary algorithms NSGA-II and MOEA/D were analyzed. An algorithm that solves this problem was selected and improved. A series of optimal solution sets for this problem were obtained by using the improved multi-objective evolutionary algorithm. Finally, based on the experimental prototype of the solid-liquid separator, a functional experiment was performed on the basis of the calculated optimal solution set. The experimental results show the feasibility of the multi-objective optimization model and algorithm.

关键词： multi-motor multi-objective evolutionary algorithm optimization solid-liquid separation

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Parallel MOEA Based on Consensus and Membrane Structure for Inferring Phylogenetic Reconstruction

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IEEE ACCESS 2020年 8卷 6177-6189页

作者： Zhang, Qianqian Zhang, Jun Zhong, Yue Ye, Congming Min, Xiaoping Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China Heilongjiang Prov Land Reclamat Headquarters Gen Rehabil Dept Harbin 150088 Peoples R China

In recent years, inferring phylogenies has attracted lots of attention in both academic community and various application fields. Phylogenetic inference usually consists of a couple of evolutionary relationships, which can be represented as a phylogenetic tree. The phylogenetic reconstruction problem can be defined as an optimization problem, targeting at finding the most eligible tree among all possible topologies according to a selected criterion. Since the combinatorial number of possible topologies exceeds tolerance, various heuristic and metaheuristic methods have been proposed to find approximate solutions according to the selected criterion. However, different criterions are based on different principle and conflict with each other basically. In this line, scholars has proposed multi-objective evolutionary algorithm (MOEA) based on diverse criteria. Nevertheless, MOEA has suffered unbearable time consumption due to its inherent drawbacks of computational complexity and convergence. By studying the independence between the sub-populations in each time-consuming step of MOEA, the steps without global information can be designed to be executed in parallel, which can fundamentally address computational problems. Effective parallel algorithms designed with the characteristics of modern multicore clusters can solve such problems. In this sense, we propose a parallelized multi-objective evolutionary algorithm (MOEA-MC) by deploying on Spark, which added consensus into evolutionary algorithm to improve the quality of convergence and used membrane structure to keep equal solutions under different weights. In order to assess the performance achieved by the proposal, we have performed comparison among different methods on three real-world datasets separately. The results have certified that the solutions derived from MOEA-MC are superior to traditional methods in all studied datasets. And parallelized MOEA-MC can get dominant position and optimal Pareto-frontier simulta

关键词： Consensus multi-objective evolutionary algorithm membrane structure phylogenetic reconstruction parallel algorithm

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multi-objective-Based Radiomic Feature Selection for Lesion Malignancy Classification

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第1期24卷 194-204页

作者： Zhou, Zhiguo Li, Shulong Qin, Genggeng Folkert, Michael Jiang, Steve Wang, Jing UT Southwestern Med Ctr Med Artificial Intelligence & Automat Lab Dallas TX 75390 USA UT Southwestern Med Ctr Dept Radiat Oncol Dallas TX 75390 USA Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China

objective: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. Methods: this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. Results: we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. Conclusion: the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. Significance: the proposed method is general and more effective radiomic feature selection strategy.

关键词： Feature extraction Lesions Entropy Informatics Sociology Statistics Linear programming Radiomics lesion malignancy classification feature selection multi-objective evolutionary algorithm evidential reasoning

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Methods That Optimize multi-objective Problems: A Survey and Experimental Evaluation

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IEEE ACCESS 2020年 8卷 80855-80878页

作者： Taha, Kamal Khalifa Univ Dept Elect & Comp Sci Abu Dhabi U Arab Emirates

Most current multi-optimization survey papers classify methods into broad objective categories and do not draw clear boundaries between the specific techniques employed by these methods. This may lead to the misclassification of unrelated methods/techniques into the same objective category. Moreover, most of these survey papers classify algorithms as independent of the specific techniques they employ. Toward this end, we introduce in this survey paper a methodology-based taxonomy that classifies multi-optimization methods into hierarchically nested, fine-grained, and specific classes. We provide a methodological taxonomy to classify methods into the following hierarchical fashion: objective categories objective functionsoptimization methodsoptimization sub-methods. We introduce a comprehensive survey on the methods that are contained under each optimization method, the optimization methods contained under each objective function, and objective functions contained under each objective category. We selected the objective functions that should be maximized for solving most real-word multi-objective optimization problems, which are pairs of the following: partitions separability, internal density, dynamic similarity, and structural similarity. For each optimization method, we surveyed the various algorithms in literature that pertain to the method. We experimentally compared and ranked the optimization methods that fall under each objective function, the objective functions that fall under each objective category, and the objective categories used for solving a specific optimization problem.

