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The paper presents an original method for controlling a surface-electromyography-driven (sEMG) prosthesis. A context-dependent recognition system is proposed in which the same class of sEMG signals may have a different interpretation, depending on the context. This allowed the repertoire of performed movements to be increased. The proposed structure of the context-dependent recognition system includes unambiguously defined decision sequences covering the overall action of the prosthesis, i.e. the so-called boxes. Because the boxes are mutually isolated environments, each box has its own interpretation of the recognition result, as well as a separate local-recognition-task-focused classifier. Due to the freedom to assign contextual meanings to classes of biosignals, the construction procedure of the classifier can be optimised in terms of the local classification quality in a given box or the classification quality of the entire system. In the paper, two optimisation problems are formulated, differing in the adopted constraints on optimisation variables, with the methods of solving the problems based on an exhaustive search and an evolutionary algorithm, being developed. Experimental studies were conducted using signals from 1 able-bodied person with simulation of amputation and 10 volunteers with transradial amputations. The study compared the classical recognition system and the context-dependent system for various classifier models. An unusual testing strategy was adopted in the research, taking into account the specificity of the considered recognition task, with two original quality measures resulting from this scheme then being applied. The results obtained confirm the hypothesis that the application of the context-dependent classifier led to an improvement in classification quality. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) are widespread in practical applications such as engineering design, resource allocation, and scheduling optimization. It is high challenging for CMOPs to balance the convergence and diversity due to conflicting objectives and complex constraints. Researchers have developed a variety of constrained multi-objective optimization algorithms (CMOAs) to find a set of optimal solutions, including evolutionary algorithms and machine learning-based methods. These algorithms exhibit distinct advantages in solving different categories of CMOPs. Recently, constrained multi-objective evolutionary algorithms (CMOEAs) have emerged as a popular approach, with several literature reviews available. However, there is a lack of comprehensive-view survey on the methods of CMOAs, limiting researchers to track the cutting-edge investigations in this research direction. Therefore, this paper reviews the latest algorithms for handling CMOPs. A new classification method is proposed to divide literature, containing classical mathematical methods, evolutionary algorithms and machine learning methods. Subsequently, it reviews the modeling and algorithms of CMOPs in the context of practical applications. Lastly, the paper gives potential research directions with respect to CMOPs. This paper is able to provide guidance and inspiration for scholars studying CMOPs. [ABSTRACT FROM AUTHOR]

Band selection (BS) is an important technique in remote sensing hyperspectral image (HSI) classification that captures informative bands and removes redundant bands, thereby reducing the computational overhead of the model and improving classification performance. Recently, evolutionary algorithm (EA) with good search capability has been successfully extended as an efficient BS method for processing HSIs. However, most of the existing EA-based BS methods still face following challenges: 1) Most of them are designed based on a single search strategy, which may not be sufficient to find the promising subset of bands and tend to fall into the local optimum; 2) Most of them ignore the problem of potential duplicate bands existing in the solution vectors, which leads to some performance degradation. To address these issues, this paper proposes an effective unsupervised multiobjective BS approach through an adaptive particle swarm optimizer (PSO), called APSO-BS. First, a new robust unsupervised multiobjective BS model is designed, which applies information entropy to measure the amount of information contained in bands and uses the structural similarity measure to evaluate the correlation among bands. Second, to effectively solve the multiobjective BS model constructed above, an adaptive particle swarm optimizer is proposed, which first applies a pre-scoring mechanism to estimate the evolutionary states (i.e., diversity and convergence degree) of particles, and then adaptively selects different PSO search strategies for different particles according to their corresponding evolutionary states. In addition, a self-repair mechanism is designed to correct the violating individuals with duplicate bands. Therefore, our proposed method can reasonably address the two major challenges mentioned above. Finally, to evaluate the performance of APSO-BS, four common HSI datasets are adopted in our experiments, and extensive results under three different classifiers show that our proposed APSO-BS outperforms several state-of-the-art BS methods. • A novel BS method via an adaptive PSO is proposed in this paper. • A pre-scoring mechanism is designed to estimate the evolutionary states of particles. • An adaptive PSO is proposed to adaptively select different search strategies. • A self-repair mechanism is used to fix violation individuals with duplicate bands. [ABSTRACT FROM AUTHOR]

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task assignment in a robotic mobile fulfillment system. The algorithm proactively deals with the effects of the processing time variability by evaluating schedules based on both its system performance as well as its robustness under uncertain conditions. The algorithm implements an efficient resource allocation method and a variance reduction technique to reduce the overall computational burden. The experimental results show that the techniques to reduce the computational time are effective and can significantly reduce the amount of simulations required for the fitness evaluation. If a candidate schedule is allocated insufficient simulation replications it can lead to an inaccurate estimate of its long-term average performance. This could lead to an average performance loss of 7.3 %. Furthermore, the proactive scheduler is able to generate schedules that are more robust compared to deterministically generated. A reduction in the average operational cost of about 5 % can be reached, compared to a deterministically generated schedule. The paper reveals the relevance of identifying and modeling uncertainty when designing schedules in an operational system, rather than looking for optimal schedules for ideal scenarios. • We present a proactive evolutionary algorithm for the stochastic RMFS scheduling problem. • We consider the integrated scheduling problem with processing variability. • We implement efficient evaluation, resource allocation and variance reduction procedures. • We evaluate the performance of the algorithm on instances with increasing due date tightness. • The proactive algorithm can generate robust and good quality schedules under uncertainty. [ABSTRACT FROM AUTHOR]

