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The main challenge in finding the optimal Pareto Front (PF) and Pareto Set (PS) sets for multimodal multi-objective optimization problems (MMOPs) with conflicting objective functions is to exhibit a balanced and sustainable diversity and exploitation capability in both the objective and decision spaces. This paper introduces dynamic reference spaces based clustering (DRSC) as a new archive handling method to overcome this challenge. DRSC incorporates a niche method called dynamically switched reference spaces and adapts the K-means-based method for clustering non-dominant vectors and handling the archive. The performance of the proposed DRSC is tested on twenty-four benchmark problems. According to the results of non-parametric statistical analysis using data from four different performance metrics, DRSC-MOAGDE designed using the proposed archiving mechanism managed to achieve the best Friedman rank among thirty different competitors. According to the stability analysis results, the average success rates and average computation times of the three best performing algorithms DRSC-MOAGDE, MMODE-ICD and SSMOPSO are (88.69%, 3.01 s), (66.87%, 9.47 s) and (64.29%, 1372.49 s), respectively. It is also observed that the proposed DRSC-MOAGDE outperforms the best cost optimization values in the literature with a minimum of 0.1838 $/h and a maximum of 30.9157 $/h for the multi-objective OPF real-world problem. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Random forest is a classic black-box model that is widely used in various fields. The structural characteristics of black-box models lead to weak model interpretability, which can be optimized with the help of interpretable techniques to promote the application and development of random forest in scenarios with high reliability requirements. This paper constructed a rule extraction model based on ensemble pruning and multi-objective evolutionary algorithm. Ensemble pruning is an effective method for solving the problem of extracting rules from tree models that tend to fall into local optima, and multi-objective evolutionary has several applications in balancing rule accuracy and interpretability. This paper found that it improved interpretability without sacrificing accuracy. This study integrated ensemble pruning technique with a multi-objective evolutionary algorithm, which enhances the interpretability of random forests and helps promote the decision-making application of this model in areas with high interpretability requirements. [ABSTRACT FROM AUTHOR]

Solving constrained multi-objective optimization problems is a challenging task and existing algorithms have been struggling to balance constrained convergence and population diversity. To address this problem, this paper proposes a dual-population cooperative algorithm (DPDCA), which maintains two populations and an archive, and can change the search strategy depending on the evolutionary stage. At the early stage of evolution, the two populations are responsible for different tasks, promote rapid convergence of the populations by exchanging solutions, and save promising solutions in the archive. In the later stages of evolution, different local search strategies are emphasized according to the diversity of the main populations and their convergence to obtain a feasible solution set with better convergence. In order to prevent the algorithm from falling into local optimality at a later stage, we introduce the archive to enhance the diversity of the populations, and finally the main populations are explored in depth to obtain the set of solutions located in the PF. To test the superiority of the algorithm, we tested it against five state-of-the-art algorithms on 28 benchmark test problems and 3 real-world problems, proving that the altered algorithm has good competitiveness in dealing with constrained multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold. [ABSTRACT FROM AUTHOR]

Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO2 emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO2 emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 "all sectors and EC incentive" for the year 2024, a large reduction of 55% of CO2 emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front. [ABSTRACT FROM AUTHOR]

Relay node placement with a hop count bound is a crucial problem in enhancing connectivity, lifetime, and reliability in multi-hop wireless sensor networks. However, existing approaches focus solely on minimizing the number of used relay nodes without considering the energy consumption among nodes. This work investigates a relay node placement problem in multi-hop wireless sensor networks with two objectives: minimize the number of used relay nodes, and minimize the maximum node energy consumption to prolong the network's lifetime while still ensuring the network's connectivity. In particular, we consider a hop count bound as a delay constraint to elevate the network's reliability. We propose a multi-objective evolutionary algorithm called GPrim to solve our problem. The algorithm is a combination of edge-set encoding and NSGA-II framework. Leveraging problem-specific properties, we introduce objective-oriented heuristics incorporated into initialization, crossover, and mutation operators to improve the algorithm's convergence. Simulation results on 3D datasets show that the proposed algorithm performs significantly better than existing algorithms on all measured metrics. [ABSTRACT FROM AUTHOR]

