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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Kohara, Debora Toshie Barbosa de Oliveira, Gina Maira Almeida Martins, Luiz Gustavo Univ Fed Uberlandia Fac Comp Uberlandia Brazil

ISBN: (纸本)9798350342734

multiobjective Evolutionary Algorithms (MOEA's) have been developed for optimization problems involving conflicting objectives. Real-world Batch Sequencing (BS) in pharmaceutical manufacturing presents a multiobjective optimization challenge, compounded by constraints and uncertain demands. This complexity often leads to low convergence of feasible solutions. In this study, we evaluate population initialization strategies, propose an optimized mutation operator, and explore various crossover types to enhance solution quality, measured by metrics including the number of non-dominated feasible solutions (NFS), hypervolume (HV), Inverted Generational Distance Plus (IGD+), Error Rate (E), and Coverage of Two Sets (CS).

关键词： multiobjective Evolutionary Algorithms constrained multiobjective optimization Pharma Manufacturing

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An Adaptive Dynamic Migration-based constrained multiobjective State Transition Algorithm 39

An Adaptive Dynamic Migration-based Constrained Multiobjecti...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Chen, Xiaolong Han, Jie Zhu, Liang Yang, Chunhua Wang, Xiaoli Cent South Univ Sch Automat Changsha Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Complex constrained multi-objective optimization problems are often characterized by narrow and disconnected feasible regions, making it challenging to obtain the optimal constrained Pareto front. To improve feasibility, diversity, and convergence in the optimization process, this paper introduces an adaptive dynamic migration-based constrained multiobjective state transition algorithm. The approach starts with an individual-based state transition algorithm, which searches for candidate solutions across the entire search space. Subsequently, these candidate solutions are evaluated using an adaptive dynamic migration constraint handling technique, which enhances the convergence of candidate solutions in the early stages of optimization and ensures their feasibility in the later stages. The migration process is adaptively adjusted based on the diversity and convergence of the population to maintain a balance among feasibility, diversity, and convergence. Experiments conducted on 14 benchmark problems reveal that the proposed method demonstrates superior or, at the very least, competitive performance when compared to other well-established methods.

关键词： constrained multiobjective optimization constraint handling techniques state transition algorithm adaptive dynamic migration

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A decomposition-based constraint-handling technique for constrained multi-objective optimization

A decomposition-based constraint-handling technique for cons...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Peng, Chaoda Liu, Hai-Lin Guangdong Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509047710

In recent years, many constraint-handling techniques maintaining infeasible individuals in the population have demonstrated their effectiveness for solving constrained optimization problems. Generally, the effective utilization of the potential of the infeasible individuals will improve the performance of the algorithms. This paper proposes an approach based on decomposition for constrained multi-objective optimization problems. By transforming the degree of constraint violation into one objective, a constraint-handling technique is introduced to exploit useful infeasible individuals during the evolution process. Besides, along with the evolution, it puts more emphasis on infeasible individuals with better objective values and smaller constraint violations, since they can provide more importance information for improving the convergence and diversity of the population. The performance of the proposed algorithm is evaluated by using 8 well-known test instances, i.e. CTP-series test instances. And the experimental results indicate that our algorithm is superior to the compared algorithm.

关键词： evolutionary algorithm decomposition constrained multiobjective optimization achievement scalarizing function

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constrained multiobjective Distance Minimization Problems

Constrained Multiobjective Distance Minimization Problems

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Nojima, Yusuke Fukase, Takafumi Liu, Yiping Masuyama, Naoki Ishibuchi, Hisao Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Southern Univ Sci & Technol Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Key Lab Computat Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781450361118

Various distance minimization problems (DMPs) have been proposed to visualize the search behaviors of evolutionary multiobjective optimization (EMO) algorithms in solving many-objective problems, multiobjective multimodal problems, and dynamic multiobjective problems. Among those DMPs, only the box constraints are considered. In this paper, we propose several constraint DMPs to visualize the behaviors of EMO algorithms with constraint handling techniques. In the proposed constraint DMPs, constraints are simply specified in the two-dimensional decision space. In the same manner, high-dimensional problems and multimodal problems can also be generated. Computational experiments show different behaviors by different algorithms in various constraint DMPs.

