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Activation Function-Assisted objective Space Mapping to Enhance Evolutionary Algorithms for large-scale many-objective optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 183-195页

作者： Deng, Qi Kang, Qi Zhou, MengChu Wang, Xiaoling Albeshri, Aiiad Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China New Jersey Inst Technol ECE Dept Newark NJ 07102 USA King Abdulaziz Univ Dept Comp Sci Jeddah 21481 Saudi Arabia

large-scale many-objective optimization problems (LSMaOPs) pose great difficulties for traditional evolutionary algorithms due to their slow search for Pareto-optimal solutions in huge decision space and struggle to balance diversity and convergence among numerous locally optimal solutions. An objective space linear inverse mapping method has successfully achieved great saving in execution time in solving LSMaOPs. Linear mapping is a fast and straightforward way, but fails to characterize a complex functional relationship. If we can enhance the expressive capacity of a mapping model, and further obtain a more general function approximator, can the evolutionary search based on objective space mapping be more efficient? To answer this interesting question, this work proposes to employ nonlinear activation functions widely used in neural networks so as to enhance the efficiency of objective space inverse mapping, thus efficiently generating excellent offspring population. A new evolutionary optimization framework based on decision variable analysis is proposed to solve LSMaOPs. In order to demonstrate its performance, this work carries out empirical experiments involving massive decision variables and many objectives. Experimental results prove its superiority over some representative and updated ones.

关键词： Activation function evolutionary algorithms large-scale many-objective optimization objective space mapping

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Recommendation Based on large-scale many-objective optimization for the Intelligent Internet of Things System

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IEEE INTERNET OF THINGS JOURNAL 2022年第16期9卷 15030-15038页

作者： Cao, Bin Zhang, Yatian Zhao, Jianwei Liu, Xin Skonieczny, Lukasz Lv, Zhihan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Warsaw Univ Technol Inst Comp Sci PL-00665 Warsaw Poland Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China

Recommender systems are of great significance for mining the data generated by the Internet of Things (IoT) and are important for the intelligent IoT systems. The traditional recommendation algorithms only consider the accuracy as the optimization objective. In this article, a many-objective optimization model consisting of the F1 measure, recommendation novelty, recommendation coverage, customer satisfaction, landmark similarity, and overfitting is constructed for recommendation. Then, to improve the recommendation performance, we propose to use a large-scale many-objective optimization algorithm based on problem transformation (LSMaOA) to optimize the matrix factorization model for the recommender system in the intelligent IoT systems. The experimental results show that LSMaOA is robust and can effectively optimize the model's six objectives. Compared with the knee point-driven evolutionary algorithm (KnEA), the grid-based evolutionary algorithm (GrEA), the large-scale multiobjective optimization framework (LSMOF), and the reference vector guided evolutionary algorithm (RVEA), the proposed algorithm can promote the F1 measure by 7.78%, 13.63%, 21.85%, and 28.63%, respectively.

关键词： optimization Recommender systems Internet of Things Matrix decomposition Evolutionary computation Pareto optimization Convergence Intelligent Internet of Things (IoT) systems Internet of Things (IoT) large-scale many-objective optimization recommender system

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A distance and cosine similarity-based fitness evaluation mechanism for large-scale many-objective optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Gao, Cong Li, Wenfeng He, Lijun Zhong, Lingchong Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Hainan 572000 Peoples R China

The fitness evaluation mechanism (FEM) based on nondominated sorting may lead to slow convergence when solving large-scale many -objective optimization problems (LSMaOPs), because the number of comparisons will become extremely large with the increase of optimization objectives and iterations. To solve this problem, a novel FEM based on distance and cosine similarity (DCS) is proposed in this paper. In each iteration, DCS needs to generate an ideal point after normalizing all objective functions. DCS consists of two important components, i.e., the distance and cosine similarity. The distance similarity that mines the similar relationship between solutions and ideal point is calculated as the convergence measure, and the cosine similarity that reflects the uniformity of solution distribution is calculated as the diversity measure. Furthermore, DCS fuses the distance and cosine similarity into a comprehensive similarity to fully evaluate the quality of solutions. Both theoretical analysis and empirical results indicate that DCS has lower computational complexity than other state-of-the-art FEMs. To verify the performance of DCS in solving LSMaOPs, DCS and the competitors are respectively embedded in genetic algorithm, and then compared on 56 test instances with 5-15 objectives and 100-1000 decision variables. The experimental results show the effectiveness and superiority of DCS.

关键词： Distance similarity Cosine similarity Fitness evaluation mechanism large-scale many-objective optimization

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A population hierarchical-based evolutionary algorithm for large-scale many-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Wang, Shiting Zheng, Jinhua Zou, Yingjie Liu, Yuan Zou, Juan Yang, Shengxiang Xiangtan Univ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Fac Sch Comp Sci Xiangtan 411105 Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & App Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

In large-scale many-objective optimization problems (LMaOPs), the performance of algorithms faces significant challenges as the number of objective functions and decision variables increases. The main challenges in addressing this type of problem are as follows: the large number of decision variables creates an enormous decision space that needs to be explored, leading to slow convergence;and the high-dimensional objective space presents difficulties in selecting dominant individuals within the population. To address this issue, this paper introduces an evolutionary algorithm based on population hierarchy to address LMaOPs. The algorithm employs different strategies for offspring generation at various population levels. Initially, the population is categorized into three levels by fitness value: poorly performing solutions with higher fitness (Ph), better solutions with lower fitness (Pl), and excellent individuals stored in the archive set (Pa). Subsequently, a hierarchical knowledge integration strategy (HKI) guides the evolution of individuals at different levels. Individuals in Pl generate offspring by integrating differential knowledge from Pa and P h , while individuals in Ph generate offspring by learning prior knowledge from P a . Finally, using a cluster-based environment selection strategy balances population diversity and convergence. Extensive experiments on LMaOPs with up to 10 objectives and 5000 decision variables validate the algorithm's effectiveness, demonstrating superior performance.