The occurrence of fire leads to unparalleled loss of resources as well as human life and hence, fire detection systems must be trustworthy and less erroneous. Real-time assessment of fire conditions through predictive learning models could lead to easier decision-making and timely rescue operations. Reported works are often restricted to the use of singular rule-based algorithms which can hardly offer a comprehensive solution by adapting to changing dynamics of fire conditions due to their static features, mostly leading to inaccurate classification. In this research, an efficient fire prediction framework has been proposed by efficiently combining the outputs of recurrent neural networks which bear the advantages of short-term predictions with long short-term memory that takes care of long-term predictions. Weights to combine multi-objective optimization functions to minimize mean absolute error and root mean square error have been designed using non-dominated sorting genetic algorithm II (NSGA-II) which offers a lower time complexity. The benchmark dataset from the NIST website has been chosen for analysis and performance validation of the proposed classifier on experimental data pertaining to different fire scenarios. A Pareto optimal front has been obtained from the proposed algorithm which represents the optimum solutions. The performance of the proposed model has been exhaustively evaluated through different factors such as accuracy, RMSE, MAE, F-Measure, binary classification rate, negative predictive value, recall and precision which justifies its contribution. The proposed model reduced RMSE by 14.95–19.88% compared to baseline machine learning models along with an enhanced accuracy of 95.05% and reduced false positive rate which is better compared to reported works along with improvement in F-Measure. Results show that the proposed NSGA-II-based RNN LSTM model accurately predicts the occurrence of fire events with reduced false alarms while maintaining a low computational overhead. [ABSTRACT FROM AUTHOR]

Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR]

The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller's preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP. [ABSTRACT FROM AUTHOR]

Background: Channel selection has become the pivotal issue a ecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside e ective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. Methods: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on di erent multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. Results: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the e ectiveness of TS-MOEA. Conclusion: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can e ectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency. [ABSTRACT FROM AUTHOR]

The performance of most existing evolutionary algorithms tends to decrease as the dimension of the decision variables increases for solving large-scale optimization problems. The Pareto solution set of a multi-objective optimization is a low dimensional manifold in the decision space, whose dimension is much smaller than that of the decision variables space. Accordingly, this paper proposed a multi-objective evolutionary algorithm based on dimensionality reduction of decision variables to solve large-scale sparse multi-objective optimization problems. It preserved local neighborhood information in the original decision variables space by using locality preserving projections, and designed an archive set to train the non-dominated solutions as much as possible to raise the accuracy of projection. The proposed algorithm was compared with four popular evolutionary multi-objective optimization algorithms on a series of test problems and practical application problems. Experimental results show that the proposed algorithm is effective in solving sparse multi-objective problems. Therefore, the reduction of independent variables can reduce the difficulty of solving the problem, improve the search efficiency of the algorithm, and have significant advantages in solving large-scale sparse multi-objective problems. [ABSTRACT FROM AUTHOR]

