Reservoir operation is an important and effective measure for realizing optimal allocation of water resources. It can effectively alleviate regional scarcity of water resources, flood disasters and other social proble...
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Reservoir operation is an important and effective measure for realizing optimal allocation of water resources. It can effectively alleviate regional scarcity of water resources, flood disasters and other social problems, and plays an important role in supporting sustainable strategic development of water resources. Coordinating the stakeholders is key to the smooth operation of a multifunctional reservoir. This research examines the competition among stakeholders of a multi-objective ecological reservoir operation aiming to provide for economic, social and ecological demands. A multi-objective game theory model (MOGM) specified 10-day water discharge to meet the triple water demands (power generation, socio-economic consumption and environment) for multi-purpose reservoir operation. The optimal operation of the Three Gorges Reservoir (TGR), with the ecological objective of providing comprehensive ecological flow demanded for some key ecological problems that may occur in the middle and lower reaches of the Yangtze River, was chosen as a case study. Discharged water calculated by the MOGM and a conventional multi-objective evolutionary algorithm/decomposition with a differential evolution operator was then allocated to different demands. The results illustrate the applicability and efficiency of the MOGM in balancing transboundary water conflicts in multi-objective reservoir operation that can provide guidance for the operation of the TGR.
In multi-objective evolutionary algorithms (MOEAs), non-dominated sorting is one of the critical steps to locate efficient solutions. A large percentage of computational cost of MOEAs is on non-dominated sorting for i...
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In multi-objective evolutionary algorithms (MOEAs), non-dominated sorting is one of the critical steps to locate efficient solutions. A large percentage of computational cost of MOEAs is on non-dominated sorting for it involves numerous comparisons. By now, there are more than ten different non-dominated sorting algorithms, but their numerical performance comparing with each other is not clear yet. It is necessary to investigate the advantage and disadvantage of these algorithms and consequently give suggestions to specific users and algorithm designers. Therefore, a comprehensively numerical study of non-dominated sorting algorithms is presented in this paper. Firstly, we design a population generator. This generator can generate populations with specific features, such as population size, number of Pareto fronts and number of points in each Pareto front. Then non-dominated sorting algorithms were tested using populations generated in certain structures, and results were compared with respect to number of comparisons and time consumption. Furthermore, In order to compare the performance of sorting algorithms in MOEAs, we embed them into a specific MOEA, dynamic sorting genetic algorithm (DSGA), and use these variations of DSGA to solve some multi-objective benchmarks. Results show that dominance degree sorting outperforms the other methods, fast non-dominance sorting performs the worst and the other sorting algorithms performs equally.
Dynamic and overlapping are two common features of community structures for many real world complex networks. Although there are few studies on detecting dynamic overlapping communities, all those studies only conside...
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Dynamic and overlapping are two common features of community structures for many real world complex networks. Although there are few studies on detecting dynamic overlapping communities, all those studies only consider a single optimization objective. In practice, it is necessary to evaluate the community detection by multiple metrics to reflect different aspects of a community structure and those metrics may conflict with each other. In this paper, we propose a multi-objective approach based on decomposition for the problem of dynamic overlapping community detection, with consideration of three optimization objectives: partition density (D), extended modularity (EQ), and improved mutual information (NMILFK). The dynamic overlapping network can be regarded as a set of network snapshots. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to detect the communities for each snapshot. To improve the search efficiency, the dynamic optimization technique and a dynamic resource allocation strategy are introduced into the approach. Experiments show that our approach can find uniformly distributed Pareto solutions for the problem and outperforms those comparative approaches.
Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused ...
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Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.
Cyber security has received increasing attention, as people use more Internet applications in their lives and worry about the security of their personal data on the Internet. Intrusion Detection Systems (IDSs) are cri...
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Cyber security has received increasing attention, as people use more Internet applications in their lives and worry about the security of their personal data on the Internet. Intrusion Detection Systems (IDSs) are critical security tools that can detect and respond to intrusions. In recent years, Deep Learning (DL) techniques have gained popularity in IDS design due to their promising performance in terms of detection accuracy. However, the design of DL architectures usually requires professional knowledge and significantly impacts the performance of the DL model. Furthermore, the existence of a small ratio of abnormal traffic in vast network traffic leads to a serious imbalanced data problem, which negatively affects the performance of the DL model in detecting minority attack classes. To alleviate these problems, this paper proposes a multi-objectiveevolutionary DL model (called EvoBMF) to detect network intrusion behaviors. The model incorporates bidirectional Long-short Term Memory (BiLSTM) for preliminary feature extraction, multi-Head Attention (MHA) for further capturing features and global information of the network traffic, and Full-Connected Layer (FCL) module to perform final classification. To deal with the challenge of manually tuning the parameters of the DL model when tackling different tasks, the parameters of the EvoBMF model are first encoded as the chromosome of the multi-objective evolutionary algorithm (MOEA), which aims to optimize the two conflicting objectives (complexity and classification ability) of the model. A state-of-the-art MOEA (MOEA/D-DRA) is then used to optimize the above two objectives, aiming to obtain the optimal architecture for EvoBMF, which can be easily deployed in cloud computing scenarios to detect and respond to network intrusions. Additionally, to alleviate the severe imbalance in routine network traffic, the synthetic minority over-sampling technique is introduced to generate representative samples of minority classes
To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becomin...
