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Adaptive neighborhood selection for many-objective optimization problems

APPLIED SOFT COMPUTING 2018年 64卷 186-198页

作者： Zou, Juan Zhang, Yuping Yang, Shengxiang Liu, Yuan Zheng, Jinhua Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Informat Engn Coll Xiangtan Hunan Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England LED Lighting Res & Technol Ctr Guizhou TongRen Tongren 554300 Guizhou Peoples R China

It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization problems (MaOPs), which have more than three objectives. Therefore, a more valid selection method is needed to balance convergence and distribution. This paper presents a many objective evolutionary algorithm, called Adaptive Neighborhood Selection for many-objective evolutionary algorithm(ANS-MOEA), to deal with MaOPs. This method defines the performance of each individual by two types of information, convergence information (CI) and distribution information (DI). In the critical layer, a well-converged individual is selected first from the population, and its neighbors, calculated by DI, are pushed into neighbor collection (NC) soon afterwards. Then, the proper distribution of the population is ensured by competition individuals with large DI go back to the population and individuals with small DI remain in the collection. Four state-of-the-art MaOEAs are selected as the competitive algorithms to validate ANS-MOEA. The experimental results show that ANS-MOEA can solve a MaOP and generate a set of remarkable solutions to balance convergence and distribution. (C) 2017 Elsevier B.V. All rights reserved.

关键词： many-objective optimization problems Critical layer Distribution Convergence Neighborhoods Selection mechanism

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Hyperplane-Approximation-Based Method for many-objective optimization problems with Redundant objectives

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EVOLUTIONARY COMPUTATION 2019年第2期27卷 313-344页

作者： Li, Yifan Liu, Hai-Lin Goodman, E. D. Guangdong Univ Technol Sch Appl Math Guangzhou 510520 Guangdong Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act NSF DBI 0939454 E Lansing MI 48824 USA

For a many-objective optimization problem with redundant objectives, we propose two novel objective reduction algorithms for linearly and, nonlinearly degenerate Pareto fronts. They are called LHA and NLHA respectively. The main idea of the proposed algorithms is to use a hyperplane with non-negative sparse coefficients to roughly approximate the structure of the PF. This approach is quite different from the previous objective reduction algorithms that are based on correlation or dominance structure. Especially in NLHA, in order to reduce the approximation error, we transform a nonlinearly degenerate Pareto front into a nearly linearly degenerate Pareto front via a power transformation. In addition, an objective reduction framework integrating a magnitude adjustment mechanism and a performance metric sigma* are also proposed here. Finally, to demonstrate the performance of the proposed algorithms, comparative experiments are done with two correlation-based algorithms, LPCA and NLMVUPCA, and with two dominance-structure-based algorithms, PCSEA and greedy delta-MOSS, on three benchmark problems: DTLZ5(I,M), MAOP(I,M), and WFG3(I,M). Experimental results show that the proposed algorithms are more effective.

关键词： many-objective optimization problems objective reduction hyperplane approximation power transformation degenerate PF

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A knowledge guided bacterial foraging optimization algorithm for many-objective optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第23期34卷 21275-21299页

作者： Yang, Cuicui Weng, Yannan Ji, Junzhong Wu, Tongxuan Beijing Univ Technol Fac Informat Technol Coll Comp Sci Beijing Municipal Key Lab Multimedia & Intelligen Beijing Peoples R China

Despite that evolutionary and swarm intelligence algorithms have achieved considerable success on multi-objective optimization problems, they face huge challenges when dealing with many-objective optimization problems (MaOPs). There is an urgent call for effective evolutionary and swarm intelligence algorithms for MaOPs. Inspired by the satisfactory performance of bacterial foraging optimization (BFO) on the single-objective optimization problems, this paper extends BFO to deal with MaOPs and proposes a knowledge guided BFO for MaOPs (called as KLBFO). Firstly, KLBFO learns promising direction knowledge based on group decision making idea to guide the population to converge toward proper directions. Secondly, KLBFO learns elite knowledge by a new biological mechanism to accelerate the population to converge. Thirdly, KLBFO learns density knowledge by a new diversity management strategy based on orthogonal grid to produce well-distributed solutions. The performance of KLBFO is comprehensively evaluated by comparing it with eight state-of-the-art algorithms on two suites of test problems and one real-world problem. The empirical results have validated the superior performance of KLBFO for MaOPs.

