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The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. In large-scale multiobjective optimization, too many decision variables hinder the convergence search of evolutionary algorithms. Reducing the search range of the decision space will significantly alleviate this puzzle. With this in mind, this paper proposes a fuzzy decision variables framework for largescale multiobjective optimization. The framework divides the entire evolutionary process into two main stages: fuzzy evolution and precise evolution. In fuzzy evolution, we blur the decision variables of the original solution to reduce the search range of the evolutionary algorithm in the decision space so that the evolutionary population can quickly converge. The degree of fuzzification gradually decreases with the evolutionary process. Once the population approximately converges, the framework will turn to precise evolution. In precise evolution, the actual decision variables of the solution are directly optimized to increase the diversity of the population so as to be closer to the true Pareto optimal front. Finally, this paper embeds some representative algorithms into the proposed framework and verifies the framework’s effectiveness through comparative experiments on various large-scale multiobjective problems with 500 to 5000 decision variables. Experimental results show that in large-scale multiobjective optimization, the framework proposed in this paper can significantly improve the performance and computational efficiency of multiobjective optimization algorithms.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

This paper aims to address the significant financial, environmental, and social risks posed by climate change to the mining industry, which is responsible for approximately 8% of global greenhouse gas emissions. With 70% of mining projects for the six largest mining companies located in water-stressed regions, the industry is facing increasing pressure to reduce its impact. Our study investigates the applicability of multi-objective optimization to integrate environmental impact considerations into short-term planning for mining operations. To achieve this, we have reviewed similar studies in various industries and developed an integrated planning framework that incorporates environmental considerations into production planning for surface mines. Our framework has the potential to be utilized in both short- and long-term planning horizons, promoting sustainable mining practices. Through this research, we aim to provide mining engineers with a more comprehensive and effective approach to minimize the environmental impacts of their operations while maintaining efficient production.

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.

Preventive maintenance activities are often the cause of downtime of technical multi-component systems. To minimize maintenance costs and maximize productivity, maintenance tasks are often grouped and carried out simultaneously. We consider the problem of obtaining an optimal maintenance schedule when the multi-component system is also a networked system and can be modeled as a directed graph, where nodes represent machines or workers, and edges represent the exchange of material, information, or work between these nodes. To find efficient maintenance schedules, we formulate a bi-objective optimization problem, which considers the limited availability of maintenance personnel, and we propose an algorithm that finds a set of maintenance schedules, which are a good approximation of the Pareto front in terms of costs and productivity. Through sensitivity analysis we show the extent to which adding maintenance personnel improves system productivity at the expense of increased maintenance costs and idle time of some resources. Besides solving the Pareto-optimal schedules, we show how the developed model is useful in maintenance personnel planning, and we outline limitations and future developments of the present work.

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

In dealing with the expensive multiobjective optimization problem, some algorithms convert it into a number of single-objective subproblems for optimization. At each iteration, these algorithms conduct surrogate-assisted optimization on one or multiple subproblems. However, these subproblems may be unnecessary or resolved. Operating on such subproblems can cause server inefficiencies, especially in the case of expensive optimization. To overcome this shortcoming, we propose an adaptive subproblem selection (ASS) strategy to identify the most promising subproblems for further modeling. To better leverage the cross information between the subproblems, we use the collaborative multioutput Gaussian process surrogate to model them jointly. Moreover, the commonly used acquisition functions (also known as infill criteria) are investigated in this article. Our analysis reveals that these acquisition functions may cause severe imbalances between exploitation and exploration in multiobjective optimization scenarios. Consequently, we develop a new acquisition function, namely, adaptive lower confidence bound (ALCB), to cope with it. The experimental results on three different sets of benchmark problems indicate that our proposed algorithm is competitive. Beyond that, we also quantitatively validate the effectiveness of the ASS strategy, the CoMOGP model, and the ALCB acquisition function.

International audience; Wastewater from the Antibiotical-Saidal pharmaceutical plant (Medéa) was pretreated by coagulation-flocculation using copper sulfate (CuSO4), iron chloride (FeCl3), and mixture of the two salts combined in a 1:1 (v/v) ratio in the present study. Response surface methodology (RSM) was used to optimize pH and coagulant dosage as independent variables, while dissolved organic carbon (DOC), absorbance at 254 nm (UV 254), and turbidity were provided as dependent variables in the central composite design (CCD). Then, the databases of the three treatments were combined in a single database to create a general model valid for the three treatments at the same time, and to predict the reduction rates of DOC, UV254, and turbidity, using the Gaussian process regression coupled with the dragonfly optimization algorithm (GPR-DA). To have the best model obtained between RMS and GPR-DA, an experimental validation was carried out after having had the optimal conditions of each type of coagulant, using the multi-objective optimization technique. The results of the experimental validation show the superiority of the GPR-DA model compared to the RSM model. Also, the results show that the mixed coagulant (CuSO4 + FeCl3) obtain better results than CuSO4 or FeCl3 alone with a treatment efficiency equal to 92.68% at pH = 5 and dosage = 600 mg/L, and the reductions in DOC, UV 254 and turbidity are 97.32%, 82.90% and 96.47%, respectively. © 2022 The Institution of Chemical Engineers

The goal of this experiment is to investigate the effect of laser engraving parameters on Al 6063 aluminum alloy specimens using a fiber laser engraving equipment. The experimental design makes use of Taguchi's L16 orthogonal array. Surface roughness and engraving depth are the responses which are to be optimum for machining process parameters such as fill spacing, power, frequency, and scan speed. Using regression analysis, the mathematical models for individual responses were created. Grey relational analysis coupled with the Taguchi technique has been presented for simultaneous response optimization. The optimal setting parameters for multiple performance characteristics is fill spacing at level-3, power at level-4, frequency at level-2 and scan speed at level-4 i.e., FS3-P4-F2-SS4. To determine the significance of the models, the Analysis of Variance (ANOVA) approach is used. Finally, in the confirmation test, it is observed that the grey relational grade is improved from the initial process parameter combination.

Constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I; 2) II; and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

Purpose Novel aircraft propulsion configurations require a greater integration of the propulsive system with the airframe. As a consequence of the closer integration of the propulsive system, higher levels of flow distortion at the fan face are expected. This distortion will propagate through the fan and penalize the system performance. This will also modify the exhaust design requirements. This paper aims to propose a methodology for the aerodynamic optimization of the exhaust for novel embedded propulsive systems. To model the distortion transfer, a low order throughflow fan model is included. Design/methodology/approach As the case study a 2D axisymmetric aft-mounted annular boundary layer ingestion (BLI) propulsor is used. An automated computational fluid dynamics approach is applied with a parametric definition of the design space. A throughflow body force model for the fan is implemented and validated for 2D axisymmetric and 3D flows. A multi-objective optimization based on evolutionary algorithms is used for the exhaust design. Findings By the application of the optimization methodology, a maximum benefit of approximately 0.32% of the total aircraft required thrust was observed by the application of compact exhaust designs. Furthermore, for the embedded system, it is observed that the design of the compact exhaust and the nacelle afterbody have a considerable impact on the aerodynamic performance. Originality/value This paper presents a novel approach for the exhaust design of embedded propulsive systems in novel aircraft configurations. To the best of the authors’ knowledge, this is the first detailed optimization of the exhaust system on an annular aft-mounted BLI propulsor.

Purpose Relative to in-service aero-engines, the bypass ratio of future civil architectures may increase further. If traditional design rules are applied to these new configurations and the housing components are scaled, then it is expected that the overall weight, nacelle drag and the effects of aircraft integration will increase. For this reason, the next generation of civil turbofan engines may use compact nacelles to maximise the benefits from the new engine cycles. The purpose of this paper is to present a multi-level design and optimisation process for future civil aero-engines. Design/methodology/approach An initial set of multi-point, multi-objective optimisations for axisymmetric configurations are carried out to identify the trade-off between intake and fancowl bulk parameters of highlight radius and nacelle length on nacelle drag. Having identified the likely optimal part of the design space, a set of computationally expensive optimisations for three-dimensional non-axisymmetric configurations is performed. The process includes cruise- and windmilling-type operating conditions to ensure aerodynamic robustness of the downselected configurations. Findings Relative to a conventional aero-engine nacelle, the developed process yielded a compact aero-engine configuration with mid-cruise drag reduction of approximately 1.6% of the nominal standard net thrust. Originality/value The multi-point, multi-objective optimisation is carried out with a mixture of regression and classification functions to ensure aerodynamic robustness of the downselected configurations. The developed computational approach enables the optimisation of future civil aero-engine nacelles that target a reduction of the overall fuel consumption.

Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider's and users' perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider's cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

In state-of-the-art, it has proven that multi-view ensemble learning performs better than classical machine learning algorithms, with the optimized setting of views (subsets of features) usually. Obtaining the appropriate number of views for a given dataset is a complex problem in multi-view ensemble learning (MEL). The finding of the total number of possible views is an NP-hard problem, i.e., equivalent to Bell number. Moreover, the complexity of multi-view learning increases over a higher number of views of the dataset. Therefore, it is highly required to consider a smaller number of views with higher accuracy for optimal performance of MEL. In this work, MEL using Multi-Objective Particle Swarm Optimization (MEL-MOPSO) method has been proposed. The two objectives (number of views of the data and classification accuracy of MEL) have considered where the trade-off between objectives has been performed while searching for an optimal solution using Particle Swarm Optimization (PSO) in the process of multiobjective optimization. The experiments have been done over sixteen high-dimensional datasets using four state-of-art view construction methods. The individual views of the dataset has been utilized to learn through a support vector machine algorithm. The quantitative and non-parametric statistical analyses show that the proposed method has performed effectively and efficiently.

The proposed research article introduces a new hybrid heuristic algorithm based on Autonomous Groups Particle Swarm and Grey Wolf optimizers (AGPSO-GWO) via multi-objective function for the allocation of the Flexible Alternating Current Transmission Systems (FACTS) controllers in power grids to minimize the active power system losses, voltage deviation, and the system operational costs. In this research work, the Static Var Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Unified power flow controller (UPFC) are used as FACTS controllers. A comparative analysis is conducted with other proposed heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Autonomous Group Particle Swarm Optimization (AGPSO) Grey Wolf optimizer (GWO), and improved grey wolf optimization (IGWO) algorithms to confirm and validate the superiority of the proposed technique. Moreover, the proposed AGPSO-GWO has been compared with the previous existing Simulated Annealing (SA) in the literature to prove its superiority and ensure its validity. The proposed scheme has been validated and applied on 30-bus and 118-bus IEEE electric power systems. The numerical results had been carried out using MATLAB and MATPOWER packages. The simulation results show that the proposed algorithm offers the best performance among all algorithms to achieve the optimum global minimum solutions with the highest convergence rate.

