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Interactive Reference Region Based multi-objective Evolutionary Algorithm Through Decomposition

IEEE ACCESS 2016年第99期4卷 7331页

作者： Liu, Ruochen Wang, Ruinan Feng, Wen Huang, Junjun Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

Many evolutionary multi-objective optimization (EMOs) methodologies have been proposed and shown a great potential in approximating the entire Pareto front. While in real world, what decision makers (DMs) want is one or several solutions to satisfy their requirements. It has become a hot problem that dynamically using preference information provided by DMs during the optimization process guides the search of EMO algorithms. An interactive reference region-based evolutionary algorithm through decomposition is proposed, denoted as RR-MOEA/D in this paper, which focuses the search on the desire of DMs to save computational resources. MOEA/D, as a well-known multi-objective optimization algorithm, is used as a basic framework here. In MOEA/D, by dealing with the sub-problems in the preference region and ignoring uninterested ones, the solutions obtained can converge to the regions which the DM prefers on the Pareto front and the computational complexity can be saved to a great extent. At each interaction, a humanized and simple interactive condition is adopted so that the reference region can be changed in a very intuitive way if the DM is unsatisfied the results in the interactive process. A rapid interaction is designed and a set of rough solutions can be obtained quickly whenever the preference information is changed. The proposed algorithm is tested on several benchmark problems and the experimental results show that the proposed algorithm can take full use of preference information and successfully converge to the reference region due to its reasonable and simple interaction mechanism.

关键词： Interactive preference multi-objective optimization problem reference region selection mechanism

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A comparison between different robust optimum design approaches: Application to tuned mass dampers

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PROBABILISTIC ENGINEERING MECHANICS 2010年第1期25卷 108-118页

作者： Marano, Giuseppe Carlo Greco, Rita Sgobba, Sara Tech Univ Bari Dept Environm Engn & Sustainable Dev I-74100 Taranto Italy Tech Univ Bari Dept Civil Engn & Architecture I-70125 Bari Italy

This paper is focused on the comparison between different approaches in structural optimization. More precisely, the conventional deterministic optimum design, based on the assumption that the only source of uncertainty concerns the forcing input, is compared to robust single-objective and multi-objective optimum design methods. The analysis is developed by considering as case of study a single-degree-of-freedom system with uncertain parameters, subject to random vibrations and equipped with a tuned mass damper device (TMD). The optimization problem concerns the selection of TMD mechanical characteristics able to enlarge the efficiency of the strategy of vibration reduction. Results demonstrate the importance of performing a robust optimum design and show that the multi-objective robust design methodology provides a significant improvement in performance stability, giving a better control of the design solution choice. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Tuned mass dampers Stochastic mechanics Lyapunov equation Robust design multi-objective optimization problem Genetic algorithm

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A knowledge-guided bi-population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Yu, Fei Lu, Chao Zhou, Jiajun Yin, Lvjiang Wang, Kaipu China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Automot Technol Sch Econ & Management Shiyan 442002 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

With the guidance of the advanced manufacturing philosophy, green scheduling and energy efficiency have received considerable attention from enterprises and countries. Meanwhile, distributed manufacturing is becoming widespread due to the exploration of the business. Thus, this paper investigates the energy-efficient scheduling of the distributed flexible job shop problem (EEDFJSP) with the goal of minimizing the makespan and total energy consumption (TEC). Considering the difficulty of simultaneously optimizing both objectives, a knowledge-guided bi-population evolutionary algorithm (KBEA) is proposed to address this issue. Firstly, a problem-specific initialization strategy based on a four-vector representation is presented, which corresponds to four sub-problems including factory assignment, operation sequence, machine assignment, and speed assignment. Secondly, five different types of evolutionary operators with adaption strategy is designed to guide the bi-population to complete efficient evolution. Thirdly, a knowledge-guided local search strategy is used to enhance the exploitation capability of the algorithm. Furthermore, an elaborately-designed energy-saving strategy based on knowledge is developed to further reduce energy consumption. Additionally, to verify the effectiveness of the proposed KBEA, extensive experiments are conducted to compare with other 7 comparison algorithms on 39 instances. Experimental results manifest that KBEA is superior to its competitors.

