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2010 IEEE World Congress on Computational Intelligence

作者： Singh, Hemant Kumar Ray, Tapabrata Smith, Warren Univ New S Wales Australian Def Force Acad Sch Informat Technol & Engn Canberra ACT Australia

ISBN: (纸本)9781424481262

Real life optimization problems often involve a tradeoff between multiple objectives. They also comprise constraints, which may be due to various factors such as the strength of materials, stability and safety of design, limits on operational time, financial viability and many others. In recent years, Evolutionary Algorithms (EAs) have been a popular choice for solving such multi-objective, constrained problems for various reasons. Point to point search methods, such as Simulated Annealing (SA) have been largely unexplored for such problems. However, some recent studies have suggested that with certain enhancements, SA can also be an effective tool to deal with such problems. The ability of SA to accept uphill moves (unlike EAs) during the search makes it a less prone to getting trapped in a local minimum, and is therefore attractive for solving optimization problems. For many engineering design problems, evaluating a candidate solution can be computationally intensive. This can often put a restriction on the number of function evaluations to find a near optimum solution. To mitigate this problem, often suitable approximations (surrogates) are used in optimization instead of real function evaluations. Surrogate assisted approaches have so far been used in the paradigm of Evolutionary Algorithms (EA). In this paper, we extend the use of surrogates to another well known heuristic, Simulated Annealing (SA), for constrained, multi-objective problems. A comparison with currently prevalent EAs on a difficult set of constrained problems has been included in order to highlight the efficacy of the proposed approach.

关键词： constrained multiobjective optimization engineering design problem evolutionary algorithm evolutionary computation heuristic point to point search method simulated annealing surrogate assisted simulated annealing

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A multiobjective Evolutionary Algorithm for Distribution System Reconfiguration Problems 12

A Multiobjective Evolutionary Algorithm for Distribution Sys...

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Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Sekizaki, Shinya Nishizaki, Ichiro Hayasida, Tomohiro Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Japan

ISBN: (纸本)9781665499248

Real-world multiobjective optimization problems (MOPs) are generally constrained and multiobjective evolutionary algorithms (MOEAs) have been proposed for solving constrained MOPs (CMOPs). When solving CMOPs observed in the real world, which are computationally demanding and have severe constraints, finding a well-converged population to constrained Pareto front is not easy. In such cases, infeasible nondominated solutions with slight constraint violations can provide useful information for decision makers. In this paper, we propose a MOEA that can provide decision makers with trade-off information between constraint violations and objective values of those untractable CMOPs. The proposed MOEA partitions the objective space of a CMOP, with objectives to minimize constraint violations, into multiple subspaces and maintains the diversity of the population by preserving feasible and infeasible solutions in the subspaces. Solutions with slight constraint violations and small objective values are preserved in the population using different selection pressures for the feasible and infeasible populations. Computational experiments for solving a distribution system reconfiguration problem, which is a computationally demanding and severely constrained CMOP in the real world, are performed to demonstrate the effectiveness of the proposed MOEA.

关键词： multiobjective evolutionary algorithm constrained multiobjective optimization Distribution system reconfiguration

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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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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 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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constrained multiobjective differential evolution algorithm with infeasible-proportion control mechanism

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KNOWLEDGE-BASED SYSTEMS 2022年 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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Pareto-optimal synthesis of microwave amplifier to design the noise-constrained gain value

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2012年第4期54卷 1079-1084页

作者： Cengiz, Yavuz Konar, Eray Suleyman Demirel Univ Dept Elect & Commun Engn TR-32200 Isparta Turkey

In this work, synthesis of microwave amplifier is put forward as a constrained vector optimization problem whose components are to meet the specific gain value constrained by the minimum noise over the available bandwidth. Nondominated solutions are acquired using the nondominated Sorting Genetic Algorithm-II as a fast nondominated genetic sorting algorithm wherefore these synthesis objectives generally conflict with each other. Then, the Pareto frontiers are obtained using these trade-off solution sets between the performance characterization of the device results in the triplet of gain GT, noise F, and input VSWR Vi and its source (ZS) and load (ZL). It can be concluded that the Pareto-optimal synthesis procedure provides microwave amplifier with optimum performance. (c) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:10791084, 2012;View this article online at ***. DOI 10.1002/mop.26724

关键词： microwave amplifier constrained multiobjective optimization Pareto-optimal solutions nondominated Sorting Genetic Algorithm

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Application of tropical optimization for solving multicriteria problems of pairwise comparisons using log-Chebyshev approximation

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2024年 169卷

作者： Krivulin, Nikolai St Petersburg State Univ Univ Skaya Emb 7-9 St Petersburg 199034 Russia

We consider a decision -making problem to find absolute ratings of alternatives that are compared in pairs under multiple criteria, subject to constraints in the form of two-sided bounds on ratios between the ratings. Given matrices of pairwise comparisons made according to the criteria, the problem is formulated as the log-Chebyshev approximation of these matrices by a common consistent matrix (a symmetrically reciprocal matrix of unit rank) to minimize the approximation errors for all matrices simultaneously. We rearrange the approximation problem as a constrained multiobjective optimization problem of finding a vector that determines the approximating consistent matrix. The problem is then represented in the framework of tropical algebra, which deals with the theory and applications of idempotent semirings and provides a formal basis for fuzzy and interval arithmetic. We apply methods and results of tropical optimization to develop a new approach for handling the multiobjective optimization problem according to various principles of optimality. New complete solutions in the sense of the maxordering, lexicographic ordering and lexicographic max -ordering optimality are obtained, which are given in a compact vector form ready for formal analysis and efficient computation. We present numerical examples of solving multicriteria problems of rating four alternatives from pairwise comparisons to illustrate the technique and compare it with others.

关键词： Idempotent semifield Tropical optimization Log-Chebyshev approximation constrained multiobjective optimization

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Dual-stage and dual-population cooperative evolutionary algorithm for solving constrained multiobjective problems

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APPLIED SOFT COMPUTING 2024年 160卷

作者： Luo, Wenguan Yu, Xiaobing Yen, Gary G. Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

During the search process, the characteristics of the feasible regions encountered by the population continually change in constrained multiobjective optimization Problems (CMOPs). This variability poses a challenge for traditional evolutionary algorithms, which often struggle to adapt to the diverse problem characteristics of the encountered feasible regions. To overcome this limitation, we propose a Dual -Stage and Dual -Population Cooperative Evolutionary Algorithm (DDCEA) to address CMOPs characterized by diverse feasible regions. DDCEA employs a dual -stage mechanism to adapt the offspring generation strategy and establishes two distinct populations to evaluate offspring using constraint -sensitive and constraint -free strategies. Comparative analyses reveal that DDCEA surpasses chosen state-of-the-art CMOEAs in adapting to the changing feasible regions and then approximating the constrained Pareto fronts.

关键词： constrained multiobjective optimization problems (CMOPs) Cooperation Evolutionary algorithm Reproduction and evaluation strategies

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