Aiming at the flexible Job-Shop lot-streaming scheduling problem, based on the single minute exchange of die(SMED), this paper established an intermingling scheduling optimization model with objections of minimizing the makespan and the total number of sub-lots, considering the flexibility of sublots splitting and sublots intermingling, automatic changeover and material transportation. Then it proposed an improved two-archive based multi-objective evolutionary algorithm to optimize the objective function. This algorithm adopted the framework of evolutionary algorithm. Based on the framework of evolutionary algorithm, it designed a two-archive based on hypervolume indicator and improved Pareto dominance to balance the convergence and diversity of the population. And according to the characteristics of lot-streaming intermingling problems, it proposed the forward/backward decoding and sub-lot splitting left-shift strategies in the decoding stage. In the stages of neighborhood exploration and global search, it designed adaptive evolution operators for lot splitting and sub-lot intermingling schemes respectively to improve the global search and local search capabilities of the algorithm. Based on different scale examples, it tested the performance of the proposed algorithm and the classical multi-objective algorithms. The experimental results show that the algorithm has obvious advantages in convergence and diversity. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Yellow River, a critical water resource, faces challenges stemming from increasing water demand, which has led to detrimental effects on hydropower generation and ecological balance. This paper will address the complex task of balancing the interests of hydropower generation, water supply, and ecology within the context of cascade reservoirs, specifically Longyangxia and Liujiaxia reservoirs. Employing a systemic coupling coordination approach, we constructed a multi-objective synergetic model of the upper Yellow River in order to explore synergies and competitions among multiple objectives. The results reveal that there is a weak competitive relationship between hydropower generation and water supply, a strong synergy between hydropower generation and ecology, and a strong competitive relationship between water supply and ecology. The Pareto solution set analysis indicates a considerable percentage (59%, 20%, and 8% in wet, normal, and dry years, respectively) exhibiting excellent coordination. The probability of excellent coordination decreases with diminishing inflow. The optimization scheme with the highest coupling coordination demonstrates significant improvements in power generation, water supply, and ecological benefits in the upper Yellow River without compromising other objectives, fostering the sustainable operation of hydropower generation, water supply, and ecology in the upper Yellow River. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of interest in numerical association rules mining (NARM) is controlling the length of itemset intervals. In this paper, we propose a novel evolutionary algorithm based on the multi-objective firefly algorithm for efficiently mining numerical association rules (MOFNAR). MOFNAR utilizes Balance, square of cosine (SOC) and comprehensibility as objectives of evolutionary algorithm to assess rules and achieve a rule set that is both simple and accurate. We introduce the Balance measure to effectively control the intervals of numerical itemsets and eliminate misleading rules. Furthermore, we suggest a penalty approach, and the crowding-distance method is employed to maintain high diversity. Experimental results on five well-known datasets show the effectiveness of our method in discovering a simple rule set with high confidence that covers a significant percentage of the data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the contemporary landscape, roughly one-fourth of worldwide carbon dioxide emissions stem from industrial energy usage. In the industrial sector, improving the efficient and flexible coupling among different energy demands (electricity, heating, and cooling) and exploiting the integration of Renewable Energy Sources (RESs) and waste heat can lead to a drastic reduction in CO2 emissions, which are also the goals of the EU founded Horizon Europe FLEXIndustries project. This study aims to establish a cost-optimized decarbonization strategy for an energy-intensive industry, focusing on an Italian pharmaceutical company. It delves into the exploration of potential pathways and diverse energy mix configurations. The approach undertaken involves coupling a customized energy system simulation framework, specifically designed for the industrial site, with a Multi-Objective Evolutionary Algorithm (MOEA). The study, conducted with a focus on the year 2024, involves a comparative analysis of three distinct scenarios. Within the intricate and challenging constraints of the industrial demo site, 13 technologies are investigated. The outcomes of each scenario reveal a set of Pareto optimal solutions, which are thoroughly analyzed to discern the evolution of the energy mix along the Pareto front. These results shed light on the compelling potential of hybrid solutions, showcasing the feasibility of achieving substantial decarbonization with only moderate increases in costs. The availability of land for RES technologies, along with the existence of a biomass supply chain in the region, emerge as pivotal determinants. [ABSTRACT FROM AUTHOR]

Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embedding these operators to strike a balance between exploration and exploitation. The DQN-based adaptive mutation operator is developed for predicting high-quality solutions, thereby improving the exploration process and maintaining diversity within the population. In parallel, the greedy-based crossover operator employs domain knowledge to minimize ineffective searches, focusing on exploitation and expediting convergence. Ablation studies revealed that our proposed operators significantly boost the algorithm performance. In particular, the DQN mutation operator shows its predictive effectiveness in identifying candidate solutions. The proposed NSGA-DRL outperforms state-and-art MOEAs in solving MO-WTA problems by generating high-quality solutions. [ABSTRACT FROM AUTHOR]