• This paper takes the community electricity trading market with multiple prosumers participating at the distribution network level as the research object, fully considering the impact of the psychological gap effect of prosumers on system operation from a micro-perspective, and proposes a distributed electricity market trading mechanism based on the Stackelberg game, which in order to provide new ideas for the improvement and optimization of the distribution-side electricity market trading system, and the core findings of the paper are as follows:. • The trading mechanism proposed in this paper could better schedule translatable loads and energy storage units, reduce the fluctuation amplitude of the load curve while performing peak cutting and valley filling, stimulate the flexibility of user-side resources, and ensure the stability of system operation. • The trading mechanism proposed in this paper could obtain more evidence-based and reasonable prices for purchasing and selling electricity within the region, ensuring that the distribution network operator could obtain considerable profits while effectively improving the profits of prosumers, which is conducive to improving the economic efficiency of system operation. • In terms of the impact of the gap effect on the energy utility and profits of prosumers, the larger the gap effect coefficient (prosumers are more sensitive to expected deviations), the lower their electricity utility and overall profits, and when the individual electricity utility and overall profits of prosumers are too low, they may withdraw from market transactions. Therefore, government departments could implement policies, such as providing subsidies and adjusting taxes, to further stimulate the enthusiasm of prosumers to participate in distributed market transactions. With the large-scale customer-side development of distributed energy and continuous advancements in power market reform, the market-oriented trading of distributed power generation with the participation of prosumers has gradually become an important solution to promote the consumption of distributed energy. In addition, prosumers exhibit obvious bounded rationality characteristics; therefore, the design of efficient and reasonable trading mechanisms determines the efficiency of the market. From this perspective, a distributed electricity market trading mechanism based on the Stackelberg game is proposed for the community electricity trading market, with multiple prosumers participating in the distribution side. Firstly, the trading architecture of the community electricity market was established under the distribution network level. Secondly, the pricing strategy of the distribution network operator and the quantitative strategy of community prosumers were analysed; on this basis, the characteristics of the bounded rationality of prosumers were considered, and the impact of the psychological gap effect of prosumers on their energy utility was introduced into the objective function of prosumers. This facilitated the construction of a Stackelberg game model in which distribution network operators lead and community prosumers follow. Thirdly, the Stackelberg equilibrium was proven to exist and be unique, which was then solved by the distributed algorithm – combining the evolutionary algorithm and quadratic programming – which improves the solution efficiency and effectively protects the privacy of prosumers. Finally, the effectiveness and feasibility of the proposed trading mechanism were verified through numerical examples. [ABSTRACT FROM AUTHOR]

The fixed-charge transportation (FCT) problem, an extension of the classical transportation problem, holds significance in practical logistics scenarios where fixed charges play a crucial role. Fixed charges categorize it as an NP-hard problem, posing challenges for conventional methods due to their inefficiency, high computational costs, and susceptibility to local optima. This paper introduces an enhanced evolutionary algorithm, offline learning-based competitive swarm optimization (OLCSO), tailored to address the FCT problems efficiently. OLCSO draws inspiration from offline learning, where particles benefit from the experiences of high performers. The algorithm incorporates (i) a ranking-based mechanism where losers learn from peer-to-peer winners based on their ranks, (ii) a two-phase cooperative evolutionary mechanism enhancing searchability and convergence of the algorithm. In the first phase, losers learn from personal best experiences and the shifted center of the population, boosting diversity. In the second phase, losers learn from winners and the center of the population, improving exploitation, (iii) a mutation strategy is embedded to update winner locations in each evolutionary procedure, and (iv) to ensure a feasible solution to FCT problems, the paper also introduces negative and fractional repair mechanisms. OLCSO is applied to solve two sets of FCT problems, linear and non-linear. Experimental results are compared with various heuristic and metaheuristic techniques, commonly used for these problems, using diverse metrics. A comparative analysis of OLCSO's design elements is also conducted. The results demonstrate OLCSO's superiority over existing algorithms. [ABSTRACT FROM AUTHOR]

Normalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-world problems. Although the effect of normalization methods on the performance of EMO algorithms has been investigated in the literature, that of preference-based EMO (PBEMO) algorithms is poorly understood. Since PBEMO aims to approximate a region of interest, its population generally does not cover the Pareto front in the objective space. This property may make normalization of objectives in PBEMO difficult. This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. We present a bounded archive-based method for approximating the nadir point. First, we demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. Then, we show that PBEMO requires normalization of objectives on problems with differently scaled objectives. Our results show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. • This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. • We demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. scaled objectives. • We show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

The prediction of human-virus protein-protein interactions (human-virus PPIs) is significant for exploring the mechanisms of viral infection, making their prediction a necessary and practically valuable research topic. Since conventional methods for the determination of human-virus protein-protein interactions are very complex and expensive, the construction of models plays a crucial role. In this paper, we construct an interpretable model, ECA-PHV, to predict human-virus protein-protein interactions based on an effective channel attention mechanism. First, we utilize five coding modalities, namely AAC, DDE, MMI, CT, and GTPC, to extract the hidden biological information in protein sequences. Individual feature weights are then learned by using a differential evolutionary algorithm that employs weighted combinations to adequately represent various protein sequence information. Next, irrelevant features in multi-information fusion are removed by Group Lasso. Finally, the prediction model is constructed by combining effective channel attention, BiGRU, and 1D-CNN. Compared with existing models, the interpretability framework ECA-PHV proposed in this paper has competitive and stable predictive performance. This shows that our model can efficiently focus on important information about protein sequences. In conclusion, this study accelerates the exploration of human-virus protein-protein interactions and provides some insights of practical value for probing human-virus relationships. • A novel method (ECA-PHV) to predict human-virus protein-protein interactions (human-virus PPIs). • The AAC, DDE, CT, MMI, and GTPC methods are fused to extract protein sequence feature information. • We first constructed an interpretable deep learning framework that combines BiGRU, 1D-CNN, and efficient channel attention to predict human-virus protein-protein interactions. • ECA-PHV increases the prediction performance compared with other methods. [ABSTRACT FROM AUTHOR]

Multi-surrogate assisted evolutionary algorithms, utilizing inexpensive global and local surrogate models to assist evolutionary search, have demonstrated a remarkable ability to solve expensive optimization problems (EOPs). The selected training samples of the local surrogate model used in the current algorithms may not be updated continuously, which could bring about a waste of computing resources. This paper proposes a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy (MS-MTGAwA), in which the local surrogate model is updated adaptively based on information from established local models to enhance the capability in exploiting the optimal solution. The adaptive training sample selection strategy (ATS) applies optimal points found in historical local models as training samples. In addition, MS-MTGAwA inherits the optimization framework of a multi-tasking genetic algorithm. The radial basis function model is chosen as the modeling basis of the global and local surrogate models. A set of common benchmark functions whose dimensions vary from 10 to 100 and the tension/compression spring design problem are adopted to validate the performance of the proposed algorithm. Based on the average best fitness value over thirty independent runs on the benchmark functions, MS-MTGAwA ranks first in Friedman's test compared to five state-of-the-art algorithms including S-JADE, GORS-SSLPSO, SAHO, SAMSO, and MS-MTO. The proposed ATS can be expected to be applied in any algorithms with local surrogate to make full use of historical information of the local model to improve the prediction accuracy of the next local model. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Use the predictive best of historical local surrogate models to update the local model. • Design an adaptive training sample selection strategy for the local surrogate model. • Develop a framework that combined multi-surrogate and multi-population-based multitasking. • Propose a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy. • MSMTGAwA shows strong competitiveness in comparison with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

