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This paper proposes a novel hybrid metaheuristic algorithm called the remora crayfish optimization algorithm (HRCOA) designed for solving continuous optimization problems. The crayfish optimization algorithm (COA), recently proposed as a meta-heuristic algorithm (MA), exhibits certain limitations such as imbalanced exploration and exploitation capacities, susceptibility to premature optimization, and a propensity for stagnation. To address these shortcomings, we incorporate the exploitation operators from the remora optimization algorithm (ROA) to enhance the exploitative behaviors of COA. In addition, we simplify the summer resort operator in the original COA to streamline the search operator design, thus avoiding unnecessary complexity. Furthermore, numerical experiments on 10-dimensional (D) and 20-D CEC2022 benchmark functions, 50-D and 100-D CEC2020 benchmark functions, engineering optimization problems, and wireless sensor networks (WSNs) coverage optimization problems are conducted to investigate the performance of our proposed HRCOA comprehensively. We compare the proposed HRCOA against eight well-known state-of-the-art MAs, including CMAES and the original COA, as competitor algorithms. The experimental and statistical results confirm the effectiveness, competitiveness, and scalability of our proposal. Finally, we conclude that the proposed HRCOA possesses significant potential for addressing diverse optimization challenges in real-world scenarios. [ABSTRACT FROM AUTHOR]

The use of ensemble approaches in the single-objective evolutionary algorithms is ubiquitous, but ensembles of multiobjective evolutionary algorithms (MOEAs) have achieved relatively little attention. On the other hand, manually selecting a suitable MOEA to solve an actual multiobjective optimization problem (MOP) is time-consuming and challenging. Therefore, developing a multiobjective hyperheuristic to allocate computational resources for multiple MOEAs in an intelligent approach is beneficial. In this work, an autoselection strategy of MOEAs based on the performance indicator (MOEAS-PI) is introduced to alleviate the abovementioned problem. In the MOEAS-PI, the performance of each constituent MOEA in the pool is assessed according to a real-time and comprehensive performance indicator, which contains both the current and future performances. The MOEAS-PI is able to easily choose the best performing MOEA during the evolutionary process. Also, it can enhance the robustness of MOEAs and reduce the application risk. The effectiveness of the MOEAS-PI is carefully evaluated on 23 MOPs. Simulation results demonstrate that the MOEAS-PI is an effective and efficient method to integrate the advantages of each individual algorithm. Finally, the MOEAS-PI is utilized to solve a translation control problem of an immersed tunnel element under current flow. Experimental results reveal that the MOEAS-PI is a reliable and effective optimization approach to solve actual MOPs. Note to Practitioners—Multiobjective optimization problems (MOPs) have been commonly found in various fields. However, a single MOEA cannot guarantee its sufficient robustness and adaptability in solving MOPs. Therefore, this study aims to propose a multiobjective hyperheuristic algorithm to improve the robustness of MOEAs. The performance of the proposed algorithm is tested on benchmark test functions and an actual MOP. The results show that the proposed approach can select a suitable MOEA to solve a particular type of MOPs during the evolutionary process and provide a solution set for decision-makers to control the translation of an immersed tunnel element under different objectives/operator environments. [ABSTRACT FROM AUTHOR]

Finding a good Pareto front (PF) approximation and locating sufficient equivalent Pareto optimal solutions are two important goals of the multimodal multiobjective optimization (MMO). Preserving the diversity in decision and objective spaces is a core task in the MMO accordingly. Although various “soft isolation” approaches, such as niching methods, have been proposed to promote the diversity and find multiple Pareto optimal solutions in the decision space, they may perform poorly on complex MMO problems (MMOPs) due to high environmental selection pressure and complex geometry of Pareto optimal sets (PSs). To alleviate the above-mentioned challenging task, a “hard/physical isolation” method called zoning search (ZS) is proposed to maintain the diversity in the decision space and reduce the problem complexity in this article. In the ZS, some decision variables of MMOPs are selected randomly and then divided into several segments, i.e., the entire search space is partitioned into many subspaces. Clearly, the population diversity can be naturally maintained in the decision space and the problem complexity is reduced by the ZS in each subspace. The effectiveness of the ZS is systematically evaluated by 11 recently proposed MMOPs. The experimental results demonstrate that the ZS can effectively assist a selected multimodal multiobjective evolutionary algorithm (MMOEA) in finding more and better distributed equivalent Pareto optimal solutions in the decision space, and keep its performance in the objective space unchanged. Additionally, if additional computational resources are given, the ZS can further help the selected MMOEA to improve its performance in the decision space when compared with a soft isolation method used in the corresponding MMOEA. Overall, the ZS is a simple and promising approach to balance the broad search and the deep search in solving MMOPs. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has become one of the most popular paradigms of evolutionary algorithms. Over the past two decades, some prior knowledge has been obtained in the DE research community. It is an interesting topic to enhance the performance of DE by taking advantage of such prior knowledge. Along this line, a prior knowledge guided DE (called PKDE) is proposed in this paper. In PKDE, we extract two levels of prior knowledge, i.e., the macro- and micro-levels. In order to integrate these two levels of prior knowledge in an effective way, the control parameters of PKDE are tuned based on two distributions (i.e., Cauchy and normal distribution), with the aim of alleviating the premature convergence at the early stage and speeding up the convergence toward the global optimal solution at the later stage. In addition, the self-adaptive mutation strategy is implemented based on our previous study. PKDE is compared with eight DE variants and seven non-DE algorithms on two sets of benchmark test functions from IEEE CEC2005 and IEEE CEC2014. Systematic experiments demonstrate that the overall performance of PKDE is very competitive. [ABSTRACT FROM AUTHOR]