Condition-based maintenance is very desirable for minimizing the maintenance and failure costs of power systems without sacrificing reliability. A systematic approach including an adaptive maintenance advisor and a system maintenance optimizer is proposed here for effectively handling the operational variations and uncertainties for condition-based maintenance. First, the maintenance advisor receives and implements the maintenance plans for its key components from the system maintenance optimizer, which optimizes the maintenance schedules with multi-objective evolutionary algorithm, considering only major system variables and the overall system performance. During operation, the offshore substation will experience continuing ageing and shifts in control, weather and load factors, measurement and human judgment detected from the connected grid and all other equipments with uncertainties. Then, the advisor estimates the changes of reliability indices due to operational variations and uncertainties of its key components by hierarchical fuzzy logic and sends the changes back to the maintenance optimizer. The maintenance optimizer will upgrade the load-point reliability and report any drastic deterioration of reliability within each substation, which may lead to re-optimization of the substation's maintenance activities for meeting its desired reliability. The offshore substation connected to a medium-sized onshore grid will be studied here to demonstrate the ability of this proposed approach in dealing with uncertainties in the implementation of maintenance with significant reduction of computational complexity and rule base.

Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embedding these operators to strike a balance between exploration and exploitation. The DQN-based adaptive mutation operator is developed for predicting high-quality solutions, thereby improving the exploration process and maintaining diversity within the population. In parallel, the greedy-based crossover operator employs domain knowledge to minimize ineffective searches, focusing on exploitation and expediting convergence. Ablation studies revealed that our proposed operators significantly boost the algorithm performance. In particular, the DQN mutation operator shows its predictive effectiveness in identifying candidate solutions. The proposed NSGA-DRL outperforms state-and-art MOEAs in solving MO-WTA problems by generating high-quality solutions.

Abstract The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Δ, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms’ criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability compared to the other two. The final results demonstrated the MOAHA algorithm’s excellent performance, particularly in difficult and significant problems such as multi-reservoir systems' optimal operation under various objectives.

Feature selection (FS) is an important dimension reduction technique in practical applications, which has been widely studied during the past decades. Although a large number of FS algorithms have been proposed and shown the promising performance, most of them face with the challenge of “curse of dimensionality”. To this end, inspired by evolutionary multitasking (EMT), in this paper, a VariAble MultiTasking-based Multi-Objective Evolutionary Algorithm, named VAMT-MOEA, is proposed for high-dimensional FS. For the existing EMT-based FS algorithms, they adopt the single or fixed assisted tasks to solve the high-dimensional FS problem (namely the original task). Once they trap into the local optima, it is difficult for them to provide valuable knowledge. Different from them, the proposed algorithm employs the variable multitasking scheme to achieve the feature subsets with high quality. The assisted task is adjusted with the changes of the weights in the evolution, where the weight measures the importance of each feature. Specifically, a variable-weight adjustment strategy is proposed to adjust the assisted task, aiming to overcome the loss of diversity during the evolution. Additionally, a novel knowledge transfer strategy is suggested, where the best and the worst solutions are used to implement the positive knowledge transfer between the assisted task and the original task. Finally, an initialization strategy is designed to generate an initial assisted task with competitiveness. The experimental results on 10 high-dimensional datasets, whose dimension ranges from 2,000 to 19,993, demonstrate the superiority of the proposed VAMT-MOEA in terms of the classification error, the number of selected features and the running time. To be specific, compared with five EA-based FS algorithms, the proposed VAMT-MOEA can achieve the feature subsets with lower classification error and smaller number of features. Moreover, the running time of different algorithms also reveal that our VAMT-MOEA uses the minimum time on all 10 high-dimensional datasets.