The paper introduces a transformer-based deep reinforcement learning (T-DRL) approach designed to address the multiobjective multihydropower reservoir operation optimization (MMROO) problem. Unlike existing literature that primarily focuses on maximizing power generation from individual reservoirs, the MMROO model in this study considers the broader context of multiple reservoirs, encompassing total power generation, ecological protection, and residential area water supply. The computational challenges posed by the numerous constraints and nonlinearities of multiple reservoirs render conventional multiobjective evolutionary algorithms both expensive and lacking in generalization capabilities for solving the MMROO problem. To overcome these challenges, the paper proposes a T-DRL approach that leverages the multihead attention mechanism within the encoder module to adeptly extract complex information from reservoirs and residential areas. The two-stage encoder effectively processes diverse information separately. The multireservoir network of the decoder then generates optimal decisions based on contextual information. The case study focusing on Lake Mead and Lake Powell in the Colorado River Basin demonstrates the efficacy of the T-DRL approach, producing operation strategies that outperform a state-of-the-art method. Specifically, the proposed approach yields a 10.11% increase in electricity generation, a 39.69% reduction in amended annual proportional flow deviation, and a 4.10% rise in water supply revenue. Overall, the T-DRL approach emerges as an effective method for the multiobjective operation of multihydropower reservoir systems. • A multiobjective multihydropower reservoir operation problem is addressed. • A T-DRL method is proposed to deal with the multiobjective optimization. • The T-DRL method outperforms current methods in the quality of the obtained PFs. • The proposed strategies obtain more electricity and water supply revenue. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 has caused a great series of negative effects on countries around the world. To curb the pandemic, many governments apply a variety of strict measures including the closure of public places, transportation systems, etc. This paper proposes an algorithm that searches through a set of government policies and selects the policies that minimize the growth rate of the pandemic with minimum cost to society. The proposed algorithm first builds a surrogate model of the pandemic and then searches through the set of government policies via an evolutionary algorithm. The surrogate model in this paper is an ensemble of several learning algorithms that uses a meta-learning algorithm that estimates where in the feature space each of the learning algorithms performs better and then gives a higher weight to the learning algorithm in the voting process. Because using the surrogate models as the fitness function induces uncertainty, in this paper we use an uncertainty reduction mechanism to perform a search through the search space better. Data from 124 countries are used in this paper which contains information about the policies each government has taken since the beginning of the pandemic and 75 environmental factors like climate, religion, politics, economy, etc. Several experimental studies are performed on the output of the modeling system and the optimization algorithms and comparisons are performed with state-of-the-art methods. • A novel ensemble learning algorithm is proposed to build a model of the COVID-19 pandemic. • A list of factors that may affect the behavior of the pandemic are studied. • The best set of features for the modeling algorithm is found. • An evolutionary algorithm is proposed that optimizes the set of government policies. • An uncertainty reduction is proposed to remove approximation uncertainty. [ABSTRACT FROM AUTHOR]

Tourist trip design is a fast-growing area of research. Tourist interest, budget, travel style, safety, and the existence of travelers with special needs (for example, wheelchair accessibility) are some of the elements to consider for obtaining personalized routes. Including these elements in a single mathematical model can considerably complicate the solution process. Also, route decision-making is affected by the context (health, social, political, economic, etc.) in which decisions are made. In this paper, the first aim is to propose a three-step methodology to obtain contextualized solutions to a tourist trip design problem (TTDP) with time-dependent recommendation factors. The methodology consists of (1) providing a basic TTDP model that avoids the complexity of including contextual information, (2) obtaining a set of solutions to the problem using a Modeling to Generate Alternatives (MGA) approach, and (3) using a recently developed a posteriori method to include the contextual information through fuzzy propositions. The second aim of the paper is to evaluate three algorithmic strategies for the MGA step. Lastly, considering a context for people with mobility impairments, an example is solved using real data. The results show the usefulness of the proposed methodology in solving the TTDP with contextual information. • A tourist trip design problem with time-dependent recommendations factors is solved. • A methodology to obtain contextualized solutions is developed. • An accessibility context is modeled with fuzzy propositions. • Solutions are obtained by using the modeling-to-generate-alternatives approach. • Best solutions are suitable for the accessibility context. [ABSTRACT FROM AUTHOR]

Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Improvement can be made by enhancing local search of grey prediction evolution. • Mine local evolutionary trend of population series by quadratic interpolation. • Construct a surrogate model by using the local evolutionary trend. • Design an improved grey prediction evolutionary algorithm with a surrogate model. • The proposed algorithm is more competitive than the well-established. [ABSTRACT FROM AUTHOR]

The indicator-based multi-objective evolutionary algorithms have demonstrated their superiority in handling diverse types of multi- and many-objective optimization problems. However, these evolutionary algorithms still face significant challenges in balancing convergence and diversity of the evolutionary population, despite numerous auxiliary mechanisms designed to improve their performance. To address this issue, an adaptive parental guidance strategy (APGS) is proposed in this paper. On the one hand, APGS leverages the current population to evaluate the quality of the newly generated offspring. On the other hand, it employs an adaptive threshold to select offspring individuals with better convergence and diversity. This approach enhances the convergence and diversity of the candidate solution set throughout the evolutionary process, thereby ensuring high-quality obtained solutions. By incorporating the APGS, this paper proposes a new indicator-based evolutionary algorithm with parental guidance (IEAPG). Simulation results on several test suites and real-world problem show that compared to PREA, SPEA/R, GrEA, TS-NSGA-II, HEA and MaOEA/IGD, the proposed IEAPG has better performance and robustness in dealing with different types of multi- and many-objective optimization problems. Furthermore, further investigation reveals that the incorporation of the APGS can significantly improve the performance of different categories of multi- and many-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The railway world is undergoing major changes. The advent of new technologies allows us to rethink the train system and face new challenges, but one must not forget all the ecological constraints that are now accentuated by the increase in energy costs. This paper focuses on the optimization of the driver commands to limit the energy consumption of the trains under punctuality and security constraints. This problem falls within the framework of control optimization problems for nonlinear dynamic mechanical systems in the presence of constraints and uncertainties. A four-step approach is then proposed in this paper to solve this problem: (1) the introduction of simplified and fast-to-evaluate models to model the nonlinear dynamic behavior of the train and its energy consumption; (2) the identification in a Bayesian formalism of the parameters on which these models depend from on-track measurements on commercial trains; (3) the reformulation of the optimization problem so that it integrates the uncertainties related to an imperfect knowledge of these estimated parameters; (4) the resolution of the optimization problem using evolutionary algorithms. The main specificity of this work lies in the fact that not only the objective function to be minimized, here the energy consumed by the train, is impacted by the uncertainties, but also the admissibility constraints of the solution, here punctuality and operating safety. The integration of the uncertainties in the search for the control function is thus not trivial and requires several original adaptations in order to make the final optimization problem well posed. • The paper considers a particular constrained control optimization problem. • Uncertainties on the cost function and constraints make the problem difficult. • The control problem is first reformulated to make the constraints deterministic. • Evolutionary algorithms are then used to identify optimized control functions. • An application to the minimization of the train electrical consumption is presented. [ABSTRACT FROM AUTHOR]