The performance of differential evolution (DE) is significantly influenced by the choice of crossover strategies; therefore, a self-adaptive differential evolution algorithm with crossover strategies adaptation (CSA-SADE) is proposed in this paper to enhance the performance of DE. In CSA-SADE, the suitable control parameters, mutation strategies, and crossover strategies can be achieved in different evolution stages. To demonstrate the effectiveness of CSA-SADE, the proposed algorithm is compared with eight state-of-the-art evolutionary algorithms. The simulation results indicate that CSA-SADE outperforms five improved DE algorithms and three non-DE approaches on a set of 25 CEC2005 benchmark functions. Additionally, the proposed algorithm is employed to estimate the kinetic parameters of mercury oxidation; the results show that CSA-SADE performs better than the compared algorithms in this simulation example. [ABSTRACT FROM AUTHOR]

Considering that the model of the $$p$$ -Xylene (PX) oxidation reaction process is a hybrid and highly nonlinear model, a differential evolution algorithm with self-adaptive mutation strategy and control parameters (SSCPDE) was proposed to optimize the operating conditions. In SSCPDE, each individual has its own control parameters and mutation strategies that can be self-adaptively adjusted to different evolution phases and various optimization problems. SSCPDE was compared with 6 state-of-the-art DE variants by 38 different types of benchmark functions. Simulation results show that the average performance of SSCPDE is better than the six famous self-adaptive DE algorithms. Finally, the SSCPDE algorithm was used to optimize the five main operating conditions of the PX oxidation reaction process. Optimization results indicate that the production cost, loss of acetic acid and PX combustion of the PX oxidation reaction process are greatly reduced and that SSCPDE performs better than JADE, EPSDE, SaDE, and the optimizer of Aspen Plus and similar to jDE and CoDE. [ABSTRACT FROM AUTHOR]

Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors. [ABSTRACT FROM AUTHOR]

To predict pedestrian movement is of vital importance in a wide range of applications. Recently, data-driven models are receiving increasing attention in pedestrian dynamics studies, demonstrating a great potential in enhancing simulation performance. This paper presents a pedestrian movement simulation model based on the artificial neural network, in which two submodels are, respectively, used to predict velocity displacement and velocity direction angle at each time step. Destination information, the pedestrian's historical movement information, neighboring pedestrians, and environmental obstacles within a semicircular-shaped perception area are used as inputs to learn pedestrian movement behavioral rules. In the velocity direction angle submodel, a novel division method on pedestrian's perception area is adopted. Specifically, perception radius is divided into several bands, and perception angle range is divided into a number of sectors, establishing a weighted spatial matrix to represent varied influences of neighboring pedestrians and obstacles. Experiments on two typical scenarios, the unidirectional flow and bidirectional flow in a long straight corridor, were conducted to obtain pedestrian movement datasets. Then, a series of simulation cases were conducted to investigate the proper values for critical parameters, including perception radius, perception angle division, weights of the spatial matrix, and historical movement adoption. In comparison of pedestrian trajectory between simulation results and real data, the mean trajectory error (MTE) and mean destination error (MDE) are, respectively, 0.114 m and 0.171 m in the unidirectional flow scenario, which are, respectively, 0.204 m and 0.362 m in the bidirectional flow scenario. In addition, the fundamental diagram representing density-velocity and density-flow relationships in simulation results agree well with that in real data. The results demonstrate great capacity and credibility of the presented model in simulating pedestrian movement in real applications. [ABSTRACT FROM AUTHOR]

An offline and online bi-level structure-based dynamic path planning algorithm is proposed for an unmanned aerial vehicle (UAV) in low-altitude complex urban environment. First, an improved Hunger Games Search (HGS) algorithm is developed to generate an offline optimized path under the UAV's performance constraints and the known static obstacles' constraints. The individuals of the proposed algorithm will be divided into multiple groups to increase the population diversity. And then, a dynamic grouping strategy and a quantum-behaved behavior are proposed to solve the premature convergence's problem and the imbalance problem between exploration and exploitation ability in HGS. To improve the dynamic obstacle avoidance efficiency of the algorithm, the dynamic obstacles are classified into three categories: newly added no-fly zone, known and unknown dynamic obstacles. Then, utilizing the information of the offline optimized path and the airborne sensors, three kinds of online planning strategies—an improved rapid-exploring random tree (RRT), a changing speed strategy, and a novel three-dimensional rolling windows—are introduced to dynamically update the path or speed of the UAV. Simulation results indicated that the improved HGS can enhance the performances of the traditional HGS and outperform other compared algorithms on the benchmark functions. Meanwhile, the online planning strategies can effectively achieve dynamic obstacle avoidance within the constraints of offline path. More specially, the planning time and angles of the local path to avoid the no-fly-zone's influence are improved by 11.3% and 56.8% through utilizing the improved RRT. [ABSTRACT FROM AUTHOR]