关键词： Evolutionary multiobjective optimization (EMO) constrained multiobjective optimization distance minimization problems

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Ensemble-Based Constraint Handling in multiobjective optimization 19

Ensemble-Based Constraint Handling in Multiobjective Optimiz...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vodopija, Aljosa Oyama, Akira Filipic, Bogdan Joef Stefan Inst Ljubljana Slovenia Joef Stefan Int Postgrad Sch Ljubljana Slovenia Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa Japan

ISBN: (纸本)9781450367486

Many real-world optimization problems involve both multiple objectives and constraints. Although constraint handling in multiobjective optimization has been considered in the literature, there is still a high demand for more advanced and versatile constraint handling techniques (CHTs) in real-world applications. For this reason, we propose a general approach to combine multiple CHTs into an ensemble-based method, providing a framework to easily construct new CHTs from existing ones. The approach is evaluated on nine test problems from the literature using an ensemble of four widely-used CHTs. The experimental results show that the ensemble is more robust than single CHTs and performs at least as well as the best single CHTs on all the test problems. Moreover, a positive synergistic effect of the ensemble is demonstrated on three problems.

关键词： constrained multiobjective optimization multiobjective evolutionary algorithms constraint handling techniques ensemble-based methods NSGA-II

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Knowledge-embedded constrained multiobjective evolutionary algorithm based on structural network control principles for personalized drug targets recognition in cancer

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INFORMATION SCIENCES 2024年 679卷

作者： Qiao, Kangjia Liang, Jing Guo, Wei-Feng Hu, Zhuo Yu, Kunjie Suganthan, P. N. Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou Peoples R China Henan Inst Technol Sch Elect Engn & Automat Xinxiang Peoples R China Qatar Univ Coll Engn Kindi Ctr Comp Res Doha Qatar

The structural network control principle for identifying personalized drug targets (SNCPDTs) is a kind of constrained multiobjective optimization (CMO) problem with NP -hard features, which makes traditional mathematical methods difficult to adopt. Therefore, this study designs a knowledge -embedded multitasking constrained multiobjective evolutionary algorithm (KMCEA) to solve the SNCPDTs by mining relevant knowledge. Specifically, the relationships between two optimization objectives (minimizing the number of driver nodes and maximizing prior -known drug -target information) and constraints (guaranteeing network control) are analyzed from the perspective of CMO. We find that two objectives are difficult to optimize;thus two single -objective auxiliary tasks are created to optimize two objectives respectively, so as to maintain diversity along the Pareto front. Furthermore, we find that two optimization objectives have a complex reverse relation and a simple positive relation with constraints, respectively;thus, a population initialization method and a local auxiliary task are designed for two single -objective auxiliary tasks, respectively, so as to improve the performance of the algorithm on two objective functions. Finally, KMCEA is used to solve two kinds of models with three kinds of datasets. Compared with various methods, KMCEA can not only effectively discover clinical combinatorial drugs but also better solve the SNCPDTs regarding convergence and diversity.

关键词： Structural network control principles Personalized drug targets with cancer Evolutionary algorithm constrained multiobjective optimization

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Surrogate-assisted expensive constrained Bi-objective optimization with highly heterogeneous evaluations

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Pang, Yong Lai, Xiaonan Wang, Yitang He, Xiwang Zhang, Shuai Song, Xueguan Dalian Univ Technol Sch Mech Engn Linggong Rd Dalian 116024 Peoples R China

Expensive multiobjective optimization problem has been thoroughly studied in the past few years. However, one type of problem is often overlooked. Still, it is common in practice, whose partial target functions (objectives and constraints) are expensive that require approximations by surrogate models, while others have explicit formulas that can calculate immediately. To address this problem, a general representation is first defined as a highly heterogeneous optimization problem (HHOP) in this work. In terms of bi-objective constrained HHOP, an algorithm is proposed based on the Kriging surrogate model with a new exact 2-D expected hypervolume improvement (EHVI) calculation method and a new constraint handling approach considering both the expensive and inexpensive target functions. In EHVI calculation, the integral is only performed in the expensive objective dimension due to the deterministic of the inexpensive objective, resulting in the reduction of the time complexity and increase of the accuracy. Combining a multiobjective and a dominance-based constraint handling approaches, a new constraint handling strategy is proposed to deal with the constrained HHOP specifically. The effectiveness of the proposed EHVI calculation method and constraint handling strategy is verified by the benchmark comparison problems. whose results also indicate the superiority of the proposed algorithm compared with the other state-of-art algorithms. Furthermore, the proposed algorithm is implemented for a head sheave optimization problem to demonstrate its practicability in real-world problems.

