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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Xiao, Dong Liao, Futao Zhang, Shaowei Wang, Hui Hu, Min Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

ISBN: (纸本)9789819770038;9789819770045

The artificial bee colony (ABC) algorithm has exhibited commendable performance in addressing single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs). However, limited research has been conducted in applying ABC to tackle many-objective optimization problems (MaOPs). The primary impediment arises from the inherent inability of traditional Pareto dominance to exert sufficient selection pressure, leading to a sharp increase in the proportion of incomparable solutions within the population. To address these challenges, this study proposes a novel many-objective ABC algorithm based on decision variable grouping (DVGABC) for MaOPs. Firstly, the decision variables are divided into two groups: convergence and diversity, updated during the employed bee stage and the onlooker bee stage, respectively. Then, a new fitness function based on strength Pareto dominance is proposed. Finally, the DTLZ benchmark suite is used to validate the effectiveness of DVGABC. The results obtained demonstrate the superiority of DVGABC over five other well-established algorithms on two performance metrics.

关键词： Swarm intelligence Artificial bee colony Many-objective optimization decision variable grouping

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Sparse large-scale multi-objective optimization algorithm based on impact factor assistance

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Hu, Ziyu Nie, Xuetao Sun, Hao Wei, Lixin Zhang, Jinlu Wang, Cong Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

In the real world, there exists a special category of multi-objective optimization problems with more than 1000 decision variables. However, only a few decision variables play a crucial role in optimizing the objective functions. Such problems are defined as sparse large-scale multi-objective optimization problems (SLSMOPs). Due to the difficulty in effectively identifying the non-zero positions of decision variables, traditional evolutionary optimization algorithms suffer from slow convergence speed and poor convergence effect, which means it is unable to efficiently obtain the Pareto optimal solution set. To address this challenge, the Impact Factor Assisted Algorithm (IFA) is proposed, which adopts a novel initial population strategy to generate sparse populations. Meanwhile, the impact factor of each decision variable is calculated, serving as a key basis for measuring the importance of each decision variable. During the algorithm's operation, the impact factors are iteratively updated to rationally group decision variables and guide population evolution. This approach can accurately identify the positions of non-zero decision variables. The experimental results on eight benchmark and real-world problems indicate that the algorithm outperforms several existing sparse large-scale multi-objective optimization algorithms (SLSMOEAs).

关键词： Evolutionary algorithm Impact factor decision variable grouping Multi-objective optimization problems Sparse large-scale optimization Instance selection

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Efficient Sparse Large-Scale Multiobjective Optimization Based on Cross-Scale Knowledge Fusion

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第11期54卷 6989-7001页

作者： Ding, Zhuanlian Chen, Lei Sun, Dengdi Zhang, Xingyi Liu, Wei Anhui Univ Sch Internet Hefei 230039 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands

Due to the curse of dimensionality and the unknown sparsity of search spaces, evolutionary algorithms face immense challenges in approximating optimal solutions for widely studied sparse large-scale multiobjective optimization problems (SLMOPs). Most bilevel encoding scheme (BLES)-based algorithms primarily focus on exploring sparsity in the binary layer, neglecting the real layer. Moreover, the interactions between two layers may be disregarded in these algorithms, thus the latent gap between the two encoding scales could lead to evolutionary ambiguity and performance limitations. To tackle the above issues, this article proposes a novel BLES-based collaborative algorithm using cross-scale knowledge fusion for SLMOPs. The algorithm integrates dual grouping and dual dimension reduction techniques via two subpopulations in a coevolutionary manner. Additionally, the interaction strategy is designed for each technique, leveraging the binary layer to guide the real layer, thus facilitating sufficient cross-scale cooperation. Extensive experiments on benchmark SLMOPs and four real-world applications validate the proposed algorithm's strong competitiveness in solving SLMOPs compared to state-of-the-art algorithms.

关键词： Encoding Optimization Vectors Dimensionality reduction Neural networks Evolutionary computation Collaboration Coevolution decision variable grouping dimension reduction sparse large-scale multiobjective optimization

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An evolutionary algorithm based on dynamic sparse grouping for sparse large scale multiobjective optimization

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INFORMATION SCIENCES 2023年 631卷 449-467页

作者： Zou, Yingjie Liu, Yuan Zou, Juan Yang, Shengxiang Zheng, Jinhua Xiangtan Univ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 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

Sparse large scale multiobjective optimization problems (sparse LSMOPs) contain numerous decision variables, and their Pareto optimal solutions' decision variables are very sparse (i.e., the majority of these solutions' decision variables are zero-valued). This poses grand challenges to an algorithm in converging to the Pareto set. Numerous evolutionary algorithms (EAs) tailored for sparse LSMOPs have been proposed in recent years. However, the final population generated by these EAs is not sparse enough because the location of the nonzero decision variables is difficult to locate accurately and there is insufficient interaction between the nonzero decision variables' locating process and the nonzero decision variables' optimizing process. To address this issue, we propose a dynamic sparse grouping evolutionary algorithm (DSGEA) that dynamically groups decision variables in the population that have a comparable amount of nonzero decision variables. Improved evolutionary operators are introduced to optimize the decision variables in groups. As a result, the population obtained by DSGEA can stably evolve towards the sparser Pareto optimal that has a precise location of nonzero decision variables. The proposed algorithm outperforms existing up-to-date EAs for sparse LSMOPs in experiments on three real-world problems and eight benchmark problems.

