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7th International Conference on Swarm Intelligence (ICSI)

作者： Luo, Naili Luo, Jianping Li, Xia Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Commun & Informat Proc Shenzhen 518060 Peoples R China

ISBN: (纸本)9783319410005;9783319409993

In this paper, we present a novel conflict information measure used for objective space partitioning in solving many-objective optimization problems. Obtained from the current Pareto front approximation to estimate the degree of conflict between objectives, the conflict information is simply evaluated by counting the occurrence of conflict (improvement vs deterioration) out of all decision making sample pairs. The proposed method is compared with the latest objective space partitioning based on Pearson correlation coefficient conflict information. The results show that the proposed method outperforms the comparison method on identifying the conflicting objectives.

关键词： Many-objective optimization multi-objective evolutionary algorithm objective space partitioning Conflict information

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Pre-Trained Hybrid-Damping Message Passing Detector for multiple MIMO Scenarios 23

Pre-Trained Hybrid-Damping Message Passing Detector for Mult...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Ge, Yingmeng Xiong, Baiping Wang, Haibo Zhang, Zaichen Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratory Nanjing211111 China

ISBN: (纸本)9798350325959

Among various multiple-input multiple-output (MIMO) detection algorithms, message passing algorithms (MPAs) have been widely considered to balance performance and complexity. Damping factors can greatly affect MPAs' convergence behavior and stability. Deep neural network (DNN) based detectors with learnable damping factors have shown improved convergence performance in MIMO systems but require retraining in different system configurations. In this paper, a pre-trained hybrid-damping (PHD) message passing detection (MPD) for multi-scenarios using multi-objective evolutionary algorithm (MOEA) is proposed, which only requires one pre-training step and can be adapted to multiple scenarios. Numerical results indicate that the proposed PHD scheme can provide better convergence and flexibility than unified damping (UD) and DNN-based ones. Furthermore, the proposed PHD scheme can also be applied to other MPAs. © 2023 IEEE.

关键词： damping factor message passing MIMO detector multi-objective evolutionary algorithm

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Leveraging Human Insights by Combining multi-objective Optimization with Interactive Evolution

Leveraging Human Insights by Combining Multi-Objective Optim...

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作者： Joshua R. Christman Air Force Institute of Technology

学位级别：硕士

Deceptive fitness landscapes are a growing concern for evolutionary computation. Recent work has shown that combining human insights with short-term evolution has a synergistic effect that accelerates the discovery of solutions. While humans provide rich insights, they fatigue easily. Previous work reduced the number of hu- man evaluations by evolving a diverse set of candidates via intermittent searches for novelty. While successful at evolving solutions for a deceptive maze domain, this ap- proach lacks the ability to measure what the human evaluator identifies as important. The key insight here is that multi-objective evolutionary algorithms foster diversity, serving as a surrogate for novelty, while measuring user preferences. This approach, called Pareto Optimality-Assisted Interactive evolutionary Computation (POA-IEC), allows users to identify candidates that they feel are promising. Experimental results reveal that POA-IEC finds solutions in fewer evaluations than previous approaches, and that the non-dominated set is significantly more novel than the dominated set. In this way, POA-IEC simultaneously leverages human insights while quantifying their preferences.

关键词： evolutionary computation interactive evolutionary computation human-led search fitness pareto pareto front multi-objective multi-objective evolutionary algorithm non-dominated set deception non-objective search novelty search stepping stones serendipitous discovery

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A Novel Hybrid multi-objective Optimization Framework: Rotating the objective Space

A Novel Hybrid Multi-objective Optimization Framework: Rotat...

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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Qiu, Xin Huang, Ye Xu, Jian-Xin Tan, Kay Chen Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 117456 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

ISBN: (纸本)9783319135632;9783319135625

multi-objective evolutionary algorithms (MOEAs) are popular approaches for solving multi-objective problems (MOPs). One representative method is Non-dominated Sorting Genetic algorithm II (NSGA-II), which has achieved great success in the field by introducing non-dominated sorting into survival selection. However, as a common issue for dominance-based algorithms, the performance of NSGA-II will decline in solving problems with 3 or more objectives. This paper aims to circumvent this issue by incorporating the concept of decomposition into NSGA-II. A grouping-based hybrid multi-objective optimization framework is proposed for tackling 3-objective problems. Original MOP is decomposed into several scalar subproblems, and each group of population is assigned with two scalar subproblems as new objectives. In order to better cover the whole objective space, new objective spaces are formulated via rotating the original objective space. Simulation results show that the performance of the proposed algorithm is competitive when dealing with 3-objective problems.