In recent years, evolutionary multi-objective based community detection algorithms are widely used in attribute networks, but such algorithms usually ignore the attributes between nodes and may lead to incorrect node division. Therefore, this paper proposes an evolutionary multi-objective attribute community detection based on similarity fusion strategy with central nodes. First, this paper proposes a pre-processing of similarity fusion to completely utilize node and topology information, the topological similarity matrix of the network is effectively combined with the attribute similarity matrix to obtain the fusion similarity matrix, and finds central nodes based on node assignment of the fusion similarity matrix. Then, the pre-division set of the attribute network is selected by central nodes and the label update equation is designed. In the scheme of evolutionary algorithm, using the community results initialized for the network after the label update can speed up the iterative process of the algorithm. Finally, a modularity-based community integration strategy is proposed to correct community detection results of attribute network based on modularity of neighbor nodes. Comparing the effectiveness of the proposed algorithm with four excellent community detection algorithms for attribute networks on fifteen real networks and six synthetic networks, the proposed algorithm can achieve high division accuracy in most networks. • A similarity fusion strategy designed by combining the topological and attribute similarity to find central nodes. • The pre-division set of attribute network is selected based on the central nodes and the label update equation is designed. • A modularity-based correction method is proposed based on the neighbors of nodes and community integration strategy. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the "curse of dimensionality" when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box adversarial samples based on differential evolution is proposed. In the first stage, a representative image-guided differential evolution method is proposed to quickly generate a universal adversarial perturbation with a high attack success rate in the frequency-domain. In the second stage, a space reduction strategy based on frequency-domain pixel blocks is designed to reduce the search space and alleviate the problem of "curse of dimensionality". In addition, a new space–frequency interaction sensitivity measure is introduced to evaluate the similarity between the adversarial samples and the original images. The adversarial perturbations obtained by the measure are more in line with the subjective perception of the human eye. Finally, compared with several typical black-box adversarial sample generation algorithms, experimental results show that the proposed algorithm can achieve higher attack success rate with less prediction times. • Proposing a two-stage evolutionary generation framework of adversarial samples. • Developing a space–frequency interaction sensitivity measure. • Presenting a representative image-guided differential evolutionary algorithm. • Designing an individual adversarial perturbation generation algorithm. [ABSTRACT FROM AUTHOR]

Dynamic constrained optimization problems (DCOPs) are common and important optimization problems in real-world, which have great difficulty to solve. Dynamic constrained evolutionary algorithms (DCEAs) are widely used methods for solving DCOPs. However, existing DCEAs often struggle with convergence, particularly for DCOPs with drastic dynamic changes or intricate constraints. To address this issue, this paper proposes a novel DCEA called DCEA-DQN, which leverages the powerful perception and decision-making capabilities of Deep Q-Network (DQN). DCEA-DQN integrates two DQNs to enhance its performance. The first DQN is designed to adaptively respond to dynamic changes, enabling effective handling of DCOPs with various types and degrees of changes. It provides a high-quality re-initialized population for subsequent static optimization, resulting in faster and improved convergence. The second DQN is introduced to guide the mutation direction during offspring generation. It steers the population towards better feasible regions or directs it towards the optimal individual within the current feasible region. Moreover, a penalty mechanism is employed to handle constraints during offspring generation. To evaluate the performance of DCEA-DQN, comprehensive empirical studies are conducted using a new test suite called C-GMPB and a dynamic flexible job-shop scheduling problem. The experimental results, using two commonly used metrics E B and E O in the field of DCOPs, demonstrate that DCEA-DQN outperforms six state-of-the-art DCEAs and achieved optimal performance on 80% and 75% of all 24 test problems, respectively. The source code for DCEA-DQN is available at https://github.com/CIA-SZU/YRT. • A change response strategy based on DQN is proposed for complex DCOPs. • An offspring generation strategy based on penalty and DQN is proposed for complex DCOPs. • A new complex dynamic constrained test suite C-GMPB is proposed. [ABSTRACT FROM AUTHOR]

The key to solving constrained multiobjective optimization problems (CMOPs) lies in maintaining the feasibility, convergence, and diversity of the population. In recent years, various constraint handling techniques (CHTs) and strategies have been proposed to enhance the performance of constrained multiobjective evolutionary algorithms (CMOEAs). However, most of these algorithms face difficulties in dealing with problems that have large infeasible regions and discontinuous small feasible regions, as they have trouble crossing large infeasible regions while simultaneously maintaining the convergence and diversity of the population. To tackle this issue, this paper proposes a dual-population auxiliary coevolutionary algorithm with an enhanced operator, denoted as DAEAEO. Auxiliary population 1 employs an improved ϵ -constraint handling technique to provide high-quality feasible solutions for the main population. Auxiliary population 2 adopts the non-dominated sorting method to provide favorable objective information for the main population to help it cross the infeasible region. In addition, to further improve diversity, each population adopts an enhanced operator and a genetic operator to generate offspring, respectively. Finally, knowledge transfer between offspring is realized. Compared to six state-of-the-art CMOEAs on DASCMOPs, LIR-CMOPs, DOC test suites, and two real-world problems, the proposed DAEAEO achieved superior performance, especially for CMOPs with large infeasible regions and discontinuous small feasible regions. • A constrained multiobjective coevolutionary algorithm is proposed, termed DAEAEO. • A dual-population auxiliary evolutionary strategy is proposed. • The use of knowledge transfer promotes information interaction among offspring. • An enhanced search operator is developed to balance convergence and diversity. • The effectiveness of DAEAEO is verified on test suites and two real-world problems. [ABSTRACT FROM AUTHOR]

