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dynamic multi-objective service composition based on improved social learning optimization algorithm

APPLIED SOFT COMPUTING 2024年 167卷

作者： Hai, Yan Xu, Xin Liu, Zhizhong North China Univ Water Resources & Elect Power Sch Informat Engn Zhengzhou 450046 Henan Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

The technique of multi-objective service composition (MOSC) aims to create powerful and large grained services through aggregating some simple services while optimizing some conflicting objectives, which has become a hot topic in fields of service computing, cloud computing, cloud manufacturing, and so on. Due to the dynamic nature of the services and users' objectives, the problem of MOSC is actually an dynamic multi-objective service composition problem (DMOSC). Recently, although some wonderful research has been conducted on multi-objective service composition, few studies have paid attention to the issue of DMOSC. To fill this gap, we propose the problem of DMOSC for the first time and then develop a method for DMOSC based on the improved social learning optimization algorithm (named DMOSCO). First, we construct the mathematical model for the problem of DMOSC considering the dynamics of services and users' objectives. Then, we devise the changes perception strategy and changes response strategy to deal with the changes of candidate services and users' objectives during the service composition process, which is designed to strike a balance between convergence and diversity within improved SLO. Finally, to solve the problem of DMOSC, we improve the SLO algorithm by designing a new observation learning operator and incorporating the changes detection strategy and response strategy. Extensive experimental results have proved the performance superiority of our proposed method.

关键词： Service composition dynamic multi-objective optimization Quality of service Social learning optimization algorithm

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dynamic multi objective Particle Swarm optimization with Cooperative Agents

Dynamic Multi Objective Particle Swarm Optimization with Coo...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kouka, Najwa Fdhila, Raja Hussain, Amir Alimi, Adel M. Univ Sfax Natl Engn Sch Sfax ENIS REGIM Lab REs Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Edinburgh Napier Univ Data Sci & Cyber Analyt DSCA Res Grp Edinburgh Midlothian Scotland

ISBN: (纸本)9781728169293

dynamic multi-objective optimization problems (DMOPs) involve multiple objectives, constraints, and parameters that may change over time. For solving such types of problems, the conventional particle swarm optimization algorithm should not only be able to evolve near-optimal and diverse optimal solutions but also continually track the time-changing environment. To address these challenges, we propose a novel dynamic multi-objective particle swarm optimization approach with cooperative agents. In this strategy, the ability of tracking based on particle memory and multiple populations with sharing knowledge are combined to deal with environmental changing. If a change is detected, solutions with no improvement are re-evaluated and the worst solutions are replaced with the newly generated one. In addition, a movement strategy based on the sharing of the best knowledge is introduced to promote the population diversity. Experiments on several optimization problems are carried out to prove the performance of the proposed algorithm and the statistical results show that the proposed algorithm performs well with DMOPs.

关键词： optimization particle swarm optimization dynamic multi-objective optimization multi-agent system

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dynamic Constrained multi-objective Model for Solving Constrained optimization Problem

Dynamic Constrained Multi-objective Model for Solving Constr...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Zeng, Sanyou Chen, Shizhong Zhao, Jiang Zhou, Aimin Li, Zhengjun Jing, Hongyong China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China China Normal Univ Shanghai Peoples R China China Acad Space Technol Xian Xian 710000 Shanxi Peoples R China

ISBN: (纸本)9781424478354

Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly, a complex DCMOEA algorithm is used to solve a relatively simple COP problem. However, the DCMOEA algorithm can adopt Pareto domination to achieve a good tradeoff between fast converging and global searching, and therefore a DCMOEA algorithm can effectively solve a COP problem by solving the DCMOP problem. An instance of DCMOEA was used to to solve 13 widely used constraint benchmark problems, The experimental results suggest it outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper. The efficient performance of the DCMOEA algorithm shows, to some extend, the DCMOP model works well.

关键词： Evolutionary algorithm Constrained optimization dynamic multi-objective optimization multi-objective optimization dynamic optimization

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dynamic multi-modal multi-objective optimization: A Preliminary Study 17th

Dynamic Multi-modal Multi-objective Optimization: A Prelimin...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Peng, Yiming Ishibuchi, Hisao Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031147210;9783031147203

Many real-world multi-modal multi-objective optimization problems are subject to continuously changing environments, which requires the optimizer to track multiple equivalent Pareto sets in the decision space. To the best of our knowledge, this type of optimization problems has not been studied in the literature. To fill the research gap in this area, we provide a preliminary study on dynamic multi-modal multi-objective optimization. We give a formal definition of dynamic multi-modal multi-objective optimization problems and point out some key challenges in solving them. To facilitate algorithm development, we suggest a systematic approach to construct benchmark problems. Furthermore, we provide a feature-rich test suite containing 10 novel dynamic multi-modal multi-objective test problems.