Considering the influence of iron core saturation and complex boundary conditions, a novel no-load magnetic field analytical method and system multi-objective optimization model for interior permanent magnet synchronous motor (IPMSM) are proposed. Firstly, an analytical method based on magnetic potential permeance method is proposed. The complex cogging effect and equivalent air gap permeability function are fully considered in this method. Furthermore, on the basis of Taguchi optimization design based on Fuzzy theory, an improved optimization model considering multi-variable interaction is proposed. Among them, in order to comprehensively consider the performance of electromagnetic, vibration, and temperature rise, five optimization objectives including torque, pulsation, cogging torque, loss, and flux density have been optimized. The sensitivity of design variables and the interaction between variables are not ignored. Finally, a 6-pole 36-slot IPMSM is manufactured for prototype experiments. A large number of prototype experiments, finite element, and computational fluid dynamics simulation analysis including no-load magnetic field, back electromotive force, temperature rise, electromagnetic torque, and so on are carried out. The accuracy and rationality of the proposed no-load magnetic field analysis method and global optimization model have been well verified.

With the explosive growth of distributed renewable energy connected to the power system, the application of AC–DC hybrid distribution network with power electronic transformer (PET) becomes more and more extensive. An equivalent loss model is formulated from the perspective of the internal topology of PET. Then, a novel PET power flow model is established to obtain the relationship between the three ports. On this basis, reactive power of PV nodes connecting PET and distributed renewable generation is modified by establishing an equivalent impedance matrix, thus the forward–backward sweep load flow calculation method is improved for power flow calculation and volt/var control of distribution network. Different from traditional reactive optimization methods, a multi-objective reactive power optimization model considering the PET and two types of photovoltaic converters is established in this paper, with the targets of minimizing voltage deviation and power loss simultaneously. Finally, the numerical simulation shows the accuracy, rationality, and feasibility of the proposed method.

In the data mining and machine learning (ML) discipline, feature selection problem is considered among many researchers in the recent times. Feature selection process targets to minimize feature set number and maximize performance accuracy by identifying optimal features. Multiple objectives are considered while identifying the optimal feature hence multi-objective metaheuristic optimization algorithms (MOMOAs) are applied. In this study, literature review is performed MOMOAs-for solving wrapper based feature selection problem (WFS). The literature review for solving WFS problem and discuss the challenges faced by the researchers in solving the feature selection problem. The literature review is performed on all relevant studies published in the last 12 years [2009-2022]. A detailed overview of the feature selection preliminaries, MOMOAs-WFS, role of the classifier in feature selection problem are presented. The outcome of this literature review is to highlight the existing works related to WFS problem using MOMOAs. Finally, the research areas for improvement are identified and emphasized for the scientists to survey in the field of MOMOAs.

Almost 75% of traffic-related emissions are caused by heavy-duty freight trucks and significantly impact neighborhoods, schools, and communities around shipping and distribution lines. With poor air quality and respiratory health, many children in at-risk and disadvantaged communities experience high rates of asthma, lower attendance in school, and lower concentration. This research creates to improve the impacts of heavy-duty electric freight by improving the route efficiency (in terms of energy, time, or route distance) of EV trucks. Our software and algorithms are tested in a simulation environment using data from several thousand fleet trucks operating in the Salt Lake City area. The software shows an anticipated energy reduction of ~ 6% to ~ 10% at the cost of ~ 3% increases in vehicle travel distance. Further, we anticipate positive health impacts in areas of dense trucking as we reduce the energy needs of electrification for fleet operators.

Exploiting energy-saving potentials while obtaining ideal processing results is highly sought for in fiber laser welding (FLW) applications. However, little is known about the correlation between energy consumption and bead geometry of FLW, which is largely determined by processing parameters. In this study, to find the optimal processing parameters of FLW, an ensemble of variable neighborhood search-based gene expression programming (VNS-GEP) and black-box metamodels (EGBM) is presented, combined with non-dominated sorting genetic algorithm (NSGA-II) to form the proposed optimization methodology. Firstly, the optimal weight coefficients of VNS-GEP and two black-box metamodels (Kriging and SVR) are determined considering the leave-one-out generalized mean square error. Then, the EGBM is used to establish the relationship between processing parameters (laser power, welding speed and defocus distance) and response results (energy consumption and bead geometry). Additional experiments are then performed to validate the accuracy of EGBM. Analysis of variance (ANOVA) is carried out to study the main effects of the processing parameters on response results. After that, NSGA-II is employed based on EGBM to approximate the Pareto front of processing parameters with minimal total energy consumption (TEC) and maximal depth-to-width ratio (DWR). Finally, experimental validations show that the obtained solutions can achieve ideal DWR and significant reductions in TEC. In conclusion, the proposed hybrid methodology, EGBM-NSGA-II, can facilitate obtaining optimal welding processing parameters and provide a reliable empirical basis for low-energy laser manufacturing.

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets.

With the widespread application of wireless communication technology and continuous improvements to Internet of Things (IoT) technology, fog computing architecture composed of edge, fog, and cloud layers have become a research hotspot. This architecture uses Fog Nodes (FNs) close to users to implement certain cloud functions while compensating for cloud disadvantages. However, because of the limited computing and storage capabilities of a single FN, it is necessary to offload tasks to multiple cooperating FNs for task completion. To effectively and quickly realize task offloading, we use network calculus theory to establish an overall performance model for task offloading in a fog computing environment and propose a Globally Optimal Multi-objective Optimization algorithm for Task Offloading (GOMOTO) based on the performance model. The results show that the proposed model and algorithm can effectively reduce the total delay and total energy consumption of the system and improve the network Quality of Service (QoS).

