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A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems

COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 655-675页

作者： Shi, Chenli Wang, Ziqi Jin, Xiaohang Xu, Zhengguo Wang, Zhangsheng Shen, Peng Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Zhejiang Peoples R China Zhejiang Univ Technol Key Lab Special Purpose Equipment & Adv Proc Techn Minist Educ & Zhejiang Prov Hangzhou 310023 Zhejiang Peoples R China Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Huadian Jiangsu Energy Co Ltd Jurong Power Generat Branch Zhenjiang 212000 Jiangsu Peoples R China

Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained multi-objective optimization evolutionary algorithm based on three-stage multi-population coevolution (CMOEA-TMC) for complex CMOPs is proposed. CMOEA-TMC contains two populations, called mainPop and helpPop, which evolve with and without consideration of constraints, respectively. The proposed algorithm divides the search process into three stages. In the first stage, fast convergence is achieved by transforming the original multi-objective problems into multiple single-objective problems. Coarse-grained parallel evolution of subpopulations in mainPop and guidance information provided by helpPop can facilitate mainPop to quickly approach the Pareto front. In the second stage, the objective function of mainPop changes to the original problem. Coevolution of mainPop and helpPop by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding helpPop to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for mainPop to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs.

关键词： Constrained multi-objective optimization problems (CMOPs) evolutionary algorithms Coevolution Parallel algorithm Staging strategy

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evolutionary Tracking Algorithm Based on Combined Received Signal Strength and Angle of Arrival Measurements in Wireless Sensor Networks

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IEEE SENSORS JOURNAL 2023年第19期23卷 23734-23743页

作者： Najarro, Lismer Andres Caceres Song, Iickho Tomic, Slavisa Salman, Muhammad Noh, Youngtae Kim, Kiseon Korea Inst Energy & Technol Sch Energy AI Naju 58330 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Anhui Peoples R China Univ Lusofona Humanidades & Tecnol Cognit & People Ctr Comp Lab P-1749024 Lisbon Portugal Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

This article addresses the target tracking problem based on the received signal strength (RSS) and angle of arrival (AOA) in wireless sensor networks (WSNs). The tracking problem is formulated in the framework of the maximum a posteriori (MAP), in which the prior knowledge of moving target nodes (TNs) is exploited. Due to the fact that the cost function of the tracking problem is highly nonlinear and nonconvex, most of the existing algorithms tend to approximate and relax the cost function. As a result, the tracking accuracy is usually compromised. In this article, we propose a tracking algorithm based on evolutionary techniques that do not require an approximation of the cost function, resulting in a considerable improvement in tracking accuracy. The proposed tracking algorithm is compared with state-of-the-art algorithms such as the MAP, particle filter (PF), and Kalman filter (KF). Simulation and real experiment results demonstrate that the proposed tracking algorithm provides an improvement roughly by 16%, 11%, and 18% over the MAP, PF, and KF, respectively, in the tracking accuracy.

关键词： Angle of arrival (AOA) evolutionary algorithms received signal strength (RSS) tracking wireless sensor networks (WSNs)

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Approximate Solution to the Problem of Optimal Scalar Control with Terminal-Phase Constraints Based on evolutionary Computations

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JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL 2023年第6期62卷 968-976页

作者： Antipin, A. F. Antipina, E. V. Mustafina, S. A. Ufa Univ Sci & Technol Sterlitamak Branch Sterlitamak Russia Ufa Univ Sci & Technol Ufa Russia

A numerical algorithm is developed for searching for an approximate solution to the optimal control problem in the presence of terminal-phase constraints. In general, the formulation of the optimal control problem with terminal-phase constraints is presented, in which the control is a limited piecewise constant function. To solve the problem, a step-by-step algorithm is formulated, which is based on the methods of penalties and differential evolution. Based on this algorithm, a program is created with the help of which a computational experiment is carried out for the catalytic reaction of the synthesis of benzylidenebenzylamine. The temperature profile of the process, which provides the highest concentration of the target substance with restrictions on the conversion of the starting substances, is determined.

关键词： evolutionary algorithms

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Improvement of the performance of the Quantum-inspired evolutionary algorithms: structures, population, operators

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evolutionary INTELLIGENCE 2014年第4期7卷 219-239页

作者： Tayarani-N, Mohammad H. Akbarzadeh-T, M. R. Univ Birmingham Sch Comp Sci Room 244Edgbaston Birmingham B15 2TT W Midlands England Ferdowsi Univ Mashhad Mashhad Iran

Population diversity is very important in giving the algorithm the power to explore the search space and not get trapped in local optima. In this respect, using a probabilistic representation for the quantum individuals, the Quantum-inspired evolutionary algorithms (QiEA) claim higher diversity in the population. Here, considering this important feature of QiEA, we propose different structures to offer better interaction between the q- individuals and propose new operators to preserve the diversity in the population and thus improve the performance of the QiEA. The effect of the structured population is investigated on the performance of the algorithm. Additionally, two operators are proposed in this paper. Being called the Diversity Preserving QiEA the first operator finds the converged similar q- individuals around a local optimum and while keeping the best q- individuals, by reinitializing the inferior ones pushes them out of the basin of attraction of the local optimum, so helping the algorithm to search other regions in the search space. The other operator is a reinitialization operator which by reinitializing the whole population helps it escape from the local optima it is trapped in. By studying the effect of the parameters of the proposed operators on their performance we show how the proposed operators improve the performance of QiEA. Experiments are performed on Knapsack, Trap and fourteen numerical objective functions and the results show better performance for the proposed algorithm than the original version of QiEA.