关键词： Linear programming Optimization methods Classification algorithms Sociology Statistics Taxonomy multi-objective optimization multi-objective problem multi-objective evolutionary algorithm objective function

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Optimization of subsurface models with multiple criteria using Lexicase Selection

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OPERATIONS RESEARCH PERSPECTIVES 2022年 9卷

作者： He, Yifan Aranha, Claus Hallam, Antony Chassagne, Romain Univ Tsukuba Sch Syst & Informat Engn Tsukuba Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Japan Heriot Watt Univ Energy Geosci Infrastructure & Soc Edinburgh Scotland

Seismic History Matching (SHM) is a key problem in the geosciences community, requiring optimal parameters of a subsurface model that match the observed data from multiple in-situ measurements. Therefore, the SHM problems are usually solved with multi-objective evolutionary algorithms (MOEAs). This group of algorithms optimize multiple objectives simultaneously, considering the trade-off between objectives. However, SHM requires the solutions that are good on all objectives rather than a trade-off. In this study, we propose a Differential Evolution algorithm using Lexicase Selection to solve the SHM problems. Unlike the MOEAs, this selection method pushes the solutions to perform well on all objectives. We compared this method with two MOEAs, namely Non-dominated Sorting Genetic algorithm II and Reference Vector-guided evolutionary algorithm, on two SHM problems. The results show that this method generates more solutions near the ground truth.

关键词： Seismic History Matching Lexicase Selection multi-objective optimization multi-objective evolutionary algorithm

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multi-objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin

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ENERGIES 2021年第5期14卷 1245-1245页

作者： Bouchekara, Houssem Rafik Al-Hana Shahriar, Mohammad Shoaib Javaid, Muhammad Sharjeel Sha'aban, Yusuf Abubakar Ramli, Makbul Anwari Muhammad Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

This paper presents an optimal design for a nanogrid/microgrid for desert camps in the city of Hafr Al-Batin in Saudi Arabia. The camps were designed to operate as separate nanogrids or to operate as an interconnected microgrid. The hybrid nanogrid/microgrid considered in this paper consists of a solar system, storage batteries, diesel generators, inverter, and load components. To offer the designer/operator various choices, the problem was formulated as a multi-objective optimization problem considering two objective functions, namely: the cost of electricity (COE) and the loss of power supply probability (LPSP). Furthermore, various component models were implemented, which offer a variety of equipment compilation possibilities. The formulated problem was then solved using the multi-objective evolutionary algorithm, based on both dominance and decomposition (MOEA/DD). Two cases were investigated corresponding to the two proposed modes of operation, i.e., nanogrid operation mode and microgrid operation mode. The microgrid was designed considering the interconnection of four nanogrids. The obtained Pareto front (PF) was reported for each case and the solutions forming this front were discussed. Based on this investigation, the designer/operator can select the most appropriate solution from the available set of solutions using his experience and other factors, e.g., budget, availability of equipment and customer-specific requirements. Furthermore, to assess the quality of the solutions found using the MOEA/DD, three different methods were used, and their results compared with the MOEA/DD. It was found that the MOEA/DD obtained better results (nondominated solutions), especially for the microgrid operation mode.

关键词： desert camps diesel generator nanogrid microgrid multi-objective evolutionary algorithm multi-objective optimization solar energy

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Application of a novel multi-objective optimisation method integrated with the artificial neural networks for optimum design of a plate heat exchanger

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AUSTRALIAN JOURNAL OF MECHANICAL ENGINEERING 2020年第1期18卷 1-15页

作者： Jilak, Ahmad Assareh, Ehsanolah Nedaei, Mojtaba Islamic Azad Univ Dept Mech Engn Dezful Branch Dezful Iran Univ Padua Dept Management & Engn Vicenza Italy Islamic Azad Univ Dept Energy Engn Grad Sch Environm & Energy Sci & Res Branch Tehran Iran