关键词： Evolutionary multi-objective optimization multi-modal multi-objective optimization dynamic multi-objective optimization Benchmark problems

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Parking Vehicle-Assisted Task Offloading in Edge Computing: A dynamic multi-objective evolutionary algorithm with multi-strategy fusion response☆

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Zhou, Yingbo Chai, Zheng-Yi Li, Ya-Lun Li, Jun-Jie Tiangong Univ Sch Comp Sci & Technol Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Quanzhou Vocat & Tech Univ Quanzhou 362268 Fujian Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

Vehicle-edge computing, as a promising paradigm, is employed to support applications that require low latency and high computational capability. In this study, we consider the idle resources of the surrounding parked vehicles (PVs) and roadside units (RSUs) as service providers to enhance the performance of User Equipment (UE). We propose a joint offloading architecture that uses parked vehicles. Additionally, owing to the dynamic and uncertain nature of the environment, we model computation offloading as a dynamic multi-objective optimization problem to simultaneously optimize the latency and energy consumption of UE applications. In this study, we propose a dynamic multi-objective evolutionary algorithm with a multi-strategy fusion response (DMOEA/D-MSFR). Specifically, we introduce a population center positioning strategy and a learnable prediction mechanism using Long Short-Term Memory (LSTM) in DMOEA-MSFR, which divides the prediction optimization process into two stages and exhibits a rapid response to environmental changes. In the static optimization phase, an adaptive weight vector adjustment strategy is employed, which significantly aids in the distribution and diversity of the solutions. Comprehensive experiments demonstrate that our proposed framework balances the trade-off between latency and energy consumption, and the convergence, feasibility, and diversity of the non-dominated solutions obtained.

关键词： Vehicle edge computing dynamic multi-objective optimization Evolutionary algorithms Computational offloading Vehicle collaboration

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dynamic multi-objective time-temperature management for climacteric fruit cold storage considering ripeness windows and energy consumption

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JOURNAL OF FOOD ENGINEERING 2025年 387卷

作者： Chen, Qian Qian, Jianping Li, Huibin Lin, Xintao Li, Jiali Liu, Zihan Zhao, Zhiyao Chinese Acad Agr Sci Key Lab Agr Remote Sensing AGRIRS Minist Agr & Rural Affairs Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China State Key Lab Efficient Utilizat Arid & Semiarid A Beijing 100081 Peoples R China Beijing Technol & Business Univ Sch Comp & Artificial Intelligence Beijing 100048 Peoples R China

Cold chain logistics (CCL) can effectively maintain the quality and safety of perishable products through lowtemperature circulation, but extra energy is densely used at the cost of economy and emissions. The multiobjective trade-off is critical for promoting sustainable time-temperature management (TTM) in CCL. For climacteric fruit in cold storage, the post-harvest ripening process should be controlled by TTM to meet various ripeness requirements of retailers with different distribution distances, market conditions. Meanwhile, randomly appeared order demands will dynamically influence complex sustainable decision-making of temperaturecontrolled path. Therefore, this paper aims to solve the dynamic multi-objective TTM optimization problem considering ripeness windows and energy consumption, for achieving online sustainable temperature control of climacteric fruit cold storage. Constructing 3D service window involving ripeness, outbound time, and cargo volume, then the multi-retailer satisfaction is designed as a significant optimization objective. Additionally, to improve operational efficiency and reduce environmental impact, energy consumption needs to be minimized under reasonable constraints. Following above objective strategy, a dynamic multi-objective TTM model is proposed with two-stage: 1) improved NSGA-II-based global optimization to find optimal temperature-controlled path for fixed retailers in real-time;2) new stochastic retailer comes up after demand matching, if accepting order, an update optimization will be triggered. A numerical study was implemented to verify proposed method, with robustness value of 0.82-1. Compared with constant low-temperature storage, the optimized comprehensive loss is the smallest at 3.374. The results indicate that the hybrid-retailer demand-driven multi-criteria decision support is effective and robust for sustainable TTM of climacteric fruit cold chain.

关键词： Climacteric fruit Cold storage dynamic multi-objective optimization Time-temperature management Sustainability

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A dynamic spectrum and power allocation method for co-located pulse radar and communication system coexistence

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CHINESE JOURNAL OF AERONAUTICS 2025年第4期38卷