Due to the increasing demand for high-speed motors with high stability, bearingless motors have attracted much attention. Bearingless permanent magnet synchronous motors (BPMSMs) are a kind of motor, which uses suspension force to eliminate the motor bearings. This article proposes a five-phase BPMSM with 10 slots and 8 poles. Based on the principle of suspension force of BPMSM, an analytical expression of the suspension force is derived for the proposed BPMSM. To improve the suspension stability and torque, an optimization method consisting of a response surface model and a multiobjective optimization algorithm is employed. Not only the geometric parameters but also the winding distribution are considered in the optimization process. Finally, the effectiveness and superiority of the optimal design are verified by experiment.

Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective functions and various constraints. Due to the presence of constraints, CMOPs' Pareto-optimal solutions are very likely lying on constraint boundaries. The experience from the constrained single-objective optimization has shown that to quickly obtain such an optimal solution, the search should surround the boundary of the feasible region from both the feasible and infeasible sides. In this article, we extend this idea to cope with CMOPs and, accordingly, we propose a novel constrained multiobjective evolutionary algorithm with bidirectional coevolution, called BiCo. BiCo maintains two populations, that is: 1) the main population and 2) the archive population. To update the main population, the constraint-domination principle is equipped with an NSGA-II variant to move the population into the feasible region and then to guide the population toward the Pareto front (PF) from the feasible side of the search space. While for updating the archive population, a nondominated sorting procedure and an angle-based selection scheme are conducted in sequence to drive the population toward the PF within the infeasible region while maintaining good diversity. As a result, BiCo can get close to the PF from two complementary directions. In addition, to coordinate the interaction between the main and archive populations, in BiCo, a restricted mating selection mechanism is developed to choose appropriate mating parents. Comprehensive experiments have been conducted on three sets of CMOP benchmark functions and six real-world CMOPs. The experimental results suggest that BiCo can obtain quite competitive performance in comparison to eight state-of-the-art-constrained multiobjective evolutionary optimizers.

Multi-modality is commonly seen in real-world multi-objective optimization problems. In such optimization problems, namely, multi-modal multi-objective optimization problems (MMOPs), multiple decision vectors can be projected to the same solution in the objective space (i.e., there are multiple implementations corresponding to that solution). Therefore, the diversity in the decision space is very important for the decision maker when tackling MMOPs. Subset selection methods have been widely used in the field of evolutionary multi-objective optimization for selecting well-distributed solutions (in the objective space) to be presented to the decision maker. However, since most subset selection methods do not consider the diversity of solutions in the decision space, they are not suitable for MMOPs. In this paper, we aim to clearly demonstrate the usefulness of subset selection for multi-modal multi-objective optimization. We propose a novel subset selection framework that can be easily integrated into existing multi-modal multi-objective optimization algorithms. By selecting a pre-specified number of solutions with good diversity in both the objective and decision spaces from all the examined solutions, the proposed framework significantly improves the performance of state-of-the-art multi-modal multi-objective optimization algorithms on various test problems.

Sparse multi-objective optimization problems have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been paid to sparse large-scale multi-objective optimization problems, whose Pareto optimal sets are sparse, i.e., with many decision variables equal to zero. To address this issue, this paper proposes an enhanced competitive swarm optimization algorithm assisted by a strongly convex sparse operator. A tri-competition mechanism is introduced into competitive swarm optimization, aiming to strike a better balance between exploration and exploitation. In addition, the strongly convex sparse operator is embedded in the position updating of the particles to generate sparse solutions. Our simulation results show that the proposed algorithm outperforms the state-of-the-art methods on both sparse test problems and application examples.

In addition to ensuring the stability of a Linear Time Invariant system with the LQR control approach, the fact that the feedback gain to be applied to the system is achieved by minimizing a quadratic performance criterion makes this control approach capable of shaping the performance expected from a closed-loop system. At this point, it is important in terms of controller performance to create the quadratic performance criterion or cost function to be minimized, over the weight matrices it contains, in accordance with the purpose. Therefore, the optimization of the weight matrices used in the design of the LQR controller should be carried out in line with the multiple performance objectives expected from the controller. This situation creates a multi-objective optimization problem. Although the weight matrices of the quadratic cost function can be adjusted with the help of classical methods such as trial and error, pole assignment, without optimization, this can be tiring and time consuming. In order to overcome this difficulty, it is possible to benefit from various multi-objective optimization techniques.Within the scope of this study, LQR-based optimal DC motor control is aimed. For the LQR controller, the Q and R weight matrix parameters of the square cost function need to be adjusted. Relevant parameters are adjusted with the help of the optimization algorithm known as Non-Dominated Sorted Genetic Algorithm (NSGA-II), which is one of the multi-purpose optimization techniques. By using the optimum parameters obtained, the performance findings of the synthesized LQR controller on the system are presented with simulation results., LQR kontrol yaklaşımıyla, Lineer Zamanla Değişmeyen bir sistemin kararlılığının temin edilmesi yanında, sisteme uygulanacak geri besleme kazancına, karesel bir performans ölçütünün minimizasyonu yoluyla ulaşılıyor olması, bu kontrol yaklaşımını, kapalı çevrim sistemden beklenen performansı şekillendirme kabiliyetine sahip kılmaktadır. Bu noktada, minimize edilecek karesel performans ölçütünün ya da maliyet fonksiyonunun, içerdiği ağırlık matrisleri üzerinden, amaca uygun biçimde oluşturulması kontrolcü performansı bakımından önem arz etmektedir. O yüzden, LQR kontrolcünün tasarımında kullanılan ağırlık matrislerinin optimizasyonu, kontrolcüden beklenen çoklu performans amaçları doğrultusunda gerçekleştirilmelidir. Bu durum çok amaçlı bir optimizasyon problemini doğurmaktadır. Karesel maliyet fonksiyonunun ağırlık matrisleri, optimizasyon yapılmaksızın, deneme yanılma, kutup atama gibi klasik yöntemler yardımıyla ayarlanabilir olsa da bu durum yorucu ve zaman alıcı olabilmektedir. Bu zorluğun üstesinden gelebilmek adına, çeşitli çok amaçlı optimizasyon tekniklerinden istifade edilmesi mümkündür.Yapılan bu çalışma kapsamında, LQR tabanlı optimal DC motor kontrolü amaçlanmıştır. LQR kontrolcü için söz konusu karesel maliyet fonksiyonuna dair Q ve R ağırlık matrisi parametrelerinin ayarlanması gerekmektedir. Çok amaçlı optimizasyon tekniklerinden biri olan ve Non-Dominated Sorted Genetic Algorithm (NSGA-II) olarak bilinen optimizasyon algoritması yardımıyla ilgili parametreler ayarlanmıştır. Elde edilen optimum parametreler kullanılarak sentezlenen LQR kontrolcünün sistem üzerindeki performans bulguları, simülasyon sonuçları ile sunulmuştur.