关键词： multi-objective evolutionary algorithm hybrid decomposition

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multi-objective Energy Optimization Of Locomotions For Underwater Snake Robot

Multi-objective Energy Optimization Of Locomotions For Under...

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作者： Kenny Duy Luong Norwegian University of Science and Technology

学位级别：硕士

Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. Tis thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs). A well-known issue of the USRs, or any AUVs, is the long- term autonomy. To achieve this, energy efcient approaches are required. Many studies have considered single-objective optimization problems regarding the energy efciency of the USR, but almost none with multi-objective Optimization Problems (MOPs). Tis thesis presents MOPs of diferent locomotions of the USR. Te presented MOPs consider the energy efcient optimiza- tion of maximizing the forward velocity, while minimizing the power consumption of the USR. For computing the efcient motion paterns, two multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sort Genetic algorithm II (NSGA-II), and Hypervolume Estimation Algo- rithm for multi-objective Optimization (HypE) are applied. A challenging topic of the USR, is their adaptability of diferent locomotions. Diferent locomotions of the USR give rise to diferent search spaces for optimization. We present simulation studies of the two most common snake locomotions: (i) lateral undulation and (ii) eel-like motion. Furthermore, we also present and in- vestigate three altered motion patern of the USR. Te aim of the altered locomotions is to let the MOEAs generate efcient locomotions through evolutionary, which we do not know the gait of. From the simulation results, it turns out that one of the altered motion patern approximates a motion similar to the lateral undulation. Tis motion patern is generated based on Fourier se- ries. Te obtained simulation results are based on optimization with optimal Genetic algorithm (GA) parameters, found by numerous presimulations of the MOPs. Since this is multi-objective optimization problems, the end results will be in the form of Pareto fronts. Tese Pareto fronts can be used as trade-ofs for selecting th

关键词： Underwater snake robot Energy efciency multi-objective evolutionary algorithm Non-dominated Sort Genetic algorithm II Hypervolume Estimation algorithm for multi-objective Optimization Principal Component Analysis Partial Least Square Regression multi-objective Optimization Problem Pareto fronts Patern generators

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Decomposition-based multi-objective optimization approach for PPI network alignment

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KNOWLEDGE-BASED SYSTEMS 2022年 243卷 108527-108527页

作者： Menor-Flores, Manuel Vega-Rodriguez, Miguel A. Univ Extremadura Escuela Politecn Campus Univ S-N Caceres 10003 Spain

The alignment of protein-protein interaction (PPI) networks is in the spotlight of the research in bioinformatics. The main goal is to find structural or functional complexes that are evolutionarily conserved between species. Recent works in the area struggle to produce alignments of both good topological and functional quality, since those two objectives conflict more than expected. To this end, we introduce a decomposition-based multi-objective algorithm with two new problem-aware mutation operators, being each of them focused on the improvement of one of both objectives. The experiments have been performed over 10 scenarios of PPI network alignment with real data from five different species. The results have been evaluated with five quality metrics. The performed experiments have confirmed that the solutions resulting from our proposal obtain statistically significant improvements and are of higher quality than the solutions from related works.(c) 2022 The Author(s). Published by Elsevier B.V.& nbsp

关键词： multi-objective optimization multi-objective evolutionary algorithm based on decomposition Protein-protein interaction Network alignment Gene ontology consistency Symmetric substructure score

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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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A loss matrix-based alternating optimization method for sparse PU learning

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SWARM AND evolutionary COMPUTATION 2022年第0期75卷

作者： Qiu, Jianfeng Cai, Xiaoqiang Zhang, Lei Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230039 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230039 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China Anhui Univ Key Lab Intelligent Comp Signal Proc Minist Educ Hefei 230039 Peoples R China