[Display omitted] • AgrOptim is an extensive cropping system simulation optimization framework. • Crop and environmental risk simulation models are coupled with genetic algorithms. • Management practices that optimize different biophysical and economic objectives are identified. • Trade-offs between economic and biophysical indicators are simulated and quantified. • Sustainability gaps are quantified for three cropping systems in Argentina. Cropping systems should be designed to be more productive and have a smaller environmental footprint to sustainably meet the growing demand for food, fiber, and fuel. However, this requires the evaluation and ranking of many cropping system designs based on their economic and biophysical performance, which are often in conflict. Although field experiments and simple crop simulation models have been used for this purpose, studies have generally considered a limited number of agronomic decision combinations or indicators that partially capture ecosystem functions. Coupling evolutionary algorithms with process-based crop simulation models provides a less resource-intensive alternative and can incorporate many indicators to (1) quantify the trade-offs between biophysical and economic performance, and (2) identify the set of agronomic decision combinations that minimize these trade-offs. The objective of this paper was to present AgrOptim, a novel cropping system simulation optimization framework that uses genetic algorithms to optimize a holistic set of biophysical and economic performance indicators through different combinations of agronomic decision variables (i.e., crop sequence, crop structure, pesticide dose, and fertilizer dose). Indicators were derived from a process-based crop simulation model, an ecotoxicological risk simulation model, and emergy synthesis. The framework was implemented in Argentina to (1) characterize the relationship between economic and biophysical indicators and (2) evaluate the current state and potential improvements of three frequently used cropping system designs. A multi-objective optimization experiment was designed to simultaneously optimize 30-year cropping sequences based on one economic objective (return on investment) and four biophysical objectives (crop residue carbon inputs, precipitation use efficiency, nonrenewable to renewable energy ratio, and pesticide ecotoxicity). Results showed that trade-offs exist between economic and all biophysical objectives, albeit with varying intensities. Additionally, the decision variables that provided improved performance in terms of carbon residues, precipitation use efficiency, and ecotoxicological risk also presented higher levels of nonrenewable energy use. For the three frequently used cropping system designs, the decision variables that improved the performance of each indicator were identified. These findings highlight the challenges faced by agricultural producers considering the trade-offs between their economic and biophysical objectives. Additionally, they reveal potential model-aided improvements that can be obtained using crop simulation models and optimization algorithms to redesign cropping systems. [ABSTRACT FROM AUTHOR]

To drive a paradigm shift towards a human-centric approach in remanufacturing systems and promote energy-efficient, carbon-reduction-friendly upgrades, this paper introduces a stochastic human–robot collaborative disassembly line balancing problem for real-world disassembly lines characterized by uncertain operation times. The developed mathematical model takes into account task priority relationships, task attributes, human–robot collaboration, and cycle time constraints, with the goal of optimizing workstation numbers, smoothing indices, and carbon emissions. Subsequently, we propose a hybrid evolutionary algorithm (HEA) to efficiently address this problem. The algorithm includes methods for generating initial solutions and decoding them based on random disassembly time. Additionally, an enhanced differential evolution operation is integrated into the non-dominated sorting genetic algorithm II (NSGA-II) framework to improve the algorithm's evolutionary and search capabilities. Validation experiments involving 21 benchmark cases demonstrate that HEA significantly outperforms seven other algorithms reported in the existing literature. Finally, the proposed algorithm is applied to a printer case comprising 55 components. The results underscore the algorithm's superior search capabilities compared to the innovative hybrid algorithm, genetic simulated annealing algorithm, as well as traditional NSGA-II and evolutionary algorithms, under both low and high randomness conditions. • The carbon emission evaluation index was developed for HRC-DLBP. • Incorporating product- and operator-induced uncertainty into HRC-DLBP. • Developed a multi-objective hybrid evolutionary algorithm for SHRC-DLBP. • Apply the proposed method to the discarded printer disassembly case. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) require extensive fitness evaluations, which constitutes a barrier to solving computationally complex problems. In contrast, surrogate-assisted evolutionary algorithms (SAEAs) have the potential to solve complex expensive optimization problems. This paper proposes a surrogate-assisted social learning particle swarm optimization (SASLPSO) to handle expensive optimization problems. An adaptive local surrogate (ALS) strategy introduced in SASLPSO is introduced to accurately fit the landscape near the global optimum. ALS consists of two layers of surrogate, the part of the particles closest to the optimal particle and the closest to the optimal particle among the particles that have been eliminated historically. The proposed SASLPSO effectively combines global surrogate (GS) and adaptive local surrogate (ALS) to balance global exploration and local exploitation. Furthermore, a novel random group-based pre-screening (RGBPS) strategy is proposed to screen promising particles for real function evaluation. The proposed SASLPSO is compared with four other state-of-the-art SAEAs on 30D, 50D, and 100D benchmark functions. In addition, the significance of the SASLPSO algorithm was also verified using the Wilcoxon rank test. The test results on the benchmark function show that the SASLPSO algorithm performs better than other comparison algorithms, especially when dealing with high-dimensional benchmark functions. To further validate the effectiveness of the SASLPSO algorithm in solving expensive optimization problems, it was also applied to feature selection problems and real-world engineering problems. In addition, in the real application of node deployment in 3D wireless sensor networks, the highest coverage rate of the SASLPSO algorithm can reach 99.96%, confirming its performance advantages in solving real application problems. Finally, in the application of network intrusion detection, SASLPSO has shown more advantages in multiple metrics, proving its versatility. • SASLPSO is proposed to solve expensive optimization problems. • A novel RGBPS strategy is proposed to screen promising particles. • An adaptive local surrogate (ALS) is proposed to local fitting. • The proposed SASLPSO effectively balances global exploration and local exploitation. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive uniform search framework designed for constrained multi-objective optimization. The framework comprises three key components: a global uniform exploration strategy, a local greedy exploitation strategy, and a search switch mechanism. These components work together to facilitate comprehensive exploration of promising areas while maintaining a balance between global exploration and local exploitation. Specifically, the global uniform exploration strategy ensures even distribution within promising areas, preventing any oversights during exploration. The local greedy exploitation strategy divides these areas into sub-areas and employs a feasibility-led constraint handling technique to enhance efficiency in identifying optimal solutions. Additionally, the search switch dynamically adjusts the search strategy between global exploration and local exploitation. Numerical simulations on various benchmark suites and real-world problem demonstrate the strong performance of the framework in addressing constrained multi-objective optimization problems. The comparison results show that compared with eight recently proposed algorithms, the proposed framework is more robust in solving diverse constrained multi-objective optimization problems. • GUE removes close individuals, promoting even distribution. • LGE divides areas for effective local optimization. • A novel switch adapts search between GUE and LGE. [ABSTRACT FROM AUTHOR]

Dynamic multiobjective optimization problems (DMOPs) change over time, which require Evolutionary algorithms (EA) track Pareto-optimal solution (PS) or/and Pareto-optimal front (PF) in a constantly change environment. Prediction-based algorithms are the most common method to solve DMOPs. However, a single elaborate predictor is not always suitable for extracting changing pattern of different DMOPs, and not to mention DMOPs with unpredictable changes. To overcome these limitations, a simple yet effective algorithm, response strategies based on adaptive selection (RSAS), are proposed in this paper. When a change occurs, RSAS provides diversified solutions by different proposed strategies, that is, a center-guided self-correcting prediction, an individual-based prediction, and a precision-controllable mutation. Based on the quality of their generated solution set, an adaptive selection mechanism can adjust the selection probability of these three strategies. Since RSAS consists of not merely two different prediction strategies but also a mutation strategy, which can be more responsive to predictable and unpredictable changes. To validate the performance of RSAS, DMOP benchmarks in CEC2018 with recommended configurations are adopted. Compared to four state-of-the-art algorithms, the experimental results show that RSAS is effective and efficient. • A proposed RSAS can deal with predictable and unpredictable changes. • RSAS proposes two predictions and a mutation method as change response strategies. • A proposed adaptive selection method combines these three proposed strategies. • The performance of RSAS is verified on CEC2018 DMOP benchmarks. [ABSTRACT FROM AUTHOR]