This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted. [ABSTRACT FROM AUTHOR]

In the past few decades, to solve the multi-objective optimization problems, many multi-objective evolutionary algorithms (MOEAs) have been proposed. However, MOEAs have a common difficulty: because the diversity and convergence of solutions are often two conflicting conditions, the balance between the diversity and convergence directly determines the quality of the solutions obtained by the algorithms. Meanwhile, the nondominated sorting method is a costly operation in part Pareto-based MOEAs and needs to be optimized. In this article, we propose a multi-objective evolutionary algorithm framework with convergence and diversity adjusted adaptively. Our contribution is mainly reflected in the following aspects: firstly, we propose a nondominated sorting-based MOEA framework with convergence and diversity adjusted adaptively; secondly, we propose a novel fast nondominated sorting algorithm; thirdly, we propose a convergence improvement strategy and a diversity improvement strategy. In the experiments, we compare our method with several popular MOEAs based on two widely used performance indicators in several multi-objective problem test instances, and the empirical results manifest the proposed method performs the best on most test instances, which further demonstrates that it outperforms all the comparison algorithms. [ABSTRACT FROM AUTHOR]

The detection of complex interactions between single nucleotide polymorphisms (SNPs) plays a vital role in genome-wide association analysis (GWAS). The multi-objective evolutionary algorithm is a promising technique for SNP-SNP interaction detection. However, as the scale of SNP data further increases, the exponentially growing search space gradually becomes the dominant factor, causing evolutionary algorithm (EA)-based approaches to fall into local optima. In addition, multi-objective genetic operations consume significant amounts of time and computational resources. To this end, this study proposes a distributed multi-objective evolutionary framework (DM-EF) to identify SNP-SNP interactions on large-scale datasets. DM-EF first partitions the entire search space into several subspaces based on a space-partitioning strategy, which is nondestructive because it guarantees that each feasible solution is assigned to a specific subspace. Thereafter, each subspace is optimized using a multi-objective EA optimizer, and all subspaces are optimized in parallel. A decomposition-based multi-objective firework optimizer (DCFWA) with several problem-guided operators was designed. Finally, the final output is selected from the Pareto-optimal solutions in the historical search of each subspace. DM-EF avoids the preference for a single objective function, handles the heavy computational burden, and enhances the diversity of the population to avoid local optima. Notably, DM-EF is load-balanced and scalable because it can flexibly partition the space according to the number of available computational nodes and problem size. Experiments on both artificial and real-world datasets demonstrate that the proposed method significantly improves the search speed and accuracy. • Propose a scalable distributed multi-objective evolutionary framework for large-scale SNP-SNP interaction detection. • Design an effective space partitioning strategy to enhance the diversity and prevent local optima. • Provide a novel multi-objective fireworks optimizer based on decomposition to obtain non-dominated solutions. • Ease the heavy computational burden via distributed computing. [ABSTRACT FROM AUTHOR]

In the networking of vehicles, the information exchange between network infrastructure and vehicles is very important. However, at the current development stage of the networking of vehicles, the coverage of a single network infrastructure is very limited, and each network base station has a variety of heterogeneous wireless networks with different performances. Vehicles must constantly switch between multiple network infrastructures, so the efficiency of information transmission becomes crucial, among which the key problems are mainly service delay and service cost in the transmission of broadcast data packets. In this paper, this key problem is summarized as the multi-objective problem. The MOEA/D-DE algorithm is proposed by adding differential evolution variation to the multi-objective evolutionary algorithm, which makes the population variation more diverse. On the basis of the convergence of the original multi-objective optimization algorithm, more diverse new individuals are generated, which makes the original convergence state broken and the population continues to evolve. A large number of simulation experiments are conducted, and the experimental results show that this method can obtain smaller average service delay and average access cost, and has good scalability, that is, it is effective for a variety of data transmission scenarios, and compared with the traditional packet sending algorithm and MOEA/D algorithm, more shows the superiority of this algorithm. [ABSTRACT FROM AUTHOR]