关键词： Heterogeneous evaluations Expensive optimization constrained multiobjective optimization Expected hypervolume improvement Kriging surrogate model

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Adaptive constraint handling technique selection for constrained multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Wang, Chao Liu, Zhihao Qiu, Jianfeng Zhang, Lei Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Sch Artificial Intelligence Minist Educ Hefei 230601 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Hubei Univ Automot Technol Inst Vehicle Informat Control & Network Technol Shiyan 442002 Peoples R China

constrained multi-objective optimization problems involve the optimization of multiple conflicting objectives simultaneously subject to a number of constraints, which pose a great challenge for the existing algorithms. When utilizing evolutionary algorithms to solve them, the constraint handling technique (CHT) plays a pivotal role in the environmental selection. Several CHTs, such as penalty functions, superiority of feasible solutions, and epsilon-constraint methods, have been developed. However, there are still some issues with the existing methods. On the one hand, different CHTs are typically better suited to specific problem and selecting the most appropriate CHT for a given problem is crucial. On the other hand, the suitability of CHTs may vary throughout different stages of the optimization process. Regrettably, limited attention has been given to the adaptive selection of CHTs. In order to address this research gap, we develop an adaptive CHT selection method based on deep reinforcement learning, allowing for the selection of CHTs that are tailored to different evolutionary states. In the proposed method, we adopt the deep Q-learning network to evaluate the impact of various CHTs and operators on the population state during evolution. Through a dynamic evaluation, the network adaptively outputs the most appropriate CHT and operator portfolio based on the current state of the population. Specifically, we propose novel state representation and reward calculation methods to accurately capture the performance of diverse actions across varying evolutionary states. Furthermore, to enhance network training, we introduce a two-stage training method that facilitates the collection of diverse samples. Moreover, this adaptive selection method can be easily integrated into the existing methods. The proposed algorithm is tested on 37 test problems, the optimal results can be achieved on 19 instances in terms of the inverted generational distance metric. Experimental res

关键词： constrained multiobjective optimization Evolutionary algorithm Constraint handling technique Operator Deep reinforcement learning

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constrained multiobjective differential evolution algorithm with infeasible-proportion control mechanism

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Liang, Jing Ban, Xuanxuan Yu, Kunjie Qiao, Kangjia Qu, Boyang Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China

When solving constrained multiobjective optimization problems, it is crucial to deal with several conflicting objectives and various constraints simultaneously. To address these two issues, a constrained multiobjective differential evolution algorithm with an infeasible-proportion control mechanism is presented in this paper. Specifically, two populations are cooperatively employed in the evolution process. The first population is used to solve the original problem, that is, to explore the constrained Pareto front, while the second population is created to search for high-quality objective function information. Furthermore, differential evolution with two specific mutation strategies is employed to update each population. Cooperative strategies can not only balance diversity and convergence but also realize information exchange between the two populations provide new evolutionary directions. In addition, an infeasible-proportion control mechanism is used to gradually decrease the proportion of infeasible solutions in the second population. This enables the main population to pass through the infeasible region barrier in the early evolution stage and facilitates the search for a constrained Pareto front in the later evolution stage. Systematic experiments on 65 benchmark test functions show that the proposed algorithm is superior to or at least comparable to five well-established constrained multiobjective optimization methods. (C) 2022 Elsevier B.V. All rights reserved.

关键词： constrained multiobjective optimization Coevolutionary Differential evolution Infeasible-proportion control mechanism

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A dual-population evolutionary algorithm based on dynamic constraint processing and resources allocation for constrained multi-objective

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Qiao, Kangjia Chen, Zhaolin Qu, Boyang Yu, Kunjie Yue, Caitong Chen, Ke Liang, Jing Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Zhengzhou 450007 Peoples R China Henan Inst Technol Sch Elect Engn & Automat Xinxiang 453003 Peoples R China

constrained multi-objective optimization problems (CMOPs) contain the satisfaction of various constraints and optimization of multiple objectives simultaneously, thus they are extremely challenging. Although many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed, they ignore the information of each constraint, which might help utilize more various infeasible solutions to improve the search ability of the population. Therefore, this paper proposes a new dual-population CMOEA to solve CMOPs, in which a dynamic constraint processing mechanism and a dynamic resource allocating scheme are designed. To be specific, the proposed algorithm evolves two populations, which adopt different mechanisms to handle constraints respectively. The main population directly optimizes all constraints to find the feasible Pareto optimal solutions, which can improve the feasibility. The auxiliary population adopts a dynamic constraint processing mechanism, which gradually increases the number of constraints being processed, so as to fully utilize various infeasible solutions to help find feasible regions. Moreover, a new dynamic resource allocating scheme is proposed to reasonably allocate the limited computational resources to the two populations according to their performance feedback. Experimental results on three test suites and ten practical problems show that the proposed algorithm has a better or competitive performance compared with several state-of-the-art CMOEAs.

关键词： constrained multiobjective optimization Dual-population Resources assignment scheme Dynamic constraint processing mechanism

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