关键词： Distributed flexible job shop scheduling multi-objective optimization problem Energy-efficient Bi-population evolutionary algorithm

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Energy-Delay Tradeoff for Dynamic Trajectory Planning in Priority-Oriented UAV-Aided IoT Networks

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2023年第1期7卷 158-170页

作者： Cao, Hailin Zhu, Wang Chen, Zhengchuan Sun, Zhiwei Wu, Dapeng Oliver Chongqing Univ Chongqing Key Lab Space Informat Network & Intelli Chongqing 400044 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Unmanned aerial vehicles (UAVs) play a crucial role in emergency-oriented applications. However, in UAV-aided Internet of Things (IoT) networks, the sensor nodes (SNs) would be mobile which poses a big challenge for trajectory planning of the UAV. In this paper, we investigate priority-oriented UAV-aided time-sensitive data collection problems in an IoT network with movable SNs. By defining different levels of delay sensitivities for each SN, we jointly minimize the energy consumed by a UAV and the average delay of different SNs through optimizing the trajectory of the UAV. The problem is formulated as a multi-objective optimization problem (MOP). To solve the formulated problem, we first transform the MOP into a single-objective optimization problem based on the weighted sum method. Then, we propose a novel autofocusing heuristic trajectory planning algorithm based on reinforcement learning (AHTP-RL) which can be operated in an online manner. The proposed algorithm can well extract the network dynamic topology and the delay-priority of SN through an attention mechanism, hence can structure the UAV's trajectory efficiently. Extensive simulations results demonstrate that the proposed online AHTP-RL algorithm can achieve a superior balance between the communication delay and energy consumption for both low and high SN mobilities.

关键词： Unmanned aerial vehicles delay sensitivity dynamic network attention mechanism multi-objective optimization problem

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Strongly geodesic preinvexity and strongly invariant η-monotonicity on Riemannian manifolds and its application

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RAIRO-OPERATIONS RESEARCH 2023年第5期57卷 2687-2702页

作者： Iqbal, Aklad Hussain, Askar Bhat, Hilal Ahmad Aligarh Muslim Univ Dept Math Aligarh 202002 India

This paper introduces the concepts of strongly geodesic preinvexity, strongly eta-invexity of order m, and strongly invariant eta-monotonicity of order m on Riemannian manifolds. Additionally, it discusses an important characterization of these functions under a condition, known as Condition C (The Condition C is defined in Remark 1 of this article), defined by Barani and Pouryayevali [J. Math. Anal. Appl. 328 (2007) 767-779]. The paper provides various non-trivial examples to support these definitions. Furthermore, it presents a significant characterization of strict eta-minimizers (or eta-minimizers) of order m for multi-objective optimization problems and a solution to the vector variational-like inequality problem.

关键词： Strongly geodesic preinvexity strongly eta-invex functions invariant eta-monotone vector fields multi-objective optimization problem vector variational like-inequality problem Riemannian manifolds

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A novel epsilon-dominance Harris Hawks optimizer for multi-objective optimization in engineering design problems

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NEURAL COMPUTING & APPLICATIONS 2022年第19期34卷 17007-17036页

作者： Allou, Lotfi Zouache, Djaafar Amroun, Kamal Got, Adel Univ Bejaia Fac Exact Sci Comp Sci Dept Bejaia Algeria Univ Mohamed El Bachir El Ibrahimi Comp Sci Dept Bordj Bou Arreridj Algeria Univ Sci & Technol Houari Boumediene LRIA Lab Algiers Algeria

In this article, A multi-Leaders Guided Harris Hawks optimizer using Epsilon-Dominance relation is developed for solving multi-objective optimization problems. For this reason, the standard HHO algorithm is equipped with a fixed-size external archive to ensure the elitism concept. On the other hand, both crowding distance computation and epsilon dominance relation are adopted when updating the archive in the hope of improving the diversity of solutions. Moreover, an efficient leader selection procedure is proposed to guarantee convergence towards less-crowded Pareto regions. Our algorithm's performance is validated on 18 test functions in all, 5 with two objectives and 13 with three objectives, and it is compared with four well-regarded algorithms, namely: multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), multi-objective Grey Wolf Optimizer (MOGWO), multi-objective Particle Swarm optimization (MOPSO), and multi-objective Salp Swarm Algorithm (MSSA). Also, it is applied to solve four engineering real-world problems, namely: Four bar truss, Speed reducer, Disk brake design, and Welded beam design problems. Inverted Generational Distance (IGD) metric and Hypervolume (HV) metric were used to quantify the behaviors of multi-objective algorithms. The obtained results show the performance of the proposed algorithm in terms of convergence and diversity for the benchmark functions and the engineering real-world problems.

