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A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D

APPLIED SOFT COMPUTING 2013年第1期13卷 128-148页

作者： Jan, Muhammad Asif Khanum, Rashida Adeeb KUST Dept Math Kohat 26000 Khyber Pakhtunk Pakistan Univ Essex Dept Math Sci Colchester CO4 3SQ Essex England

Penalty functions are frequently employed for handling constraints in constrained optimization problems (COPs). In penalty function methods, penalty coefficients balance objective and penalty functions. However, finding appropriate penalty coefficients to strike the right balance is often very hard. They are problems dependent. Stochastic ranking (SR) and constraint-domination principle (CDP) are two promising penalty functions based constraint handling techniques that avoid penalty coefficients. In this paper, the extended/modified versions of SR and CDP are implemented for the first time in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework. This led to two new algorithms, CMOEA/D-DE-SR and CMOEA/D-DE-CDP. The performance of these new algorithms is tested on CTP-series and CF-series test instances in terms of the HV-metric, IGD-metric, and SC-metric. The experimental results are compared with NSGA-II, IDEA, and the three best performers of CEC 2009 MOEA competition, which showed better and competitive performance of the proposed algorithms on most test instances of the two test suits. The sensitivity of the performance of proposed algorithms to parameters is also investigated. The experimental results reveal that CDP works better than SR in the MOEA/D framework. (C) 2012 Elsevier B. V. All rights reserved.

关键词： constrained multiobjective optimization Constraint-domination principle Decomposition MOEA/D Stochastic ranking

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Joint optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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Terminal Trajectory Planning for Synthetic Aperture Radar Imaging Guidance Based on Chronological Iterative Search Framework

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第5期54卷 3065-3078页

作者： Sun, Zhichao Ren, Hang Sun, Huarui Yen, Gary G. Wu, Junjie Yang, Jianyu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater MN 74078 USA

Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

关键词： Trajectory Radar polarimetry Trajectory planning Synthetic aperture radar Search problems optimization Radar imaging constrained multiobjective optimization evolutionary algorithm subproblem division synthetic aperture radar (SAR) trajectory planning

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A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving multiobjective Problems With Multiconstraint

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第1期28卷 267-280页

作者： Zou, Juan Sun, Ruiqing Liu, Yuan Hu, Yaru Yang, Shengxiang Zheng, Jinhua Li, Ke Xiangtan Univ Hunan Engn Res Ctr Intelligent Syst Optimizat & Se Key Lab Intelligent Comp & Informat Proc Minist Educ China Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Secu Xiangtan 411105 Hunan Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England Univ Exeter Dept Comp Sci Exeter EX4 4QF England

In science and engineering, multiobjective optimization problems (MOPs) usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This article aims to solve the challenges brought by multiple complex constraints. First, this article analyzes the relationship between single-constrained Pareto front (SCPF) and their common Pareto front (PF) subconstrained PF (SubCPF). Next, we discussed the SCPF, SubCPF, and unconstraint PF (UPF)'s help to solve constraining PF (CPF). Then, further discusses what kind of cooperation should be used between multiple populations constrained multiobjective optimization algorithm (CMOEA) to better deal with multiconstrained MOPs (mCMOPs). At the same time, based on the discussion in this article, we propose a new multipopulation CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed activation dormancy detection (ADD) to accelerate the optimization process and uses the proposed combine occasion detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.

关键词： Statistics Sociology optimization Convergence Sun Evolutionary computation Computer science Coevolutionary algorithm constrained multiobjective optimization constraint handling

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A Multistage Algorithm for Solving multiobjective optimization Problems With Multiconstraints

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1207-1219页

作者： Sun, Ruiqing Zou, Juan Liu, Yuan Yang, Shengxiang Zheng, Jinhua Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ China Hunan Engn Res Ctr Intelligent Syst Optimizat & S Xiangtan 411105 Hunan Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

There are usually multiple constraints in constrained multiobjective optimization. Those constraints reduce the feasible area of the constrained multiobjective optimization problems (CMOPs) and make it difficult for current multiobjective optimization algorithms (CMOEAs) to obtain satisfactory feasible solutions. In order to solve this problem, this article studies the relationship between constraints, then obtains the priority between constraints according to the relationship between the pareto front (PF) of the single constraint and their common PF. Meanwhile, this article proposes a multistage CMOEA and applies this priority, which can save computing resources while helping the algorithm converge. The proposed algorithm completely abandons the feasibility in the early stage to better explore the objective space, and obtains the priority of constraints according to the relationship. Then, the algorithm evaluates a single constraint in the medium stage to further explore the objective space according to this priority, and abandons the evaluation of some less important constraints according to the relationship to save the evaluation times. At the end stage of the algorithm, the feasibility will be fully considered to improve the quality of the solutions obtained in the first two stages, and finally get the solutions with good convergence, feasibility, and diversity. The results on five CMOP suites and three real-world CMOPs show that the algorithm proposed in this article can have strong competitiveness in existing constrained multiobjective optimization.