This paper is concerned with the security problem for a networked control system subject to deception attacks. An adversary is present to corrupt both the sensor-to-controller channel and the controller-to-actuator channel with deception signals when sensor and control data are transmitted via the network medium. We model the malicious deception signals as unknown-but-bounded signals in such a way that no priori knowledge of the attack signals is required. First, a delicate set-membership performance index is proposed for meeting the system security requirement. Second, a novel set-membership estimation method is developed to obtain the desired ellipsoidal estimation set which guarantees the enclosing of true state of the system at every instant of time. Such an ellipsoidal estimation set is then used for deriving the sufficient conditions such that the resulting closed-loop system is set-membership input-to-state-stable and achieves the prescribed security requirement. Finally, a target tracking system is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network. [ABSTRACT FROM AUTHOR]

For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the objective space and maintain the population diversity in the decision space. To achieve the above objectives, this article proposes a zoning search-based multimodal multi-objective brain storm optimization algorithm (ZS-MMBSO). At first, the search space segmentation method is employed to divide the search space into some sub-regions. Moreover, a novel individual generation strategy is incorporated into the multimodal multi-objective brain storm optimization algorithm, which can improve the search performance of the search engineering. The proposed algorithm is compared with five famous multimodal multi-objective evolutionary algorithms (MMOEAs) on IEEE CEC2019 MMOPs benchmark test suite. Experimental results indicate that the overall performance of the ZS-MMBSO is the best among all competitors. [ABSTRACT FROM AUTHOR]

For the traveling salesman problem (TSP), it is usually hard to find a high-quality solution in polynomial time. In the last two years, graph neural networks emerge as a promising technique for TSP. However, most related learning-based methods do not make full use of the hierarchical features; thereby, resulting in relatively-low performance. Furthermore, the decoder in those methods only generates single permutation and needs additional search strategies to improve the permutation, which leads to more computing time. In this work, we propose a novel graph convolutional encoder and multi-head attention decoder network (GCE-MAD Net) to fix the two drawbacks. The graph convolutional encoder realizes to aggregate neighborhood information through updated edge features and extract hierarchical graph features from all graph convolutional layers. The multi-head attention decoder takes the first and last selected node embeddings and fused graph embeddings as input to generate probability distributions of selecting next unvisited node in order to consider global features. The GCE-MAD Net further allows to choose several nodes at each time step and generate a permutations pool after decoding to increase diversity of solution space. To assess the performance of GCE-MAD Net, we conduct experiments with randomly generated instances. The simulation results show the proposed GCE-MAD Net outperforms the traditional heuristics methods and existing learning-based algorithms on all evaluation metrics. Especially, when encountering large scale problem instances, the small scale pretrained GCE-MAD Net can get much better solutions than CPLEX solver with less time. [ABSTRACT FROM AUTHOR]

The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton’s second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses. [ABSTRACT FROM AUTHOR]

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints. [ABSTRACT FROM AUTHOR]

Stroke is a leading cause of mortality and disability globally. Cerebral ischaemia-reperfusion (I/R) injury is characterized by significant inflammation and extensive cell death. Multiple signaling pathways play essential roles in the process, and identifying the unclear crucial regulators of these pathways may provide promising targets for treatment. CASP8 and FADD-like apoptosis regulator (CFLAR) is expressed in multiple organs to regulate inflammation. Here, we reported that CFLAR expression was markedly reduced in brain samples of mice with middle cerebral artery occlusion (MCAO) stroke. Furthermore, CFLAR knockdown markedly elevated the neurological deficit, brain water content and the infarct volume. In addition, significantly promoted inflammation and endoplasmic reticulum (ER) stress was detected in brain tissues of mice after MCAO, as evidenced by the promoted expression of p-IκBα, p-nuclear factor (NF)-κB (p65), glucose-regulated protein 78 (GRP78), PKR-like ER kinase (PERK), activating transcription factor-6 (ATF-6) and cleaved Caspase-12. Notably, MCAO-induced cerebral I/R injury was markedly alleviated in mice over-expressing CFLAR through suppressing inflammation and ER stress. Furthermore, our in vitro results indicated that oxygen-glucose deprivation (OGD)-induced cell death was evidently ameliorated by CFLAR over-expression. In contrast, the cell death triggered by OGD was accelerated by CFLAR knockdown in vitro through enhancing Caspase-3 cleavage, and this effect was obviously ameliorated by the blockage of ER stress using 4-phenyl butyric acid (4-PBA). Collectively, these results demonstrated that CFLAR could be considered as a novel candidate to develop effective therapeutic treatment against cerebral I/R injury. [ABSTRACT FROM AUTHOR]