A solid transportation problem emerges when the decision variables are represented by three items: the source, the destination, and the mode of transport. In applications, the STP generally requires considering multiple objectives such as cost minimization, time minimization, security level maximization, etc. In this way, a multi-objective solid transportation problem arises. This paper deals with the solution of the problem and analyzes the effect of several important fuzzy aggregation operators on the solution of the problem. In this context, the most commonly used aggregation operators are investigated for this problem. To explain the solution approach, a numerical example from the literature is given and a Pareto-optimal solution set is provided to offer the decision-maker. Furthermore, graphical comparisons and sensitivity analysis are presented with the solution obtained.

Funding Information: This research has been supported by the National Natural Science Foundation of China (Grant No. 22109022 and 51736006 ) and the Fundamental Research Funds for the Central Universities (Grant No. 2242021k30028 ). Publisher Copyright: © 2022 Elsevier Ltd Integrating renewable resources into traditional tri-generation systems helps to reduce fossil fuel use and emissions. A solar thermal and photovoltaic assisted integrated energy system is proposed here using high- performance cooling approaches to provide cooling, heating, and electricity. To find the best system configurations with a focus on the ecological performance, the specific thermo-ecological cost of the energy products considering energy level is optimized employing the cumulative exergy consumption over the whole life-cycle. The results show that the ideal specific costs for cooling, heating and electricity demands are 8.699, 7.129, and 1.970 J/J, respectively. Compared to the method without the energy level consideration, the specific cost of the hybrid system is 0.47 J/J higher due to the lower energy level of water products. Moreover, the specific thermo-ecological cost of natural gas has higher impacts on the performance of hybrid system than the other parameters.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Recently, dynamic multi-objective optimization has received growing attention due to its popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a polynomial fitting-based prediction algorithm (PFPA) and incorporates it into the model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. When an environment change is detected, the main mission of PFPA is to predict high-quality search populations for tracking the moving Pareto-optimal set effectively. Firstly, the non-dominated solutions obtained in past environments are utilized to predict high-quality solutions based on a multi-step movement strategy. Secondly, a polynomial fitting-based strategy is designed to fit the distribution of variables according to the obtained search populations, and capture the relationship between variables in the new search environment. Thirdly, some effective search agents are generated for improving population convergence and diversity based on characteristics of variables. To evaluate the performance of the proposed algorithm, experimental results on a set of benchmark functions, with a variety of different dynamic characteristics and difficulties, and two classical dynamic engineering design problems show that PFPA is competitive with some state-of-the-art algorithms.

The design of fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train FNN, which may easily occur overfitting phenomenon to degrade the generalization performance. To improve the generalization performance, a fuzzy neural network with multi-objective optimization algorithm (MOO-FNN) is proposed in this paper. First, the multi-level learning objectives are designed around the generalization performance to guide the training process of FNN. Then, the method utilizes the approximation error, the structure complexity, and the output smoothness indicators instead of a single indicator to improve the evaluation accuracy of generalization performance. Second, a MOO algorithm with continuous-discrete variables is developed to optimize FNN. Then, MOO is able to use a novel particle update method to adjust both the structure and parameters rather than adjust them separately, thereby achieving suitable generalization performance of FNN. Third, the convergence of MOO-FNN is analyzed in detail to guarantee its successful applications. Finally, the experimental studies of MOO-FNN have been performed on model identification of nonlinear systems to verify the effectiveness. The results illustrate that MOO-FNN has a significant improvement over some state-of-the-art algorithms.

In this paper, we solved an ambulance location problem with a multi-objective framework considering the case of the study of Emergency Medical Service (EMS) of Antofagasta (Chile). Nowadays, in Antofagasta, the ambulances are located in bases that are not necessarily the optimal location achieving an estimated 67% of coverage under the 8 min not meeting the requirements dictated by the Chilean Ministry of Health. We used a multi-objective model considering mean response time, maximum response time, and the demand not covered. The model is solved using an iterative ε-constraint method to generate a Pareto set of efficient solutions. We considered historical data from the years 2015 and 2016 to generate the demand and emergency nodes with a clustering algorithm. The results show improvements on all criteria of the multi-objective model, where we highlight a potential increment on coverage within 8 min from 67 to 99%. In order to test the new policy in a real setting, a pilot plan is proposed, which reaches 89% of coverage within 8 min.