关键词： constrained multiobjective optimization constraint-handling priority evolutionary algorithm

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On Formulating and Designing Antenna Arrays by Evolutionary Algorithms

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2021年第2期69卷 1118-1129页

作者： Xu, Qinghui Zeng, Sanyou Zhao, Fei Jiao, Ruwang Li, Changhe China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Southwest Elect & Telecommun Technol Res Inst Sci & Technol Blind Signal Proc Lab Chengdu 610041 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China

The design of antenna arrays is usually imposed with lots of requirements. The design can be modeled as different kinds of optimization problems, such as single-objective optimization problems (SOPs) with or without constraints or multiobjective optimization problems with or without constraints. Then, different optimizers need to be applied to deal with the corresponding optimization problems. There are two issues to be addressed for antenna designers: one is which kind of optimization problems is the fittest to model antenna arrays;the other is a general and efficient algorithm that can solve different kinds of problems. To this end, for the first issue, constrained multiobjective optimization problems (CMOPs) are recommended as the most suitable kind in this article because CMOPs not only fully reflect the requirements of array design but also cover SOPs, constrained optimization problems (COPs), and multiobjective optimization problems (MOPs) as degeneration cases. For the second issue, a dynamic constrained multiobjective evolutionary algorithm (DCMOEA) is provided to solve CMOPs. Notably, DCMOEA is a general framework that can also work well on SOPs, COPs, and MOPs. Finally, two examples of antenna arrays are designed to verify the proposed conclusion.

关键词： Antenna arrays optimization Genetic algorithms Evolutionary computation Heuristic algorithms Task analysis Antenna arrays constrained multiobjective optimization evolutionary algorithm

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Adaptive Constraint Penalty-Based multiobjective Operation optimization of an Industrial Dynamic System With Complex Multiconstraint

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第8期54卷 4724-4737页

作者： Zhou, Ping Zhang, Shuai Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Aiming at the operation optimization of the wastewater treatment process (WWTP) with nonstationary time-varying dynamics and complex multiconstraint, this article proposes a novel adaptive constraint penalty decomposed multiobjective evolutionary algorithm with synthetical distance (SD)-based cross-generation crossover. First, the concept of spatial SD is presented to comprehensively evaluate the similarity of individual solutions from two aspects of distance and angle, and the individual information between two adjacent generations is used to enhance the diversity of individuals and accelerate the convergence of the algorithm. Second, aiming at the complex multiconstraint during the operation optimization of WWTP, an adaptive penalty algorithm is further adopted to punish the individual solutions that violate the constraints, so as to improve the handling efficiency and success rate of constraints. Furthermore, in view of the time-varying dynamics of actual WWTP, a recursive bilinear subspace identification method based on sliding window is adopted to establish the optimization models as well as the constraint models with self-learning parameter, which provides accurate model guarantee for high-performance multiobjective operation optimization. Finally, the effectiveness, superiority, and practicability of the proposed method are verified through test function experiments as well as operation optimization control experiments of WWTP.

关键词： Adaptive penalty of complex constraints constrained multiobjective optimization intergenerational information operation optimization synthetical distance (SD) wastewater treatment process (WWTP)

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Enhancing Decomposition-Based Algorithms by Estimation of Distribution for constrained Optimal Software Product Selection

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第2期24卷 245-259页

作者： Xiang, Yi Yang, Xiaowei Zhou, Yuren Huang, Han South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510006 Peoples R China

This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator is a probability vector, which is adaptively learned from historical information of each subproblem. We show that this update operator can significantly enhance decomposition-based algorithms on a number of benchmark problems. Moreover, we apply the enhanced algorithms to the constrained optimal software product selection (OSPS) problem in the field of search-based software engineering. For this real-world problem, we give its formal definition and then develop a new repair operator based on satisfiability solvers. It is demonstrated by the experimental results that the algorithms equipped with the EoD operator are effective in dealing with this practical problem, particularly for large-scale instances. The interdisciplinary studies in this paper provide a new real-world application scenario for constrained multiobjective binary optimizers and also offer valuable techniques for software engineers in handling the OSPS problem.

关键词： optimization Software Software algorithms Probabilistic logic Estimation Software engineering Evolutionary computation constrained multiobjective optimization decomposition-based multiobjective algorithm estimation of distribution (EoD) optimal software product selection (OSPS) search-based software engineering (SBSE)

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Clustering-Based Dual-Population Co-Evolutionary Algorithm for 2D Segmentation Coding in NAND Flash Memory

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3603-3607页

作者： Luo, Jianjun Chen, Menghao Fan, Lingyan Liu, Hailuan Gao, Lijuan Feng, Guorui Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China Hangzhou Hualan Microelect Co Ltd Hangzhou 310018 Zhejiang Peoples R China China Elect Technol Grp Corp Res Inst 52 Hangzhou 311121 Zhejiang Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

In NOT-AND (NAND) flash memory-based solid-state drive (SSD), the error checking and correction (ECC) module is configured to ensure the stability of stored data. With the increase of raw bit error rate (RBER), the traditional error correction coding mode is not only difficult to meet the requirements, but also the efficiency of data repair is extremely low because the error bits cannot be located, when the error correction cannot be completed. Therefore, an innovative error correction coding mode, called two-dimensional (2D) segmentation coding mode, is proposed. To our knowledge, this is the first attempt to apply this coding mode to NAND flash memory. It improves error correction capability and data recovery efficiency by coding information bits in rows and columns separately. We propose a dual-population co-evolution algorithm based on clustering algorithm to optimize the segmentation method for better efficiency. Under the condition of satisfying all the constraints, the solutions obtained by the proposed algorithm achieve the balance of rewrite efficiency, coding efficiency and coding time. Experimental results show that the proposed algorithm performs well compared with six state-of-the-art evolutionary algorithms (EAs) in solving the proposed constrained multi-objective optimization problem.

关键词： Encoding Statistics Sociology Error correction codes Flash memories Clustering algorithms Linear programming NAND flash memory 2D block error correction code Bose-Chaudhuri-Hocquenghem code constrained multiobjective optimization evolutionary algorithm

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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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