关键词： Evolutionary algorithms Hierarchical knowledge integration large-scale many-objective optimization Population hierarchy

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A memetic evolution system with statistical variable classification for large-scale many-objective optimization

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APPLIED SOFT COMPUTING 2022年第0期114卷 108158-108158页

作者： Ge, Hongwei Zhang, Naiqiang Sun, Liang Wang, Xia Hou, Yaqing Dalian Univ Technol Coll Comp Sci & Technol Dalian 116024 Peoples R China

Although the optimization algorithms have been widely studied, the large-scale many-objective optimization problems (LSMaOPs) remain challenging. Due to the existence of a large number of decision variables, it is necessary to carry out decision variable analysis. However, it is often difficult to discriminate the diversity-related and convergence-related variables when the problem has complex characteristics. Meanwhile, as the number of decision variables and the number of objectives increase, many algorithms will suffer from the convergence challenge. To overcome these challenges, this paper proposes a Memetic Evolution System with Statistical Variable Classification (MES-SVC). A statistical variable classification method is proposed to discriminate the convergence-related and the diversity-related variables. A memetic evolution system, which includes a memetic exploitation and exploration module, and a memetic elite imitation module, is proposed to make information guidance during the evolution, thereby promote convergence. The performance of MES-SVC is compared with the state-of -the-art algorithms on 50 test instances with 3 to 10 objectives and 300 to 1000 decision variables. Experimental studies demonstrate the promising performance of the proposed MES-SVC in terms of both diversity and convergence of solutions. (C) 2021 Published by Elsevier B.V.

关键词： large-scale many-objective optimization Decision variable analysis Memetic evolution system Memetic exploitation and exploration Memetic elite imitation

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A Multi-Population Multi-objective Evolutionary Algorithm Based on the Contribution of Decision Variables to objectives for large-scale Multi/many-objective optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第11期53卷 6998-7007页

作者： Xu, Ying Xu, Chong Zhang, Huan Huang, Lei Liu, Yiping Nojima, Yusuke Zeng, Xiangxiang Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

Most existing multiobjective evolutionary algorithms treat all decision variables as a whole to perform genetic operations and optimize all objectives with one population at the same time. Considering different control attributes, different decision variables have different optimization effects on each objective, so decision variables can be divided into convergence-or diversity-related variables. In this article, we propose a new metric called the optimization degree of the convergence-related decision variable to each objective to calculate the contribution objective of each decision variable. All decision variables are grouped according to their contribution objectives. Then, a multiobjective evolutionary algorithm, namely, decision variable contributing to objectives evolutionary algorithm (DVCOEA), has been proposed. In order to balance the convergence and diversity of the population, the DVCOEA algorithm combines the multipopulation multiobjective framework, where two different optimization strategies are designed to optimize the subpopulation and individuals in the external archive, respectively. Finally, DVCOEA is compared with several state-of-the-art algorithms on a number of benchmark functions. Experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.

关键词： optimization Evolutionary computation Convergence Statistics Sociology Diversity methods Clustering algorithms Decision variables analysis large-scale many-objective optimization large-scale multiobjective optimization multiobjective evolutionary algorithm multipopulation multiobjective evolutionary algorithm

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Proposal of a New Test Problem for large-scale Multi- and many-objective optimization

Proposal of a New Test Problem for Large-Scale Multi- and Ma...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Pang, Lie Meng Shang, Ke Chen, Longcan Ishibuchi, Hisao Chen, Weiyu Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

ISBN: (纸本)9781665442077

The research on large-scale multi- and many-objective optimization has received increasing attention in the evolutionary multi-objective optimization (EMO) community. A number of large-scale EMO algorithms based on different strategies (e.g., divide-and-conquer, coevolution, and dimensionality reduction) have been proposed over the last decade. The performance of the large-scale EMO algorithms was empirically evaluated using several benchmark test suites, including the ZDT, DTLZ, WFG, MaF, UF and LSMOP test suites. Even though these test suites are theoretically scalable to any number of decision variables, they are not necessarily appropriate for examining the performance of large-scale EMO algorithms. In fact, among these benchmark test suites, only the LSMOP test suite is specifically designed to test the performance of large-scale EMO algorithms. In this paper, we propose a new scalable multi- and many-objective test problem for examining large-scale EMO algorithms. The proposed test problem has the following features: 1) the number of objectives and decision variables can be arbitrarily specified;2) the interaction strength among the objectives can be adjusted by a correlation parameter. The performance of six EMO algorithms is examined on the new test problem. Our experimental results show that the proposed new test problem poses difficulties to some state-of-the-art large-scale EMO algorithms.

关键词： Evolutionary many-objective optimization large-scale many-objective optimization benchmark test problems

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