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To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.
Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies...
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Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision-making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision-making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline.
Satellite range scheduling always plays a crucial role in tracking, telemetry, and control of the spacecraft. With the significant increase in the number and type of satellites in orbit, the demands of users for satel...
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Satellite range scheduling always plays a crucial role in tracking, telemetry, and control of the spacecraft. With the significant increase in the number and type of satellites in orbit, the demands of users for satellite range scheduling are becoming more and more diverse. However, existing studies for the satellite range scheduling problem (SRSP) rarely consider multiple optimization objectives, which is quite significant in practice. Hence, this paper investigates a multi-objective satellite range scheduling problem (MO-SRSP) that optimizes three objectives simultaneously: the overall profits of tasks, load-balance of antennas, and completion timeliness of tasks. To address MO-SRSP, this paper establishes a mathematical model on the basis of analysis of MO-SRSP and proposes a multi-objective evolutionary algorithm, which is called multi-objective differential evolution algorithm based on space division and adaptive selection strategy (MODE-SDAS). The space division strategy will uniformly divide the objective space into a set of subspaces and preserve a set of non-dominated solutions in each subspace during the environmental selection even if some of these solutions are dominated by other solutions in other subspaces, so as to maintain the diversity of the algorithm. The adaptive selection strategy will adaptively allocate computational resources to different subspaces to improve the convergence of the population. Besides, problem-specific designs such as coding and encoding methods, the discrete differential evolution operator, as well as objective-specific individual variation operators are incorporated into the algorithm to enhance the search capability. Finally, extensive experiments based on simulation test cases are carried out to verify the effectiveness and efficiency of MODE-SDAS. The comparison results show that MODE-SDAS significantly outperforms its competitors in terms of three metrics. Meanwhile, knee point analysis, sensitivity analysis, and applic
Assembly processes for complex products primarily involve manual assembly and often encounter various disruptive events, such as the insertion of new orders, order cancellations, task adjustments, workers absences, an...
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Assembly processes for complex products primarily involve manual assembly and often encounter various disruptive events, such as the insertion of new orders, order cancellations, task adjustments, workers absences, and job rotations. The dynamic scheduling problem for complex product assembly workshops requires consideration of trigger events and time nodes for rescheduling, as well as the allocations of multi-skilled and multilevel workers. The application of digital twin technology in smart manufacturing enables managers to more effectively monitor and control disruptive events and production factors on the production site. Therefore, a dynamic scheduling strategy based on digital twin technology is proposed to enable real-time monitoring of dynamic events in the assembly workshop, triggering rescheduling when necessary, adjusting task processing sequences and team composition accordingly, and establishing a corresponding dynamic scheduling integer programming model. Additionally, based on NSGA-II, an improved multi-objective evolutionary algorithm (IMOEA) is proposed, which utilizes the maximum completion time as the production efficiency indicator and the time deviation before and after rescheduling as the production stability indicator. Three new population initialization rules are designed, and the optimal parameter combination for these rules is determined. Finally, the effectiveness of the scheduling strategy is verified through the construction of a workshop digital twin system.
This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms (MOEA/Ds) to reduce the unnecessary usage of computational resource. In MOEA/D, a multi-objective problem is...
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This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms (MOEA/Ds) to reduce the unnecessary usage of computational resource. In MOEA/D, a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach. Then, optimal Pareto front (PF) is obtained by optimizing the Tchebycheff objective of all the subproblems. The proposed stopping criterion monitors the variations of Tchebycheff objective at every generation using maximum Tchebycheff objective error (MTOE) of all the subproblems and stops the algorithm, when there is no significant improvement in MTOE. test is used for statistically verifying the significant changes of MTOE for every generations. The proposed stopping criterion is implemented in a recently constrained MOEA/D variant, namely CMOEA/D-CDP, and a simulation study is conducted with the constrained test instances for choosing a suitable tolerance value for the MTOE stopping criterion. A comparison with the recent stopping methods demonstrates that the proposed MTOE stopping criterion is simple and has minimum computational complexity. Moreover, the MTOE stopping criterion is tested on real-world application, namely multi-objective loop shaping PID controller design. Simulation results revealed that the MTOE stopping criterion reduces the unnecessary usage of computational resource significantly when solving the constrained test instances and multi-objective loop shaping PID controller design problems.
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