关键词： Optimization methods Quantum-inspired evolutionary algorithms Diversity preservation Reinitialization

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Regression-based multiobjective optimization approaches for the autoclave composite parts placement problem: a mixed integer nonlinear programming model and an evolutionary algorithm

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SOFT COMPUTING 2023年第19期27卷 14045-14062页

作者： Kirdar, Gozdenur Ozturk, Diclehan Tezcaner Testik, Murat Caner Hacettepe Univ Dept Ind Engn TR-06800 Ankara Turkiye

The autoclave curing process is an important step in composite parts production, in which a batch of parts is cured in an autoclave simultaneously by systematically changing the inner temperature and pressure. Typically, curing cycles have three main phases: heating, dwell, and cooling. During the heating phase, parts are heated until they reach a curing temperature. Due to factors such as positions of parts inside the autoclave and batch composition, it is often not possible for the parts to reach the curing temperature simultaneously. Parts that reach the curing temperature earlier than the others are overcured, which negatively affects their quality. Moreover, shorter curing cycles are preferred due to the savings in cost, energy, and time. This study addresses these two considerations with a unified approach that integrates two decision support methods: regression and optimization. In the first stage, we determine the factors affecting the time to reach the curing temperature and relate them using multiple linear regression models. In the second stage, we utilize the regression models of the first stage and determine efficient placements of the parts in the autoclave considering two objectives: minimization of the duration of the heating phase and the maximum time delay between parts in reaching the curing temperature. The former corresponds to increasing productivity and the latter corresponds to minimizing quality losses. We propose a biobjective mixed integer nonlinear programming model together with its equivalent linear model to generate the efficient frontier. Additionally, to obtain solutions faster, a multiobjective evolutionary algorithm and its mechanisms that address the problem are proposed. The approach is applied on real cases in a composite factory. The estimates of the regression models are significantly close to the realizations, and considerable gains in both objectives are observed with the optimization tools.

关键词： Composite parts production Parts placement in autoclave Multiple linear regression Multiobjective optimization evolutionary algorithms

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Minehunting Mission Planning for Autonomous Underwater Systems Using evolutionary algorithms

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UNMANNED SYSTEMS 2014年第4期2卷 323-349页

作者： Abreu, Nuno Matos, Anibal INESC TEC Campus FEUPRua Dr Roberto Frias 378 P-4200465 Oporto Portugal FEUP DEEC P-4200465 Oporto Portugal

Autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations but its capabilities are limited by the efficiency of the planning process. Here we study the problem of multiobjective MCM mission planning with AUVs. The vehicle should cover the operating area while maximizing the probability of detecting the targets and minimizing the required energy and time to complete the mission. A multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure, aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The combination of different techniques creates another problem, related to the high amount of parameters that needs to be tuned. Thus, the effect of these parameters on the quality of the obtained Pareto Front was assessed. This allowed us to define an adaptive tuning procedure to control the parameters while the algorithm is executed. Our algorithm is compared against an implementation of a known EA as well as another mission planner and the results from the experiments show that the proposed strategy can efficiently identify a higher quality solution set.

关键词： evolutionary algorithms neural networks mission planning AUV 3D coverage minehunting

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New Blind Image Moments Watermarking Method Based on evolutionary Optimization of Embedding Parameters

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IEEE ACCESS 2023年 11卷 48517-48547页

作者： Chekira, Chaimae Marzouq, Manal Batioua, Imad Fadili, Hakim El Lakhliai, Zakia Zenkouar, Khalid Sidi Mohamed Ben Abdellah Univ Comp Sci & Interdisciplinary Phys Lab LIPI Fes 30003 Morocco Sidi Mohamed Ben Abdellah Univ Lab Intelligent Syst & Applicat LSIA Fes 30003 Morocco

In image watermarking, the locations where the watermark is embedded in the frequency domain and the embedding strength influence the overall performance of the blind watermarking procedure. The present paper aims to propose a new blind watermarking method based on the dither modulation by developing an automatic selection of the optimum embedding parameters that guarantee high-quality watermarked images and low bit error rates during the extraction process. The proposed automatic search method for the best discrete moments subsets is based on an evolutionary algorithm and adopts a specific coding strategy with a group of genes representing the embedding positions. The second part of the chromosome is reserved for the embedding strength coding, followed by the application of different evolutionary operators on the evolution pool. Our study explores the impact of maximum generation, population size, and cutting-point positions with different crossover and mutation rates. The performances under different attack conditions are evaluated, and a comparative study is established with other conventional selection methods and other discrete transforms. Results show that our proposed optimization algorithm baptized EWIMps achieves the best trade-off between robustness and imperceptibility with a peak signal-to-noise ratio varying from 28.33 dB to 59.87 dB and a normalized cross-correlation value from 0.707 to 1.