This document is a list of academic research papers related to optimal power flow (OPF) in power systems. The papers cover various topics, including the use of different optimization algorithms, the integration of renewable energy sources, and the application of flexible AC transmission system (FACTS) devices. The papers come from a diverse range of authors and institutions, including researchers from Pakistan, China, Saudi Arabia, and other countries. [Extracted from the article]

Evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed and implemented by the scientific community, the popular Non-Dominated Sorting Genetic Algorithm (NSGA-II) remains as a widely used baseline for algorithm performance comparison purposes and applied to different engineering problems. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for obtaining an effective and efficient performance in its execution. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems show that the proposed modified NSGA-II with a self-adaptive mutator outperforms its static counterpart in more than 75% of the problems using three quality metrics (hypervolume, generalized spread, and modified inverted generational distance). [ABSTRACT FROM AUTHOR]

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front.

Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO2 emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO2 emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 “all sectors and EC incentive” for the year 2024, a large reduction of 55% of CO2 emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front.

Abstract In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted.

Abstract The construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen‐fired gas turbine. The proposed system is modelled as a bi‐objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi‐objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA‐II (a widely used multi‐objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system.

With the development of information media and the change of design concepts, the mode of cultural education has deepened from one-way linear to two-way circular. Lefebvre’s perspective is used in the study to uncover the curatorial narrative of cultural education exhibition space, and the ternary structure of cultural education spatial language is constructed from both time and space dimensions. At the same time, three multi-objective evolutionary algorithms are combined to optimize the spatial layout of cultural education, and according to the dominance of the solutions obtained by the algorithms, the optimal set of spatial layout solutions for cultural education is further sought. Finally, using the S cultural education exhibition hall as a case study, the characteristic relationship between spatial languages is reflected by studying the measures of centrality and spatial self-explicitness of the cultural education space. The sample cultural education exhibition’s spatial intelligibility is 0.3017, which establishes the aesthetic foundation of the spatial language, according to the results. The symbolic meaning of spatial language is constructed by the degree of spatial synergy, which is 0.51. To sum up, this study offers new ideas for the transformation and development of current cultural and educational exhibitions.

At present, it is an important period of transformation of China’s social and economic structure, and the state vigorously supports the development of the tertiary industry, to achieve the purpose of optimizing the economic structure. Although the animation industry is a new industry with a short history of development in China, with the rapid development of digital media technology in recent years, digital media technology in 2D, 3D animation film, and television drama is increasingly accepted by the audience. This paper uses the current situation of 3D animation group modeling design and genetic algorithm, multi-objective evolutionary algorithm, digital media technology, and animation design briefly explained. Digital media technology has an important role in animation design, which greatly improves the quality of animation works and promotes the development of China’s animation industry.

With the accelerated advancement of big data Internet development, people gradually realize the urgency and importance of energy saving, so the multi-objective optimization research for the performance of office buildings in cold regions is significant, and this paper focuses on the construction of a building window design model based on visual comfort supported by multi-objective evolutionary algorithm- taking office buildings in cold regions as an example, firstly, by reviewing the literature to understand the principle, algorithm, and concept of multi-objective evolution, construct the building window design model based on visual comfort according to the objective function and extract the standard model of inner corridor slab space building in office buildings in cold regions of Harbin and Shenyang. The optimized design of building window parameters was carried out using the established joint simulation and optimization work platform. The results show that for Harbin, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. An increase in window heat transfer coefficient by 0.1W/ (m2 ∙K) Increases are cooling energy consumption by 0.18%, heating energy consumption by 0.78%, and total energy consumption by 0.46%. For Shenyang, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. If the heat transfer coefficient of the window increases by 0.1W/ (m2 ∙K), cooling energy consumption increases by 0.18%, heating energy consumption increases by 0.78%, and total energy consumption increases by 0.46%. This study provides a theoretical basis for extracting standard models for other building types while making the results more generalizable and improving the efficiency of sustainable office building design.

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front. [ABSTRACT FROM AUTHOR]