关键词： decision variable grouping Evolutionary algorithm Large scale multiobjective optimization Sparse multiobjective optimization

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping grouping of decision variables

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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A Particle Swarm Optimization Decomposition Strategy for Large Scale Global Optimization

A Particle Swarm Optimization Decomposition Strategy for Lar...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： McDevitt, Liam J. S. Ombuki-Berman, Beatrice M. Engelbrecht, Andries P. Brock Univ Dept Comp Sci St Catharines ON Canada Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Stellenbosch Univ Div Comp Sci Stellenbosch South Africa

ISBN: (纸本)9781665487689

Countless large-scale global optimization (LSGO) problems occur in an ever-growing number of professions. Cooperative co-evolution (CC) has been shown to assist in discovering encouraging solutions to such complicated issues effectively. CC does this by breaking down a massive problem into distinct smaller sub-problems, which, when solved, are combined to form a solution to the original problem. How a problem is broken down is referred to as decomposition. CCs performance on LSGO problems is highly dependent on the decomposition used. Numerous LSGO decomposition methods have been introduced to address this issue;however, finding a favourable decomposition is challenging, hinting there is still room for improvement and further exploration. This paper presents a new particle swarm optimization decomposition (PSOD) strategy for tackling LSGO problems. PSOD, in addition to its parameters, is explored, showing they provide statistically significant importance in their selection for a few of the CEC'2013 benchmark functions while being arbitrary for others. Further empirical studies compare PSOD's performance to other leading decomposition algorithms, resulting in PSOD performing best on the fully-separable Ackley function, being interchangeable for a few others, and performing competitively with the rest. PSOD attempts to combine Particle Swarm Optimization (PSO) and Cooperative Particle Swarm Optimization (CPSO) to evolve a decomposition while simultaneously optimizing an objective function for improved performance.

关键词： Particle Swarm Optimization Decomposition decision variable grouping Cooperate Co-evolution Large Scale Global Optimization

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Cooperative Co-evolutionary Algorithm for Dynamic Multi-objective Optimization Based on Environmental variable grouping

Cooperative Co-evolutionary Algorithm for Dynamic Multi-obje...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Xu, Biao Zhang, Yong Gong, Dunwei Rong, Miao China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China Huaibei Normal Univ Sch Math & Sci Huaibei 235000 Peoples R China

ISBN: (纸本)9783319410005;9783319409993

This paper presents a cooperative co-evolutionary dynamic multi-objective optimization algorithm, i.e., DNSGAII-CO for solving DMOPs based on environmental variable grouping. In this algorithm, a new method of grouping decision variables is first presented, in which all the decision variables are divided into two subcomponents according to whether they are interrelated with or without environment parameters. Then, when cooperatively optimizing the two subcomponents by using two populations, two prediction methods, i.e., differential prediction and Cauchy mutation, are employed to initialize them, respectively. The proposed algorithm is applied to a benchmark DMOPs, and compared with two state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of convergence and distribution.

关键词： Dynamic multi-objective optimization Cooperative co-evolution decision variable grouping

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Solve large-scale many-objective optimization problems based on dual analysis of objective space and decision space

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 70卷

作者： Zhang, Jinlu Wei, Lixin Fan, Rui Sun, Hao Hu, Ziyu Yanshan Univ Minist Educ Intelligent Control Syst & Intelligen Engn Res Ctr Qinhuangdao Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

A method based on decision variable partition is often utilized for large-scale optimization problems, but it only considers the convergence and diversity of decision variables. However, decision variables in the same group may still have different (strong or weak) correlations with objectives, even if they are only related to conver-gence or diversity. This situation not only reduces the accuracy of the grouping of decision variables, but also affects the allocation of computational resources. To address this issue, an algorithm based on dual analysis of objective space and decision space to group decision variables more accurately for solving large-scale many-objective optimization problems is proposed;this algorithm is called LM-DAS. It first divides decision variables into two categories: convergence-related variables and the diversity-related variables. Then, the correlation be-tween convergence-related variables and objective functions is analyzed. The decision space is divided into several subspaces according to the objective importance. Next, the decision variables are grouped using the method of interaction analysis between two level variables. Under the premise of ensuring the correctness of the grouping as much as possible, the algorithm can allocate more search resources to decision variables that are related to more important objective functions. The performance of LM-DAS is compared with four representative algorithms for 71 test instances. These instances involved 5-10 objectives and 200-1000 decision variables. The experimental results demonstrate that the proposed algorithm is competitive and effective.

关键词： Large-scale optimization Many-objective optimization Evolutionary algorithms decision variable grouping

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Multigroup strategy for well control optimization

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年 214卷

作者： Ma, Zhiwei Volkov, Oleg Durlofsky, Louis J. Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA

Practical production optimization often involves determining a large number of well control settings such that an objective function is maximized or minimized. When derivative-free population-based optimization algorithms are applied for these high-dimensional problems, inefficient performance or slow convergence can be observed. In this work, we develop a multigroup optimization strategy to mitigate these issues. The first step of the method entails the ranking of decision variables based on their statistical significance, as observed during a short preprocessing optimization run. variable groupings are then established. Group-by-group optimization, in which only a subset of decision variables is considered at each stage, is then conducted. As a final step, additional (limited) iterations with the full set of variables are performed. Particle swarm optimization (PSO), a global stochastic search method, is applied in this study, though the multigroup treatment is compatible with a range of algorithms. Detailed performance comparisons between multigroup strategies and standard optimization are presented for a model based on the 3D Brugge case. The problem involves 64 wells and 320 decision variables. The multigroup optimization framework includes several algorithmic parameters, and the use of different settings for these is shown to provide a range of promising procedures. Specifically, one such multigroup treatment requires 72% of the flow simulations of standard optimization, but it provides a final net present value (NPV) that is 2.6% higher than that from standard optimization. Another procedure provides essentially the same NPV as standard optimization but with only 40% of the flow simulations. The multigroup framework developed in this study can be extended to treat other challenging high-dimensional problems such as field development optimization.

关键词： Production optimization decision variable grouping Multigroup optimization Particle swarm optimization Reservoir simulation

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