关键词： many-objective optimization problems Bacterial foraging optimization Consensus directions Orthogonal nearest neighbor Elite knowledge

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An improved MOEA/D design for many-objective optimization problems

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APPLIED INTELLIGENCE 2018年第10期48卷 3839-3861页

作者： Zheng, Wei Tan, Yanyan Meng, Lili Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Shandong Normal Univ Inst Data Sci & Technol Jinan 250358 Shandong Peoples R China

MOEA/D is one of the most popular multi-objective evolutionary algorithms. To extend the effective application scope of MOEA/D for high-dimensional objectives, an improved MOEA/D design for many-objective optimization problems, named I-MOEA/D, is proposed in this paper. Comparing with the original MOEA/D, we redesigned the weight vectors used in the subproblems, making the distribution of the weight vectors broader and more effective to ensure the diversity and convergence of solutions in the objective space. Moreover, a new decomposition approach, called the weighted mixture-style method, which combines the advantages of the weighted sum decomposition and the Tchebycheff decomposition approaches, is adopted in I-MOEA/D to improve the effectiveness of the algorithm. A three-part experimental comparison using DTLZ1-DTLZ4, with the number of objectives ranging from three to fifteen, is performed. Experimental results verify the effectiveness of each strategy and reveal that the proposed I-MOEA/D method achieves better performance than the other related state-of-the-art algorithms in solving this type of many-objective optimization problems.

关键词： many-objective optimization problems Weight vector design Decomposition Evolutionary algorithm

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NSLS with the Clustering-Based Entropy Selection for many-objective optimization problems 18th

NSLS with the Clustering-Based Entropy Selection for Many-Ob...

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18th International Conference on Intelligent Computing (ICIC)

作者： Ma, Zhaobin Ding, Bowen Zhang, Xin Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214000 Jiangsu Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi 214000 Jiangsu Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

ISBN: (纸本)9783031138706;9783031138690

The multi-objective optimization algorithm based on nondominated sorting and local search (NSLS) has shown great competitiveness in the most multi-objective optimization problems. NSLS can obtain the Pareto-optimal front with better distribution and convergence than other traditional multi-objective optimization algorithms. However, the performance of NSLS degrades rapidly when facing the many-objective optimization problems (MaOPs). This paper proposes another version of NSLS, named NSLS with the Clustering-based Entropy Selection (NSLS-CE), which replaces the farthest-candidate approach with the clustering-based entropy selection approach. The concept of clustering-based entropy is proposed to measure the distribution of populations, which is implemented by the k-means clustering algorithm. Besides, to reduce the time complexity of the proposed clustering-based entropy selection approach, we apply the thermodynamic component replacement strategy. In order to prove the efficacy of NSLS-CE for solving MaOPs, the experiment is carried out on eighteen instances with three different objective numbers. The experimental results indicate that NSLS-CE can obtain Pareto solutions with better convergence and better distribution than NSLS.

关键词： Clustering-based entropy selection Local search many-objective optimization problems

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Behavior of EMO Algorithms on many-objective optimization problems with Correlated objectives

Behavior of EMO Algorithms on Many-Objective Optimization Pr...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Ishibuchi, Hisao Akedo, Naoya Ohyanagi, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781424478354

Recently it has been pointed out in many studies that evolutionary multi-objective optimization (EMO) algorithms with Pareto dominance-based fitness evaluation do not work well on many-objective problems with four or more objectives. In this paper, we examine the behavior of well-known and frequently-used EMO algorithms such as NSGA-II, SPEA2 and MOEA/D on many-objective problems with correlated or dependent objectives. First we show that good results on many-objective 0/1 knapsack problems with randomly generated objectives are not obtained by Pareto dominance-based EMO algorithms (i.e., NSGA-II and SPEA2). Next we show that the search ability of NSGA-II and SPEA2 is not degraded by the increase in the number of objectives when they are highly correlated or dependent. In this case, the performance of MOEA/D is deteriorated. As a result, NSGA-II and SPEA2 outperform MOEA/D with respect to the convergence of solutions toward the Pareto front for some many-objective problems. Finally we show that the addition of highly correlated or dependent objectives can improve the performance of EMO algorithms on two-objective problems in some cases.

关键词： Evolutionary multi-objective optimization (EMO) many-objective optimization problems multi-objective 0/1 knapsack problems correlated objectives independent objectives

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A Study on Two-Step Search based on PSO to Improve Convergence and Diversity for many-objective optimization problems

A Study on Two-Step Search based on PSO to Improve Convergen...

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IEEE Congress on Evolutionary Computation

作者： Hirano, Hiroyuki Yoshikawa, Tomohiro Nagoya Univ Grad Sch Engn Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781479904549;9781479904532

Particle Swarm optimization (PSO) is one of the most effective search methods in optimization problems. Multi-objective optimization problems (MOPs) has been focused on and PSO researches applied to MOPs have been reported. On the other hand, the problem that the search performance using conventional methods for MOPs becomes low is reported in many-objective optimization problems (MaOPs) which have four or more objective functions. The authors have proposed two-step search method based on PSO for MaOPs. In the first step, it divides the population into some groups, and each group performs the single objective search for each objective function and the center of them. In the second step, the search is performed to acquire the diversity of Pareto solutions by PSO search with the goal, global-best, based on the solutions acquired in the first step. This paper defines the real coded multi-objective knapsack problem and studies the performance of the proposed method applied to this problem. The experimental results shows that the search of the first step for high convergence and that of the second step for large diversity aimed in the proposed method works well. It also shows that the proposed method is superior to other conventional methods especially in terms of the convergence in MaOPs.