Additive manufacturing (AM) suggests promising manufacturing technologies, which complement traditional manufacturing in multiple areas, such as biomedical, aerospace, defense, and automotive industries. This paper addresses the production planning problem in multi-machine AM systems. We consider all relevant physical and technological parameters of the machines and the produced parts, for using direct metal laser sintering (DMLS) technology. In DMLS technology, each machine produces jobs, where each job consists of several parts arranged horizontally on the build tray. Starting a new job requires a setup operation. We address the simultaneous assignment of parts to jobs and jobs to the machines, while considering the cost and makespan objectives. A unified mixed-integer linear-programming (MILP) formulation that can minimize the above objectives separately and simultaneously is suggested, along with analytical bounds and valid inequalities. Experimentation demonstrates the effectiveness of the proposed formulation with single objectives versus similar formulations from the literature. An efficient frontier approach is applied to the multi-objective problem while generating a diverse set of exact non-dominated solutions. The trade-off between the objectives is analyzed via experimentation. Results show that when identical machines are used, the trade-off is relatively small, and hence the decision-maker can use any of the single objectives. However, when non-identical machines are used, it is important to consider both objectives simultaneously. Moreover, the trade-off increases with the number of machines and heterogeneity of the system, with respect to the size and settings of the machines.

How to evaluate Pareto front approximations generated by multi/many-objective optimizers is a critical issue in the field of multiobjective optimization. Currently, there exist two types of comprehensive quality indicators (i.e., volume-based and distance-based indicators). Distance-based indicators, such as Inverted Generational Distance (IGD), are usually computed by summing up the distance of each reference point to its nearest solution. Their high computational efficiency leads to their prevalence in many-objective optimization. However, in the existing distance-based indicators, the distributions of the solution sets are usually neglected, leading to their lacks of ability to well distinguish between different solution sets. This phenomenon may become even more severe in high-dimensional space. To address such an issue, a kernel-based indicator (KBI) is proposed as a comprehensive indicator. Different from other distance-based indicators, a kernel-based maximum mean discrepancy is adopted in KBI for directly measuring the difference that can characterize the convergence, spread and uniformity of two sets, i.e., the solution set and reference set, by embedding them in Reproducing Kernel Hilbert Space (RKHS). As a result, KBI not only reflects the distance between the solution set and the reference set, but also can reflect the distribution of the solution set itself. In addition, to maintain the desirable weak Pareto compliance property of KBI, a nondominated set reconstruction approach is also proposed to shift the original solution set. The detailed theoretical and experimental analysis of KBI is provided in this paper. The properties of KBI have also been analyzed by the optimal μ-distribution.

This work is geared toward a real-world manufacturing planning (MP) task, whose two objectives are to maximize the order fulfillment rate and minimize the total cost. More important, the requirements and constraints in real manufacturing make the MP task very challenging in several aspects. For example, the MP needs to cover many production components of multiple plants over a 30-day horizon, which means that it involves a large number of decision variables. Furthermore, the MP task's two objectives have extremely different magnitudes, and some constraints are difficult to handle. Facing these uncompromising practical requirements, we introduce an interactive multiobjective optimization-based MP system in this article. It can help the decision maker reach a satisfactory tradeoff between the two objectives without consuming massive calculations. In the MP system, the submitted MP task is modeled as a multiobjective integer programming (MOIP) problem. Then, the MOIP problem is addressed via a two-stage multiobjective optimization algorithm (TSMOA). To alleviate the heavy calculation burden, TSMOA transforms the optimization of the MOIP problem into the optimization of a series of single-objective problems (SOPs). Meanwhile, a new SOP solving strategy is used in the MP system to further reduce the computational cost. It utilizes two sequential easier SOPs as the approximator of the original complex SOP for optimization. As part of the MP system, TSMOA and the SOP solving strategy are demonstrated to be efficient in real-world MP applications. In addition, the effectiveness of TSMOA is also validated on benchmark problems. The results indicate that TSMOA as well as the MP system are promising.

In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with the dimensionality of the objective space. The challenge is exacerbated in expensive optimization domains where evaluation data is at a premium. To overcome insufficient representations of PFs, Pareto estimation (PE) invokes inverse machine learning to map preferred but unexplored regions along the front to the Pareto set in decision space. However, the accuracy of the inverse model depends on the training data, which is inherently scarce/small given high-dimensional/expensive objectives. To alleviate this small data challenge, this paper marks a first study on multi-source inverse transfer learning for PE. A method to maximally utilize experiential source tasks to augment PE in the target optimization task is proposed. Information transfers between heterogeneous source-target pairs is uniquely enabled in the inverse setting through the unification provided by common objective spaces. Our approach is tested experimentally on benchmark functions as well as on high-fidelity, multidisciplinary simulation data of composite materials manufacturing processes, revealing significant gains to the predictive accuracy and PF approximation capacity of Pareto set learning. With such accurate inverse models made feasible, a future of on-demand human-machine interaction facilitating multi-objective decisions is envisioned. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University Published version This research was supported in part by the Data Science and Artifcial Intelligence Research Center (DSAIR), School of Computer Science and Engineering, Nanyang Technological University, the A*STAR Center for Frontier AI Research, the A*STAR grant C211118016 and RIE2025 MTC IAF-PP grant M22K5a0045.