作者： Meng, Youwei Li, Yaoyao Cai, Shaoxiong Su, Donglin Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Airborne pulse radar and communication systems are essential for precise detection and collision avoidance, ensuring that aircraft operate safely and efficiently. A major challenge in spectrum sharing is the allocation of resources in both the time and frequency domains, aiming to minimize inter-system interference as the available spectrum fluctuates over time. In this paper, regarding maximization of detection probability and spectrum utilization efficiency as two fundamental objectives, a novel dynamic Spectrum and Power Allocation based on Genetic Algorithm (GA-DSPA) model is proposed, which dynamically allocates communication channel frequency and power under the constraints of pulse radar detection probability and signal-to-interference- plus-noise ratio of communication. To solve this bi-objective model, a non-dominated sorting- based multi-objective genetic algorithm is developed. A novel environment perception strategy and offspring sorting technique based on radar echoes are integrated into the optimization framework. Simulation results indicate that by integrating environmental monitoring mechanisms and dynamic adaptation strategies, the proposed method effectively tracks the evolving Paretooptimal Fronts (PoFs), thereby ensuring optimal performance for both co-located pulse radar and communication systems. Hardware test results confirm that within the GA-DSPA framework, the pulse radar achieves higher detection probabilities under identical conditions, while the communication system realizes increased average throughput. (c) 2025 The Authors. Published by Elsevier Ltd on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Communication systems dynamic multi-objective optimization Electromagnetic compatibility Radar-communication coexistence Spectrum and power allocation

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DPb-MOPSO: A dynamic Pareto bi-level multi-objective Particle Swarm optimization Algorithm

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Aboud, Ahlem Rokbani, Nizar Fdhila, Raja Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Hussain, Amir Alimi, Adel M. Univ Sousse ISITCom Sousse 4011 Tunisia Univ Sfax Natl Engn Sch Sfax ENIS REGIM Lab Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Univ Sousse High Inst Appl Sci & Technol Sousse Sousse Tunisia Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 Taif 21944 Saudi Arabia King Saud Univ Coll Appl Comp Sci Riyadh Saudi Arabia Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Scotland Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

Particle Swarm optimization (PSO) system based on the distributed architecture over multiple sub -swarms is very efficient for static multi-objective optimization but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a dynamic Pareto bi-level multi-objective Particle Swarm optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable multiple sub-swarm' subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes in the objective function due to time-varying parameters. A response strategy consisting in reevaluating all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), hypervolume difference (HVD), Robust IGD (RIGD), and Robust General Distance (RGD) metrics are used to assess the DPb-MOPSO performance. Quantitative results are analyzed using Friedman's analysis of variance, and the Wilcoxon sum ranks test, while the stability is analyzed using Lyapunov's theorem. The DPb-MOPSO is more robust than several dynamic multi -objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 8/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO can resolve 7/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 6/8 with m

关键词： dynamic multi-objective optimization problem dynamic particle swarm optimization Detectors dynamic response Friedman analysis of variance Wilcoxon test Lyapunov theorem

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Solving hybrid charging strategy electric vehicle based dynamic routing problem via evolutionary multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期68卷 100975-100975页

作者： Wang, Zhenzhong Ye, Kai Jiang, Min Yao, Junfeng Xiong, Neal N. Yen, Gary G. Xiamen Univ Sch Informat Xiamen Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Minist Culture & Tourism Key Lab Digital Protect & Intelligent Proc Intang Beijing Peoples R China

With the development of Electric Vehicle (EV) technology, the new generation of EVs combines the advantages of both Wireless Charging Technology (WCT) and Plug-in Charging Technology (PCT) to extend their transport dis-tance. However, some difficulties emerge in this hybrid charging strategy based EVs for Vehicle Routing Problem (VRP). First, the availability of devices for PCT and WCT varies with time. EVs not only need to find the optimal routes but also to determine charging strategies according to the environment. Second, in such a dynamic envi-ronment, the Pareto-optimal Solutions (POS) should be rapidly tracked and Decision Makers (DMs) should pick desired solutions quickly for implementation. To address these issues, in this work, we propose a framework to reuse knee points into the newly generated environment. Reusing knee points can provide high-quality knowl-edge to generate a better initial population and the knee points also bring convenience to DMs. In this work, we also introduce a benchmark of Hybrid Charging Strategy based dynamic Vehicle Routing Problem (HCS-DVRP). The obtained experimental results on this benchmark validate that our proposed design can achieve a promising optimization performance via reusing knee points.

关键词： dynamic multi-objective optimization Electric vehicle Knee points Vehicle routing applications

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A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 100987-100987页

作者： Zheng, Jinhua Zhang, Zeyu Zou, Juan Yang, Shengxiang Ou, Junwei Hu, Yaru Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm's convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (POF) shape, the proposed algorithm utilizes the complementary characteristics in the search area of the adversarial vector, and the two populations are alternately updated and co-evolved by adversarial search directions. Additionally, a novel particle update strategy is proposed to select promising neighborhood information to guide evolution and enhance diversity. To improve the ability to cope with environmental changes, an effective dynamic response mechanism is proposed, including three parts: archive set prediction, exploration of global optimal information, and retention of excellent particles to accelerate convergence to the Pareto optimal set (POS) in the new environment. The proposed algorithm is tested on a series of benchmark problems and compared to several state-of-the-art algorithms. The results show that ADNEPSO performed excellently in both convergence and diversity, and is highly competitive in dealing with dynamic problems.

关键词： dynamic multi-objective optimization Particle swarm optimization Adversarial decomposition

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