The outbreak of uncertain events, e.g., financial crisis, regional conflict and abrupt contagion, has a significant impact on residents' income. Hence, the wealth management and portfolio selection become more and more important. In addition, the behavioral finance believes that decision-making process of the investors not only depend on utility maximization, but also on who to compare with. It differs from the traditional finance based on rational cognitive acquiring and decision-making of agents, in this paper, we consider both investment uncertainty and investment utility with reference level for portfolio selection. First, we introduce the uncertainty theory and loss aversion utility with reference level based on behavioral finance. Then we study an uncertain multi-period portfolio selection problem. Moreover, we formulate an uncertain behavioral portfolio selection model with three objective functions for which we propose an equivalent program consisting of a main model and two auxiliary models for tri-objective optimization model. Thereafter, we use a modified evolutionary root system growth algorithm to solve the transformed models. Finally, a numerical simulation allows to show the practicability of proposed models and validity of modified algorithm. [ABSTRACT FROM AUTHOR]

The multi-objective multi-task evolutionary algorithm is emerging in the field of evolutionary computating. A great deal of multi-objective multi-tasking algorithms have been proposed recently and proved to be superior in solving many problems. However, in the real world, there are numerous high-dimensional objective problems that need to be solved. With the increasing number of objectives, the slow convergence speed, high computational complexity and reduced population diversity will occur in the existing multi-objective multi-tasking algorithms. There is a growing need to study high-dimensional objective algorithms in multi-tasking environment. To fulfill this research gap, a novel many-objective multi-tasking evolutionary algorithm (MaMTO-ADE) is put forward in this paper. The reference points-based non-dominated sorting method is introduced, which guarantees the diversity of the population in high-dimensional space. And a new offspring generation strategy is proposed to accelerate the population convergence and enables the population to generate high-quality offspring. The performance of MaMTO-ADE is verified on the classical benchmark problems, and the experimental results emphasize the excellent competitiveness of MaMTO-ADE compared to other related algorithms. • A novel many-objective multitask framework is proposed to solve the high-dimension multi-objective problems. • This algorithm uses a new mixture probability model-based knowledge transfer method. • A novel offspring generation strategy is proposed. • The new individual selection method is used for inter-task crossover. [ABSTRACT FROM AUTHOR]

Robust design in analog integrated circuits (ICs) is intricate due to process variations, culminating in notable performance uncertainties. Contemporary surrogate-based techniques depend on lengthy pre-training and are prone to prediction inaccuracies amidst process, voltage, and temperature (PVT) fluctuations. This paper introduces a framework that amalgamates a problem reformulation strategy with an evolutionary algorithm, expediting robust analog IC design in the face of PVT variations. Initially, by segmenting decision variables into subgroups and correlating them with weight variables, the decision space is adeptly reconstructed. An indicator function is subsequently utilized to minimize the objective space's dimensionality, thereby reshaping the original problem into a low-dimensional single-objective optimization problem. Given the reduced dimensionality of both objective and decision spaces, the Bayesian optimization algorithm is invoked to efficiently diminish performance disparity under variations. Consequently, a collection of quasi-optimal, robust solutions is acquired. In the final phase, a multi-objective evolutionary algorithm ensures the even distribution of these quasi-optimal solutions along the approximate Pareto optimal front. To validate the effectiveness of our framework, we conducted tests on three integrated voltage references and compared the results with four cutting-edge methods. The empirical data highlight the superior performance of our framework, which yields a 3 to 40-fold increase in the quality factor and a 30% to 60% reduction in necessary function evaluations. Additionally, the framework led to a 40% to 70% decrease in the overall execution time. • Novel framework for robust analog IC optimization under PVT variations. • Reformulating the original problem into a low-dimensional single-objective one. • Obtaining PVT-robust quasi-optimal solutions via problem reformulation. • Enhancing circuit performance with multi-objective optimization. • Reducing function evaluations and simulation costs via worst-case-driven strategy. [ABSTRACT FROM AUTHOR]

The multi-robot task allocation problem has garnered significant attention in research and development. This paper addresses the multi-robot task allocation problem by introducing a unified model that seamlessly transforms into four popular mathematical models through simple parameter adjustments. We propose an efficient indicator-based multi-objective evolutionary algorithm with a hybrid encoding scheme for task execution order and robot starting point information. The algorithm employs the hypervolume indicator for environmental selection to enhance convergence and the modified crowding distance for archive updates to promote diversity. Additionally, an adaptive archive update mechanism is designed for time efficiency. In experiments comparing our proposed algorithm with 8 state-of-the-art algorithms on 18 randomly generated instances of various sizes, along with 4 real-world problems from the TSPLIB benchmark, our algorithm consistently outperformed the comparison algorithms in five key indicators. These results underscore the effectiveness of our approach in addressing real-world multi-robot task allocation problems. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new approach is studied for near-infrared (NIR) wavelength selection. • Maximum information coefficient (MIC) is modified with orthogonal projection. • Firefly algorithm (FA) is discretized by 0–1 encoding. • Discrete FA is improved in cooperation with a designed adaptive mapping function. • Modified MIC and adaptive DFA methods are fused for NIR model optimization. Full-length spectral data analysis has a big problem that the variables are highly in collinearity and correlation. Spectral wavelength selection is a continuing hot topic in quantitative or qualitative analysis. In this paper, we propose a new approach for near-infrared (NIR) wavelength selection. The novel strategy mainly refers to the modification of maximum information coefficient (MIC) method and an improvement of firefly evolutionary algorithm. We introduce the orthogonal decomposition to modify the MIC method, so as to search the informative signals conceived in projection vectors. We also raise the common firefly algorithm (FA) as in the discretized mode, and design a novel adaptive mapping function to improve its intelligent computing effect. In experiment, the modified MIC (MIC m) method and the adaptive discrete FA algorithm (DFA adp) are joint together for combined optimization of the NIR calibration model. The proposed combined modeling strategy is applied for quantitative analysis of the fishmeal samples, in the concern to select their informative variables/wavelengths. Experimental results indicate that the combination of MIC m and DFA adp perform better than traditional MIC method and common DFA. We conclude that the proposed combined optimization strategy is beneficial for wavelength selection in NIR spectral analysis. It is anticipated to be validated for further applications in a wide range. [ABSTRACT FROM AUTHOR]