关键词： many-objective optimization problems Two-Step Search real coded multi-objective knapsack problem Particle Swarm optimization Multi-objective optimization problems global-best

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A non-dominated sorting based evolutionary algorithm for many-objective optimization problems

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SCIENTIA IRANICA 2021年第6期28卷 3293-3314页

作者： Mane, S. U. Rao, M. R. Narasinga Deemed Univ Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

The optimization problems with more than three objectives are many-objective optimization problems (MaOPs) that exist in various scientific and engineering domains. The existing multi-objective evolutionary algorithms are not found effective in addressing the MaOPs. Its limitations initiated the need to develop an algorithm that efficiently solves MaOPs. The proposed work presents the design of the many-objective Hybrid Differential Evolution (MaOHDE) algorithm to address MaOPs. Initially, two multi-objective evolutionary algorithms viz. Non-dominated Sorting based Multi-objective Differential Evolution (NS-MODE) and Non-dominated Sorting based Multi-objective Particle Swarm optimization (NS-MOPSO) algorithms were designed. These algorithms were developed by incorporating the non-dominated sorting approach from Non-dominated Sorting-based Genetic Algorithm II (NSGA-II), the ranking approach, weight vector, and reference points. Tchebycheff-a decomposition-based approach, was applied to decompose the MaOPs. The MaOHDE algorithm was developed by hybridizing the NS-MODE with the NS-MOPSO algorithm. The strength of the presented approach was determined using 20 instances of DTLZ functions, and its effectiveness and efficiency were verified upon its comparison with the recently developed state of algorithms existing in the literature. From the results, it is observed that the MaOHDE responds better than its competitors or is competitive for most of the test instances and the convergence rate is also good. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： many-objective hybrid differential evolution algorithm Non-dominated sorting Decomposition-based approach Differential evolution algorithm Particle swarm optimization algorithm many-objective optimization problems

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Rank-based multimodal immune algorithm for many-objective optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Zhang, Hainan Gan, Jianhou Zhou, Juxiang Gao, Wei Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Yunnan Peoples R China Yunnan Normal Univ Key Lab Educ Informatizat Nationalities Minist Educ Kunming 650500 Yunnan Peoples R China Yunnan Normal Univ Yunnan Key Lab Smart Educ Kunming 650500 Yunnan Peoples R China

The immune algorithm (IA) is a prestigious heuristic algorithm based on a model of an artificial immune system, and the IA has shown promising results in the multi -objective optimization field. However, the algorithm's low search ability in high -dimensional space and the clone assignment metric problem must be addressed. Thus, to solve these problems, we propose a rank -based multimodal immune algorithm (RMIA) for many -objective optimization problems. To alleviate the clone assignment metric problem, we design a novel vaccine selection mechanism, which is a rank -based clone selection method. We also propose a dynamic age -based elimination mechanism and a multimodal mutation strategy to address the poor searching ability of the IA in high -dimensional space, where the former is eliminated randomly via roulette in terms of the survival time and the advantages of antibodies in the population, and the latter adopts different mutation strategies based on the different states of antibodies. The proposed algorithm was evaluated and compared to multiple advanced multi -objective optimization immune algorithms (MOIAs) and many -objective optimization evolutionary algorithms (MaOEAs) to demonstrate its superiority. The code is available at https://***/AizhEngHN/RMIA.

关键词： Artificial immune system Rank-based multimodal immune algorithm many-objective optimization problems

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An improved competitive particle swarm optimization for many-objective optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期189卷 116118-116118页

作者： Gu, Qinghua Liu, Yingyin Chen, Lu Xiong, Naixue Xian Univ Architecture & Technol Sch Management 13 Yanta Rd Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Resources Engn 13 Yanta Rd Xian 710055 Shaanxi Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

Multi-objective particle swarm optimization (MOPSO) has been widely applied to solve multi-objective optimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ineffective in achieving the balance between convergence and diversity in the high-dimensional objective space. In this paper, an improved competitive particle swarm optimization is proposed for solving manyobjective optimization problems. To improve the quality of the first generation population, a decision variable dividing-based multi-step initialization mechanism is presented, decision variables are divided into two groups and optimized individually. Moreover, an improved competitive learning strategy is suggested as the main part to further optimization, where particles are updated via leader information from winner particles with well convergence and diversity. The performance of the proposed algorithm is verified by benchmark comparisons with several state-of-the-art evolutionary algorithms. Experimental results demonstrate the promising performance of the algorithm in terms of balance convergence and diversity.

关键词： many-objective optimization problems many-objective particle swarm optimization Initialization mechanism Competitive strategy

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