关键词： multi-objective optimization problem Harris hawks optimization Epsilon dominance Crowding distance computation External archive Engineering design problems

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SLA-WS: SLA-based workload scheduling technique in multi-cloud platform

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第8期14卷 10001-10012页

作者： Nelli, Arundhati Jogdand, Rashmi KLS Gogte Inst Technol Dept Comp Sci & Engn Belagavi Karnataka India

Real-time workload execution resource provisioning with SLA prerequisite in multi-cloud platform is considered to a difficult job. Data intensive workload is composed direct acyclic graph (DAG);thus, there exist high dependency among different subtask with varying quality of service (QoS) prerequisite. The existing workload scheduling is designed using multi-objective parameter such as minimizing time and cost;however, reducing delay and energy overhead is not considered. This paper presents Service level agreement-based workload scheduling (SLA-WS) technique for execution of real-time workload on multi-cloud platform. The SLA-WS emphasizes multi-objective parameter such as processing efficiency with energy optimization and task offloading benefits using soft-computing based dragonfly algorithm (DA). The SLA-WS model reduces processing time and energy consumption for execution of different workload in comparison with existing WS-framework leveraging multi-cloud platform.

关键词： Cloud computing Heterogeneous computing environment multi-objective optimization problem Resource provisioning Work scheduling

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Utilizing a projection neural network to convex quadratic multi-objective programming problems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2023年第7期37卷 1847-1865页

作者： Jahangiri, Mohammadreza Nazemi, Alireza Shahrood Univ Technol Fac Math Sci POB ***-316 Shahrood Iran

In this paper, we propose a projection neural network model for solving convex quadratic multi-objective optimization problem (CQMOP). The CQMOP is first converted into an equivalent convex nonlinear programming problem by the means of the weighted sum method, where the Pareto optimal solutions are calculated via different values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of the suggested model. Both theoretical and numerical approaches are studied. Numerical results are in good agreement with the proved theoretical concepts.

关键词： convergence convex non-linear programming problem multi-objective optimization problem neural networks Pareto optimal solution stability

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multi-objective optimization ON SUPERCRITICAL CO₂ RECOMPRESSION BRAYTON CYCLE USING KRIGING SURROGATE MODEL

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THERMAL SCIENCE 2017年第Sup1期21卷 S309-S316页

作者： Sun, Lei Wang, Chongyu Zhang, Di Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermofluid Sci & Engn Xian Shaanxi Peoples R China

Supercritical CO2 cycle has become one of the most popular research fields of thermal science. The selection of operation parameters on thermodynamic cycle process is an important task. The computational model of supercritical CO2 recompression cycle is built to solve the multi-objective problem in this paper. Then, the optimization of parameters is performed based on genetic algorithm. Several Kriging models are also used to reduce the quantity of samples. According to the calculation, the influence of sample quantity on the result and the time cost is obtained The results show that it is required to improve the heat transfer when improvement of the cycle efficiency is desired.

关键词： supercritical CO2 Brayton cycle recompression multi-objective optimization problem Kriging surrogate model

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multi-objective attribute reduction in three-way decision-theoretic rough set model

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2019年 105卷 327-341页

作者： Li, Weiwei Jia, Xiuyi Wang, Lu Zhou, Bing Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Sam Houston State Univ Dept Comp Sci Huntsville TX 77341 USA

Attribute reduction plays an important role in rough set theory. Many attribute reduction methods have been proposed based on different definitions of attribute reduct, while an attribute reduct can be regarded as a minimal attribute subset that satisfies specific criteria. Most reducts are defined on the basis of a single criterion, which can only affect one specific characteristic of the data or one preference of users. However, the single criterion-based attribute reduct may not meet the requirement of complex problems. To address this problem, based on three-way decision-theoretic rough set model, this paper defines a multi-objective attribute reduct. Three types of criteria for defining attribute reduct including the positive region, decision cost and mutual information are considered and combined to a multi-objective optimization problem. Based on the proposed multi-objective attribute reduct, we also introduce a multi-objective optimization-based attribute reduction method and an ensemble learning-based attribute reduction method. Experimental results on several datasets show that the proposed attribute reduction methods can obtain a robust and better classification performance. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Attribute reduct Three-way decisions Decision-theoretic rough set model multi-objective optimization problem Ensemble learning

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