The influence of reputation and emotions on the evolution of cooperation is acknowledged, but current research predominantly focuses on one aspect. This paper integrates both reputation and emotions to examine their collective impacts on the evolution of cooperation. Initially, we define the expression of individual emotional profiles and emotional reputation. Subsequently, we consider the combined effects of the emotional reputation of an individual's neighbors and their interactive individuals on emotional responses. Further, an emotional transfer activation parameter is introduced. Lastly, we construct an algorithm for the evolutionary game of emotions. Simulation results demonstrate that considering emotional reputation is more conducive to the cooperation. The emotional reputation of interacting individuals has a greater influence on the development of positive emotions and the establishment of collaboration within groups than that of neighboring environments. In heterogeneous networks, considering the emotional reputation of neighbors increases the probability of "free-riding" by unfriendly people. When game parameters are set in favor of betrayal and negative emotions, the system evolves to produce "cautious and friendly people". Individuals with stable emotions, high sympathy for the weak, and high respect for the strong dominate the group. Furthermore, the individuals' emotional expression towards both the weak and the strong exhibits an evolutionary synchronicity. Compared to other network structures, scale-free networks are more conducive to the evolutionary dominance of emotions of high respect for the strong. • Emotional reputation influences the evolution of emotion and cooperation. • An emotional transfer activation parameter is introduced. • The proposed model integrates environmental and interactive individual's emotional reputation. [ABSTRACT FROM AUTHOR]

• The coupling between generator terminal voltage and system frequency is considered. • Realize the coupling of power and status information between sub-microgrids. • Coordinate the microgrid frequency, MTs regulation cost and EVs user demand. • EDRL can deal with cheating and sparse rewards in multi-objective control. • The convergence of algorithm is improved, and the transmission distance is reduced. The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control. [ABSTRACT FROM AUTHOR]

As a special type of multi-objective combinatorial optimization problems (MOCOPs), the multi-objective traveling salesman problem (MOTSP) plays an important role in practical fields such as transportation and robot control. However, due to the complexity of its solution space and the conflicts between different objectives, it is difficult to obtain satisfactory solutions in a short time. This paper proposes an end-to-end algorithm framework for solving MOTSP based on deep reinforcement learning (DRL). By decomposing strategies, solving MOTSP is transformed into solving multiple single-objective optimization subproblems. Through linear transformation, the features of the MOTSP are combined with the weights of the objective function. Subsequently, a modified graph pointer network (GPN) model is used to solve the decomposed subproblems. Compared with the previous DRL model, the proposed algorithm can solve all the subproblems using only one model without adding weight information as input features. Furthermore, our algorithm can output a corresponding solution for each weight, which increases the diversity of solutions. In order to verify the performance of our proposed algorithm, it is compared with four classical evolutionary algorithms and two DRL algorithms on several MOTSP instances. The comparison shows that our proposed algorithm outperforms the compared algorithms both in terms of training time and the quality of the resulting solutions. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for optimising routing for towing of fully assembled Floating Offshore Wind Turbines using a purpose-built ship simulator to generate datasets describing dynamics for a towing arrangement together with the engine data of the ship, and using such dataset and historical metocean data to perform multi-objective route optimisation for the tow using NSGA-II evolutionary algorithm. The work introduces the new ship simulator and the modelling of the platform VolturnUS-S, including the discussion of a comparative experiment between the model in the ship simulator and a 1:70 scale model in a wave tank. This is then followed by a presentation of the towing experiments, the characteristics of the data obtained from them, and the methodology for the optimisation of the towing routes with the following minimisation objectives: the duration of the tow, the maximum tension in the towing line, and the carbon emissions. Results are presented and discussed together with the limitations. The methodology has the potential to offer rapid and accurate results, providing a framework for safe, fast, and economical experimental process that could enhance visibility for operations before high maturity level is achieved or they can be physically performed, and contribute to improve marine operations. • Multi-objective routing optimisation of a floating offshore wind turbine. • Presentation of a new ship simulator designed for research. • Comparison of experiments performed in the simulator and in a wave tank. [ABSTRACT FROM AUTHOR]

Operational optimization of the Hall-Héroult cell is essential for achieving high efficiency and cost-effectiveness in the aluminum electrolysis process. Due to the complicated mechanism and variable working conditions, manual operational decision-making is extensively used in practice. They challenge the reliable and optimal operation of aluminum electrolysis process. In this paper, we develop a data-knowledge-driven decision-making support system (DMSS) to achieve operational optimization for the aluminum electrolysis process. DMSS consists of a prediction model, a multi-objective optimizer, and a knowledge-guided decision-making module. Specifically, we propose a working-conditions-based attention with the exogeneous inputs auto-regressive neural network (WCA-NARX) to construct a data-driven heat balance indicator (HBI) prediction model, where the working condition-related variables serve as covariates to enhance predictability. In addition, the designed structure of introducing working condition information through an attention mechanism can decouple covariates from operational variables and autoregressive variables, facilitating subsequent operational optimization. Then, a novel knowledge-assigned reference vector evolutionary algorithm (KRVEA) is designed to solve the multi-objective optimization problem of the aluminum electrolysis process, in which Pareto front solutions can be solved in the preferred region. Finally, we utilize the knowledge base that stores historical optimization cases to make decisions regarding the selection of a practical-requirement-based control scheme from the Pareto set. Real-world industrial experiments demonstrate that DMSS can effectively enhance control performance and achieve superior results compared to other competitive methods. The source code is available at https://github.com/wjiecsu/WCA-NARX. [ABSTRACT FROM AUTHOR]

• A multi-task multi-objective evolutionary network is proposed. • The framework combines two tasks by effective high-frequency information sharing. • Multi-task learning is modeled as a deep MOEAD to obtain trade-off solutions. • The method provides diverse and sufficient samples for multi-task. Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances. To address this challenge, this paper proposes a multi-task multi-objective evolutionary network (DMOEAD) for joint learning of HC and HP. A multi-task sufficiency-and-diversity sampling method is designed to unify the heterogeneity of sample construction between two types of tasks. Two types of task-specific networks are constructed to decompose high-frequency information. Further, a collaborative learning module is designed to dynamically learn complementary high-frequency information from another task in different layers. To be compatible with the optimization direction of two types of tasks, multi-task optimization is realized using a deep multi-objective evolutionary algorithm (DMEO). In the DMEO, the set of parameters of the DMOEAD is regarded as an individual. A deep mutation operator is designed and used for network optimization, which accelerates large-scale network parameter searching. The DMEO can coordinate the differences between multiple tasks and provide a set of Pareto network parameter solutions. Finally, the experimental results demonstrate that the proposed method can significantly enhance the performance of both pansharpening and classification tasks. [ABSTRACT FROM AUTHOR]