Positive and unlabeled (PU) learning is an important research topic in machine learning area, whose aim is to learn a good classifier from PU data. Due to its wide applications, a variety of PU algorithms with promising performance have been proposed. However, few of them consider PU learning from high-dimensional PU data. To fill the gap, in this work, we focus on designing the sparse PU classifier. However, it is difficult to achieve it, since the labels of U samples are uncertain. To this end, a loss matrix-based alternating optimization method, named LMAO-PU is proposed, where a two-stage alternating optimization idea is suggested to solve the difficulty in sparse PU learning. Firstly, a loss matrix is designed to measure the classification performance of the PU classifier on U samples. Then, a two-stage alternating optimization under the guide of loss matrix is developed. Specifically, the first stage is to optimize the PU model, aiming to address the challenge of labels uncertainty of U samples, where two objectives, TPR (True Positive Rate) and a suggested LRU (Loss Rate on Unlabeled), are optimized simultaneously. The second stage is to perform the sparse model optimization, where Sparsity and ErrorRate are adopted as the objectives to obtain the sparse model. The two-stage optimization procedure mentioned above is carried alternately and the sparse PU classifier with high quality is finally achieved. Experiments on 10 high-dimensional datasets demonstrate the superiority of proposed method over six state-of-the-art baselines in terms of sparsity, accuracy and area under ROC curve.

关键词： Sparse PU learning Alternating optimization Loss matrix multi-objective evolutionary algorithm

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Cricketer?s tournament-wise performance prediction and squad selection using machine learning and multi-objective optimization ?

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Tirtho, Devopriya Rahman, Shafin Mahbub, Shahriar Ahsanullah Univ Sci & Technol Dhaka Bangladesh

The selection of a proper squad for a competition is vital in any sport, and cricket is no different. The diversity of squad formation is vital because those in charge of squad formation must consider all characteristics of the players. The squad must be broad and diverse enough for any event that lasts a long time since the squad must be competent in every situation, regardless of whether the side is playing at home or against. We aim to make the procedure as computationally efficient as possible. Machine Learning algorithms appear to have gone a long way in anticipating a system based on the information gained from the provided data. The article attempts to forecast player performance for a future tournament while considering most important characteristics of a cricket player. The prediction appears more accurate than an approximation. Several machine learning algorithms are utilized and analyzed to select the best one to use in our proposed work. The second phase employs multi-objective optimization (MOO) techniques to perceive optimum squads from a list of players based on anticipated performance and some additional attributes. However, the problem appears to have certain conflicting aspects that may be handled by multi -objective evolutionary algorithms (MOEA). Finally, a comparison is made between well-known MOEAs, and the results suggest that NSGA-II performs better for the problem.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Machine learning algorithm Prediction of performance Squad formation multi-objective evolutionary algorithm

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A multi-objective Community Detection algorithm for Directed Network Based on Random Walk

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IEEE ACCESS 2019年 7卷 162652-162663页

作者： Wen, Xuyun Lin, Ying Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Peoples R China

Uncovering community structure is an important technique for studying complex networks. While a large bulk of algorithms have been proposed for community detection in recent years, most of them were designed for undirected networks. Considering many real-world networks are by nature directed, it is necessary to develop community detection methods that can handle directed networks. In this work, we formulates a multi-objective framework for community detection in directed networks and proposes a multi-objective evolutionary algorithm for finding efficient solutions under this framework. Specifically, based on the theory that an efficient partition of directed networks should have larger network information flow within the community than that between different communities, we first designed two conflicting objective functions based on PageRank random walk, one of which is to maximize within-community transition probability, and the other is to minimize between-community transition probability. By optimizing these two objectives simultaneously, we modelled the problem of community detection as a multi-objective optimization problem, and then developed a novel multi-objective evolutionary algorithm to solve it. Particularly, to guarantee the capability of searching the optimal solution, our proposed method designed/adopted the directed-network-specific population initialization method and evolutionary operator by introducing label propagation algorithm into multi-objective genetic algorithm. Comparison with other four art-of-the-state algorithms, our method showed the competitive performance on both synthetic and real-world networks. Moreover, attributing to the multi-objective framework, the proposed method could generate multiple optimal network partitions in a single run, which provides a hierarchical description of community structure of the network.

关键词： Community detection directed network multi-objective evolutionary algorithm PageRank random walk

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