Human activities, principally through emissions of greenhouse gasses (GHGs), have unequivocally caused global warming, with global surface temperature reaching 1.1 °C above pre-industrial (1850–1900) levels in 2011–2020, precipitating widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere. To mitigate escalating climate change effects, the Paris Agreement calls for limiting global warming to no more than 1.5 °C; IPCC global modeled pathways which achieve this goal reach net-zero CO2 emissions by 2050. Deep decarbonization efforts in pursuit of net-zero emissions should be especially prioritized within the energy sector which is responsible for approximately two-thirds of global CO2 emissions. However, increased deployment of weather-dependent renewable energy sources like wind and solar will make the energy supply more variable and out of phase with demand. This research pertains to two load-balancing energy sources capable of increasing grid stability and enabling the transition to renewable energy: hydropower and nuclear energy. My capstone research examines the Columbia River Basin—which provides more than 40% of total U.S. hydroelectric generation—and considers the effects adapting hydropower operations for greater load balancing on the region’s ability to sustain environmental flows for wildlife and ensure sufficient water supply and flood protection. The study uses the Borg multi-objective optimization algorithm in combination with the California and West Coast Power Systems model and a reservoir simulation model to design alternative reservoir operating rules that balance these conflicting objectives. This optimization additionally considers the human and social dimensions of dam operation, as changes in streamflow temperatures from re-designed operating policies can significantly affect salmon and steelhead populations and by extension the local Native American groups who rely on them. My STS research applies the social construction of technology framework to examine the historical adoption of nuclear energy in the United States as well as assess the potential of nuclear energy to facilitate the transition to a carbon-neutral energy sector. I analyze current U.S. energy and climate initiatives to find discrepancies between renewable and nuclear energy policies and compare the projected low and decreasing adoption of nuclear energy which results from these policies with the levels modeled in net-zero emissions studies by Breakthrough Institute, Princeton University, Vibrant Clean Energy, and Williams et al. (2021). Ultimately, I explain the dichotomy between projected and net-zero nuclear deployment with poor project management and a lack of enabling legislative policies which create a reinforcing loop that makes nuclear energy prohibitively expensive as an investment. These two areas of research in conjunction aim to improve the feasibility of cost-efficient net-zero emissions pathways.

Electric vehicles (EVs) offer many improvements over traditional combustion engines including increasing efficiency, while decreasing cost of operation and emissions. There is a need for the development of cheap and efficient charging systems for the future success of EVs. Most EVs currently utilize static plug-in charging systems. An alternative charging method of significant interest is dynamic inductive power transfer systems (DIPT). These systems utilize two coils, one placed in the vehicle and one in the roadway to wirelessly charge the vehicle as it passes over. This method removes the current limitations on EVs where they must stop and statically charge for a period of time. However, the physical designs of the charging unit coils depends on many physical parameters, which leads to complexity when determining how to design the unit. A design then needs to be judged for its quality and performance, for which there are eight proposed objective functions. These objective functions represent performance metrics but are conflicting. Some metrics, such as output power are to be maximized, while others such as stray magnetic field and volume of windings and magnetic cores are to be minimized. Different unit designs will trade off performance for these objectives. In order to address the complex issue of finding near-optimal designs, a machine-learning, Generative Neural Network (GNN) approach is presented for the rapid development of near-optimal DIPT systems. GNNs generate new examples from a trained neural network and have demonstrated remarkable power in creating novel graphic design images from text-to-image training. This stems from their ability to be creative yet constrained in a regular domain. In this case, a simulator network and evaluator generator network are implemented. The simulator is a pre-trained neural network that maps from the physical designs to the objective functions. The generator network is trained to generate the near-optimal physical designs. A design is considered successful if it passes given thresholds for each of the eight objective functions, which evaluate the quality of a design. Before training, the rate of finding successful designs is 0.005%, but within 500 training epochs the rate becomes 98% (about 30 seconds total GPU run time). Engineers and production professionals are interested in both the best performing designs as well as a diversity of configurations to build. In order to improve on these criteria, several different loss functions were developed that incorporate the objective functions. Loss functions are what the neural networks use to determine how to adjust parameters and produce a better design. The various loss functions presented greatly influence the ability of the GNN to produce diverse and high-performance design solutions.

Annual cane pruning of grape vineyards is a time-consuming and labor-intensive job, but no mechanized or automatic way has been developed to do it yet. Robotic pruning can be a perfect alternative to human labor. This article proposes a systematic seven-stage procedure to design a kinematically optimized manipulator, named ‘Prubot’, to manage vineyards’ cane pruning. The manipulator structure was chosen, resulting in a 7R (Revolute) manipulator with a spherical shoulder and wrist. To obtain the design constraints, the manipulator task space was modeled. The robot’s second and third link lengths were determined by optimizing the global translational version of the measure of manipulability and the measure of isotropy of the manipulator arm section. Finally, simulations confirmed the appropriateness of the manipulator workspace. Furthermore, sampling-based path planning simulations were carried out to evaluate the manipulator’s kinematic performance. Results illustrated the impressive kinematic performance of the robot in terms of path planning success rate (≅100%). The simulations also suggest that among the eight single-query sampling-based path planning algorithms used in the simulations, Lazy RRT and KPIECE are the best (≤5s&~100%) and worst ≥5s&≤25% path planning algorithms for such a robot in terms of computation time and success rate, respectively. The procedure proposed in this paper offers a foundation for the kinematic and task-based design of a cane pruning manipulator. It could be promisingly used for designing similar agricultural manipulators.

