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Game Model-based Co-evolutionary Algorithm with Non-dominated Memory and Euclidean Distance Selection Mechanisms for multi-objective optimization

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2011年第5期9卷 924-932页

作者： Park, Seung-Min Ko, Kwang-Eun Park, Junheong Sim, Kwee-Bo Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

Many real-world problems involve simultaneous optimization of several incommensurable and often competing objectives. In the search for solutions to multi-objective optimization problems (MOPs), we find that there is no single optimum but rather a set of optimums known as the "Pareto optimal set". Co-evolutionary algorithms are well suited to optimization problems which involve several often competing objectives. Co-evolutionary algorithms are aimed at evolving individuals through individuals competing in an objective space. In order to approximate the ideal Pareto optimal set, the search capability of diverse individuals in an objective space can be used to determine the performance of evolutionary algorithms. Non-dominated memory and Euclidean distance selection mechanisms for co-evolutionary algorithms have the goal of overcoming the limited search capability of diverse individuals in the population space. In this paper, we propose a method for maintaining population diversity in game model-based co-evolutionary algorithms, and we evaluate the effectiveness of our approach by comparing it with other methods through rigorous experiments on several MOPs.

关键词： Co-evolutionary evolutionary algorithm multi-objective optimization problem pareto optimal set

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Hybrid heuristic search method for design of digital IIR filter with conflicting objectives

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SOFT COMPUTING 2017年第12期21卷 3461-3476页

作者： Sidhu, D. S. Dhillon, J. S. Kaur, Dalveer Dept Elect & Commun Engn Giani Zail Singh Punjab Tech Univ Campus Bathinda 151001 India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 India Punjab Inst Technol Dept Elect & Commun Engn Kapurthala 144601 India

The design of digital IIR filter design by using evolutionary algorithms has gained much attention in the previous years. Most of the researchers treated the design problem as a single objective optimization problem and applied the techniques for minimizing the magnitude response error. In this paper the design of filter is treated as a multi-objective problem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order along with meeting the stability criterion. A hybrid heuristic search technique having differential evolution (DE) method as a global search technique and binary successive approximation based evolutionary search method as a local search technique has been proposed. Based on mean value of population, new mutation strategies have been proposed. The above proposed hybrid heuristic search technique has been applied effectively to solve the multi-parameter and multi-objective optimization problem of low-pass, high-pass, band-pass and band-stop digital IIR filter design. The obtained results reveal that the proposed technique with new proposed mutation strategies performs better than the already existing mutation strategies of DE and other algorithms applied by other researchers for the design of digital IIR filter.

关键词： Hybrid heuristic search technique Mutation strategies of DE Binary successive approximation (BSA) Digital IIR filter multi-objective optimization problem

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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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Hierarchical Resource Allocation in multi-Service Wireless Networks With Wireless Network Virtualization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第10期69卷 11811-11827页

作者： Han, Yan Tao, Xiaofeng Zhang, Xuefei Jia, Sijia Beijing Univ Posts & Telecommun Natl Engn Lab Mobile Network Technol Beijing 100876 Peoples R China Dept Beijing Sci & Technol Co Fast Online 3 Beijing 100102 Peoples R China

To balance the contradiction between the rapid growth of data service demands and the limited spectrum resources, wireless network virtualization (WNV) has been proposed as a promising technology by isolating and sharing wireless resources among different virtual networks in the future wireless networks. In this paper, a two-dimension-time-scale hierarchical resource allocation scheme is proposed in the multi-service wireless virtualized network, which consists of three 5G generic scenarios. The resource slicing problem is decomposed into two time scales including large time period for inter-slice resource pre-allocation and small time slot for intra-slice resource scheduling. In large time period, the inter-slice resource pre-allocation problem is formulated as a multi-objective optimization problem (MOOP) by modeling the packets arriving and serving process of each slice as a queueing system. While in small time slot, the resource block (RB) and power scheduling in each slice is formulated as a stochastic optimization problem considering dynamic traffic arrivals and time-varying channel conditions, which is aimed at optimizing the overall performance subject to various quality of service (QoS) requirements such as network stability, delay, reliability, transmission rate and power constraints. The stochastic optimization problem can be transformed into a delay-aware optimization problem by applying Lyapunov optimization technique, and be solved by the proposed algorithm consisting of a heuristic algorithm and a concave optimization algorithm. The simulation results show that the proposed schemes are close to the optimal solution with a lower complexity, which can also achieve a performance-delay tradeoff related to the control factor.

关键词： Lyapunov optimization multi-objective optimization problem resource allocation wireless network virtualization

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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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Interactive Reference Region Based multi-objective Evolutionary Algorithm Through Decomposition

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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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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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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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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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