关键词： Watermarking Robustness Quaternions Harmonic analysis evolutionary algorithms Image processing Frequency-domain analysis Data analysis Data hiding blind image watermarking orthogonal discrete moments discrete transforms dither modulation evolutionary algorithm

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Neural Network Guided evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 1860-1875页

作者： Zhong, Ziyuan Kaiser, Gail Ray, Baishakhi Columbia Univ Dept Comp Sci New York NY 10025 USA

Self-driving cars and trucks, autonomous vehicles (avs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability - which can most practically and convincingly be achieved by testing. But existing testing methods are inadequate for checking the end-to-end behaviors of av controllers against complex, real-world corner cases involving interactions with multiple independent agents such as pedestrians and human-driven vehicles. While test-driving avs on streets and highways fails to capture many rare events, existing simulation-based testing methods mainly focus on simple scenarios and do not scale well for complex driving situations that require sophisticated awareness of the surroundings. To address these limitations, we propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used av simulators' API grammars to generate semantically and temporally valid complex driving scenarios (sequences of scenes). To efficiently search for traffic violations-inducing scenarios in a large search space, we propose a constrained neural network (NN) evolutionary search method to optimize AutoFuzz. Evaluation of our prototype on one state-of-the-art learning-based controller, two rule-based controllers, and one industrial-grade controller in five scenarios shows that AutoFuzz efficiently finds hundreds of traffic violationsin high-fidelity simulation environments. For each scenario, AutoFuzz can find on average 10-39% more unique traffic violationsthan the best-performing baseline method. Further, fine-tuning the learning-based controller with the traffic violationsfound by AutoFuzz successfully reduced the traffic violationsfound in the new version of the av controller software.

关键词： Automobiles Testing Fuzzing Vehicle crash testing Grammar Artificial neural networks Roads Search-based software engineering evolutionary algorithms neural networks software testing test generation autonomous vehicles

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An optimized design of optical networks using evolutionary algorithms

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JOURNAL OF HIGH SPEED NETWORKS 2014年第1期20卷 11-27页

作者： Abedifar, Vahid Eshghi, Mohammad Shahid Beheshti Univ Fac Elect & Comp Engn Dept Elect Engn GC Tehran *** Iran

Optical networks are key enablers of the modern communication services to handle the increasing bandwidth requests. Virtualization is a feasible technology to response to the users' demands. On the other hand, the cloud computing, as a distinguished use case of the virtualized optical networks, has impacted the IT world. In virtualization of the resources, optimum mapping of the virtual optical networks on the physical infrastructure plays an important role. In this paper, a novel optimized scheme for mapping the virtual optical networks on the physical infrastructure is proposed. A new formulation for Routing andWavelength Assignment, RWA, problem is presented. A novel encoding method for optical networks is proposed based on categorizing the wavelengths into different groups according to data transmission rate. Then, the Genetic Algorithm, GA, and Binary Particle Swarm Optimization, BPSO, as two popular evolutionary algorithms are implemented to find the optimum map of the virtual optical networks on the physical infrastructure using the proposed encoding method. The optimization constraints and two heuristics, proposed to satisfy them, are detailed. Finally, the simulation results for a physical infrastructure and different virtual optical networks are presented. Results show that the GA outperforms the BPSO in terms of providing optimized solutions with less values of the defined cost function. But the run time required to find the optimum map of the virtual optical networks is more than the BPSO.

关键词： evolutionary algorithms optimization Routing and Wavelength Assignment RWA virtual optical network mapping

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A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization

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NATURAL COMPUTING 2023年第2期22卷 287-313页

作者： Zhu, Shuwei Xu, Lihong Goodman, Erik Deb, Kalyanmoy Lu, Zhichao Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Tongji Univ Dept Elect & Informat Engn Shanghai 201804 Peoples R China Michigan State Univ BEACON Ctr E Lansing MI 48824 USA Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

In the last decade, it is widely known that the Pareto dominance-based evolutionary algorithms (EAs) are unable to deal with many-objective optimization problems (MaOPs) well, as it is hard to maintain a good balance between convergence and diversity. Instead, most researchers in this domain tend to develop EAs that do not rely on Pareto dominance (e.g., decomposition-based and indicator-based techniques) to solve MaOPs. However, it is still hard for these non-Pareto-dominance-based methods to solve MaOPs with unknown irregular PF shapes. In this paper, we develop a general framework for enhancing relaxed Pareto dominance methods to solve MaOPs, which can promote both convergence and diversity. During the environmental selection step, we use M different cases of relaxed Pareto dominance simultaneously, where each expands the dominance area of solutions for M - 1 objectives to improve the selection pressure, while the remaining one objective keeps unchanged. We conduct the experiments on a variety of test problems, the result shows that our proposed framework can obviously improve the performance of relaxed Pareto dominance in solving MaOPs, and is very competitive against or outperform some state-of-the-art many-objective EAs.

关键词： evolutionary algorithms Many-objective optimization Pareto dominance Enhance selection pressure

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