Developing efficient algorithms harnessing the power of current quantum processors has sparked the emergence of techniques that combine soft computing with quantum computing. This paper proposes two methods that effectively exploit quantum processors for electroencephalography-based motor imagery classification. The first method is a wrapper feature selection approach based on a quantum genetic algorithm, while the second approach employs a hybrid classical–quantum network comprising a baseline feature extraction network and a variational quantum circuit serving as the classifier. Our evaluation on the BCI Competition IV dataset 2b yielded competitive mean accuracies of 83.82%, 85.56%, and 73.73% for the subject-dependent cross-validation, subject-dependent hold-out validation, and subject-independent leaving one subject out classification approaches, respectively. Notably, our statistical analysis revealed that the hybrid models performed on par with the majority of state-of-the-art architectures, underscoring the practical viability of quantum-hybrid methodologies in real-world problem-solving scenarios. • Novel Quantum Genetic Algorithm designed for Feature Selection on NISQ devices. • Exploration of Quantum Algorithms for EEG-based Motor Imagery Classification. • Demonstration of Practical Feasibility of NISQ Algorithms for real-world problems. [ABSTRACT FROM AUTHOR]

The challenge of high-dimensional feature selection (FS) lies in the search technique, which needs to consider both minimizing the size of feature subset and maximizing the classification accuracy. Recently, multi-objective evolutionary algorithms (MOEAs) have shown excellent performance in solving FS tasks. However, most existing MOEAs struggle to effectively balance these two conflicting objectives when solving high-dimensional FS tasks. To cover this issue, in this paper, a decomposition-based multi-population evolutionary algorithm is proposed, called MPEA-FS. In the initialization stage, a multi-population generation strategy (MGS) based on feature weights (MGS) was adopted, with each population corresponding to a search subspace, which can improve the algorithm's search ability. During the evolutionary stage, an external population is designed to integrate the excellent feature subsets of multiple populations to achieve knowledge sharing between them. In addition, to reduce the feature dimension, a feature reduction strategy (FRS) is proposed, which can remove unimportant features while maintaining classification accuracy. Extensive experiments are performed on 13 high-dimensional datasets, and the proposed algorithm is compared with 5 state-of-the-art FS methods proposed in the last 3 years on multiple metrics. The experimental results indicate that MPEA-FS can achieve higher classification accuracy on most datasets, and the number of selected features is also competitive. • A decomposition-based multi-population feature selection algorithm is proposed. • The generation of multi-population is based on a novel feature weight method. • Multi-population knowledge sharing strategy based on elite population is proposed. • Evolution operator based on guide vector can correct the feature subset effectively. • Obtained higher classification accuracy and competitive number of features. [ABSTRACT FROM AUTHOR]

• A workflow scheduling framework is proposed based on MAPE-K control model in Fog. • An ANFIS neural network is utilized to predict future resource load. • A dynamic resource management is applied to minimize the energy consumption. • An I-ALO-GWO algorithm is designed for workflow scheduling. Fog computing paradigm provides diverse processing resources and storage close to the edge of Internet of Things (IoT) networks. Workflow scheduling is an open issue in fog computing as it addresses the deployment of IoT workflow tasks on fog cells. Deploying workflow tasks on fog cells in an inefficient manner can have negative consequences. It can lead to bandwidth waste, resource depletion, and significant operating expenses. Additionally, the majority of scheduling evolutionary algorithms suffer from premature convergence and sticking in local optima. In this paper, a predictive energy-aware scheduling framework is proposed as a MAPE-K control model consisting of the four Monitor, Analyzer, Planner and Executer components with a shared Knowledge base in fog computing. First, a prediction method applying an Adaptive Network-based Fuzzy Inference System (ANFIS) into an analyzer component is introduced to predict future resource load. Second, a resource management strategy based on predicted resource load is presented to reduce energy consumption. Third, the Improved Ant Lion Optimizer (ALO) and weighted Grey Wolf Optimizer (GWO) are combined into a planner component called I-ALO-GWO for workflow scheduling. In the end, the decisions made in the previous steps are executed on the fog cells. The effectiveness of the I-ALO-GWO evolutionary algorithm has been tested on the IEEE CEC2019 benchmark functions. In addition, the proposed approach is evaluated practically under several famous scientific workflows using the iFogSim tool. Experimental results indicate that I-ALO-GWO improves the makespan up to 18 %, the energy consumption up to 17 %, the total execution cost up to 11 % and 26 % in terms of efficiency in comparison with the second-best results. [ABSTRACT FROM AUTHOR]

This work introduces a new Parallel Island Model (PIM) that encompasses the benefits of heterogeneity and algorithmic reconfigurability. The former feature, heterogeneity, means that different islands may execute different evolutionary algorithms. The latter, reconfigurability, means that each island can change the algorithm being executed during the evolutionary process. Allowing such features increases the usual diversity obtained by the communication topologies and migration policies by homogeneous PIMs (HoPIMs). Previous (non-reconfigurable) heterogeneous PIMs (HePIMs) were able to provide competitive solutions regarding the HoPIMs. By adding the reconfiguration capability, PIMs can change dynamically from executing one evolutionary algorithm to another. In this manner, the required diversity and flexibility to outperform HoPIMs and HePIMs is achieved. This paper discusses policies to profit from the feature of reconfigurability on HePIM models and provides an innovative and successful stagnation-based reconfiguration policy. The benefits of the new reconfigurable model are verified using the unsigned reversal distance optimization problem as a case study. [ABSTRACT FROM AUTHOR]

Constrained multimodal multi-objective optimization (CMMOPs) involves multiple equivalent constrained Pareto optimal sets (CPSs) matching the same constrained Pareto front (CPF). An essential challenge in solving CMMOPs is how to balance exploration and exploitation in searching for the CPSs. To tackle this issue, a dynamic constrained co-evolutionary multimodal multi-objective algorithm termed DCMMEA is developed in this paper. DCMMEA involves a constraint-relaxed population for handling constraints and a convergence-relaxed population for improving convergence quality. Subsequently, a constraint-relaxed epsilon strategy that considers the constraint violation degree between individuals is designed and applied dynamically in the constraint-relaxed population to develop equivalent CPSs. Similarly, a dynamic convergence-relaxed epsilon strategy that considers the differences between objective values is developed and used dynamically in the convergence-relaxed population. It explores CPSs with high convergence quality and transfers the convergence knowledge to the constraint-relaxed population. Additionally, the constraint- relaxed population size is dynamically increased and the convergence-relaxed population size is dynamically decreased to balance the exploration and exploitation procedures. Experiments are performed on standard CMMOP test suites and validate that DCMMEA obtains superior performance on solving CMMOPs in comparison to state-of-the-art algorithms. Also, DCMMEA is implemented on standard CMOPs and demonstrated good performance in handling CMOPs. [ABSTRACT FROM AUTHOR]