Despite the growing interest on many-objective evolutionary algorithms, studies on combinatorial problems are still rare. In this study, we choose many-objective knapsack problem (MaOKP) as the benchmark and target the challenges imposed by many-objectives in discrete search spaces by investigating several reference set handling approaches and combining several prominent evolutionary strategies in an innovative fashion. Our proposed algorithm uses elitist nondominated sorting and reference set based sorting, however reference points are mapped onto a fixed hyperplane obtained at the beginning of the algorithm. All evolutionary mechanisms are designed in a way to complement the reference set based sorting. Reference point guided path relinking is proposed as the recombination scheme for this purpose. Repair and local improvement procedures are also guided by reference points. Moreover, the reference set co-evolves simultaneously with the solution set, using both cooperative and competitive interactions to balance diversity and convergence, and adapts to the topology of the Pareto front in a self-adaptive parametric way. Numerical experiments display the success of the proposed algorithm compared to state-of-art approaches and yield the best results for MaOKP. The findings are inspiring and encouraging for the use of co-evolutionary reference set based techniques for combinatorial optimization.

Multitasking optimization can achieve better performance than traditional single-tasking optimization by leveraging knowledge transfer between tasks. However, the current multitasking optimization algorithms suffer from some deficiencies. Particularly, on high similar problems, the existing algorithms might fail to take full advantage of knowledge transfer to accelerate the convergence of the search, or easily get trapped in the local optima. Whereas, on low similar problems, they tend to suffer from negative transfer, resulting in performance degradation. To solve these issues, this article proposes an evolutionary multitasking optimization algorithm for multiobjective/many-objective optimization with two-stage adaptive knowledge transfer based on population distribution. The resultant algorithm named EMT-PD can improve the convergence performance of the target optimization tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population. At the first stage of knowledge transfer, an adaptive weight is used to adjust the search step size of each individual, which can reduce the impact of negative transfer. At the second stage of knowledge transfer, the search range of each individual is further adjusted dynamically, which can improve the population diversity and be beneficial for jumping out of the local optima. Experimental results on multitasking multiobjective optimization test suites show that EMT-PD is superior to other state-of-the-art evolutionary multitasking/single-tasking algorithms. To further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multitasking many-objective optimization test suite is also designed in this article. The experimental results on the new test suite also demonstrate the competitiveness of EMT-PD.

Minimization of negative environmental impact of transport cannot be pursued through mobility limiting, but rather through efficient utilization of natural resources. Some of the ways to reduce harmful pollution and noise include increasing the use of electric energy for transportation and developing electromobility. However, municipalities face difficult decisions connected with the siting of charging stations, for example, budget limitations are an important factor. The presented method allows for selecting a subset of existing parking lots where the charging devices will be sited. As the inputs, only easily accessible data is required. Applying a genetic algorithm combined with fuzzy logic and the Pareto front analysis, one could establish a set of optimal solutions for multiple pre-defined restrictive and partially contradictory criteria. The method has been discussed using a real example of a medium-sized city in southern Poland. Its results have also made it possible to verify whether a budget required for the planned investment is substantiated.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and many objective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems.

With the increasing penetration of renewable energy (RE), the operation of distribution network is threatened and some issues may appear, i.e., large voltage deviation, deterioration of statistic voltage stability, high power loss, etc. In turn, RE accommodation would be significantly impacted. Therefore, we propose a many-objective distribution network reconfiguration (MDNR) model, with the consideration of RE curtailment, voltage deviation, power loss, statistic voltage stability, and generation cost. This aims to assess the trade-off among these objectives for better operations of distribution networks. As this proposed model is a non-convex, non-linear, many-objective optimization problem, it is difficult to be solved. We further propose a deep reinforcement learning (DRL) assisted multi-objective bacterial foraging optimization (DRLMBFO) algorithm. This algorithm combines the advantages of DRL and MBFO, and is targeted to find the Pareto front of proposed MDNR model with better searching efficiency. Finally, case study based on a modified IEEE 33-bus distribution system verifies the effectiveness of MDNR model and outperformance of DRL-MBFO.

Convergence, diversity and feasibility are three important issues when solving constrained multiobjective optimization problems (CMOPs). To deal with the balance among convergence, diversity and feasibility well, this article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) for solving CMOPs. CMOEA-PP has two populations, including propulsive population and normal population, and these two populations work cooperatively. Specifically, propulsive population focuses on convergence. Normal population gives priority to feasibility and is obligated to maintain diversity. To cross through the infeasible region and reach the Pareto front (PF), propulsive population does not consider constraints in the early stage and only considers constraints in the later stage. To further accelerate the speed of convergence, propulsive population only searches for corner solutions and center solutions, while normal population searches for the whole PF. As a result, propulsive population can cross through the infeasible region because of the lack of attention to feasibility. In addition, propulsive population also can guide and accelerate the convergence of the evolutionary process. Comprehensive experiment results on several sets of benchmark problems demonstrate that CMOEA-PP is better than existing state-of-the-art competitors.

Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.

This work investigates the energy efficiency (EE) and the spectral efficiency (SE) performance of multi-hop full duplex cognitive relay networks (MH-FDCRNs) operating in the spectrum sharing mode, under the influence of interference from the primary source. We formulate three distinct optimization problems for finding the optimal power allocation for the secondary nodes in MH-FDCRN: (i) EE optimization with minimum SE requirement, (ii) SE optimization with minimum EE requirement, and (iii) EE-SE trade-off optimization. The impact of residual self-interference (RSI) arising due to full duplex operation at the relay nodes and the inter relay interference (IRI) arising due to frequency re-use are considered. For the EE/SE optimization problems, we transform the original non-convex optimization problems to their convex forms by expressing the numerator of the objective function as the difference of concave functions, and by using parametric transformation. For the EE-SE trade-off optimization problem, we first transform the original multi objective optimization problem into a single objective form; and then into its convex form by introducing an auxiliary variable. Computationally efficient algorithms are proposed to solve the considered problems. With the help of numerical results, the best trade-off among EE and SE can be achieved by proper selection of priority factor