In the current study, a plate heat exchanger model is optimised using a multi-objective evolutionary algorithm/decomposition. Considering the importance of the thermal efficiency and the cost of manufacturing the heat exchangers, these two parameters are used as objective functions during the optimisation process. For the purpose of analysis, the epsilon-NTU method was applied to analyse the probability values for the pressure drop and efficiency of the heat exchanger. The experimental data for the analysis were extracted from a gas furnace in a ceramics factory located in the Kerman province. The results of the analysis reported 19.962% enhancement in the efficiency rate as well as 9.533% reduction in cost through the proposed model, which indicated a significant improvement. In another section of this study, by performing a Pareto front analysis, the distribution of the optimum points for the design variables was outlined. As well as that, the correlation between the objective functions and the design variables by employing the artificial neural networks was discussed. The obtained results were then compared with the two reference cases to achieve a better insight towards the new developed model scheme. At the last stage of study, an error analysis concluded that in most cases, the performance and cost error rates are close to zero with the maximum values being in the range of 0.6-1 and 0-0.13, respectively.

关键词： Flat fin heat exchangers plate heat exchangers artificial neural network multi-objective evolutionary algorithm decomposition (MOEA/D)

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Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm

Risk-adjusted portfolio optimisation using a parallel multi-...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Maguire, Phil O'Sullivan, Donal Moser, Philippe Dunne, Gavin Natl Univ Ireland Dept Comp Sci Maynooth Kildare Ireland

ISBN: (纸本)9781467318037

In this article we describe the use of a multi-objective evolutionary algorithm for portfolio optimisation based on historical data for the S&P 500. Portfolio optimisation seeks to identify manageable investments that provide a high expected return with relatively low risk. We developed a set of metrics for qualifying the risk/return characteristics of a portfolio's historical performance and combined this with an island model genetic algorithm to identify optimised portfolios. The algorithm was successful in selecting investment strategies with high returns and relatively low volatility. However, although these solutions performed well on historical data, they were not predictive of future returns, with optimised portfolios failing to perform above chance. The implications of these findings are discussed.

关键词： multi-objective evolutionary algorithm historical data Investments

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A decomposition-based multi-objective optimization approach for balancing the energy consumption of wireless sensor networks

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APPLIED SOFT COMPUTING 2021年 107卷 107365-107365页

作者： Nguyen Thi Tam Tran Huy Hung Huynh Thi Thanh Binh Le Trong Vinh Vietnam Natl Univ Univ Sci Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam

Wireless sensor networks consist of many sensor nodes with limited resources and computing capability. Thus, managing energy consumption to prolong network lifetime is a critical issue. Several approaches have been proposed to extend the network lifetime, one of which involves deploying relay nodes to transfer data from sensors to the base station. However, the limited number of relay nodes is a challenge that often goes overlooked. This paper examines the problem of optimizing the network lifetime and the number of relay nodes in three-dimensional terrains. A novel algorithm called MOEA/D-LS is proposed with the aim of obtaining a better tradeoff between two objectives. The algorithm is a hybridization between multiobjective evolutionary algorithm based on decomposition, and a special local search to optimize the former's subproblems. Simulation results on 3D datasets show that the proposed algorithm has a significantly better performance compared with existing algorithms on all measured metrics. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Network lifetime multi-objective evolutionary algorithm

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An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand

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KNOWLEDGE-BASED SYSTEMS 2021年 230卷 107378-107378页

作者： Niu, Yunyun Kong, Detian Wen, Rong Cao, Zhiguang Xiao, Jianhua China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Singapore Inst Mfg Technol Singapore 138634 Singapore Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of "route failure" caused due to stochastic customer demands, we propose a novel chromosome representation based on priority with bubbles. Moreover, an efficient nondominated sort using a sequential search strategy (ENS-SS) in conjunction with some heuristic operations are leveraged to handle the multi-objective property of the problem. Our algorithm is evaluated on the instances of modified Solomon VRP benchmark. Experimental results show that the proposed IMOLEM is capable to find better Pareto front of solutions and also deliver superior performance to other evolutionary algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Vehicle routing problems Stochastic demand Learnable evolution model multi-objective evolutionary algorithm

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