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This editorial presents a summary of the Special Issue on Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces, presented in the "Computer Science & Engineering" section of I Electronics i (ISSN 2079-9292). The application of artificial intelligence (AI) in distributed environments has become a research area of high-added value and economic potential. The Present Issue This Special Issue consists of seven papers that address pivotal topics in the field of AI solutions for distributed systems and their applications. [Extracted from the article]

The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: "Not all marriages have happy endings". [ABSTRACT FROM AUTHOR]

The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population's capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm's performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms. [ABSTRACT FROM AUTHOR]

Network protocol implementations, as integral components of information communication, are critically important for security. Due to its efficiency and automation, fuzzing has become a popular method for protocol security detection. However, the existing protocol-fuzzing techniques face the critical problem of generating high-quality inputs. To address the problem, in this paper, we propose MSFuzz, which is a protocol-fuzzing method with message syntax comprehension. The core observation of MSFuzz is that the source code of protocol implementations contains detailed and comprehensive knowledge of the message syntax. Specifically, we leveraged the code-understanding capabilities of large language models to extract the message syntax from the source code and construct message syntax trees. Then, using these syntax trees, we expanded the initial seed corpus and designed a novel syntax-aware mutation strategy to guide the fuzzing. To evaluate the performance of MSFuzz, we compared it with the state-of-the-art (SOTA) protocol fuzzers, namely, AFLNET and CHATAFL. Experimental results showed that compared with AFLNET and CHATAFL, MSFuzz achieved average improvements of 22.53% and 10.04% in the number of states, 60.62% and 19.52% improvements in the number of state transitions, and 29.30% and 23.13% improvements in branch coverage. Additionally, MSFuzz discovered more vulnerabilities than the SOTA fuzzers. [ABSTRACT FROM AUTHOR]

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain. [ABSTRACT FROM AUTHOR]

WebAssembly code is designed to run in a sandboxed environment, such as a web browser, providing a high level of security and isolation from the underlying operating system and hardware. This enables the execution of untrusted code in a web browser without compromising the security and integrity of the user's system. This paper discusses the challenges associated with using fuzzing tools to identify vulnerabilities or bugs in WebAssembly interpreters. Our approach, known as ESFuzzer, introduces an efficient method for fuzzing WebAssembly interpreters using an Equivalent-Statement concept and the Stack Repair Algorithm. The samples generated by our approach successfully passed code validation. In addition, we developed effective mutation strategies to enhance the efficacy of our approach. ESFuzzer has demonstrated its ability to generate code that achieves 100% WebAssembly validation testing and achieves code coverage that is more than twice that of libFuzzer. Furthermore, the 24-h experiment results show that ESFuzzer performs ten times more efficiently than libFuzzer. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAV) employ vehicle-to-vehicle communications to safely drive in a platoon with short inter-vehicle distances, which can improve traffic throughput and reduce fuel consumption. With the development of wireless communication technology, more and more information can be used for vehicle controllers. However, is more information better? In this paper, a fuel economy-based performance evaluation index is established for evaluating the efficiency of CAVs driving in a platoon with different communication topologies. Four typical communication topologies that describe the CAV with different amounts of information are studied by the linear controller. The differential evolution algorithm is used to solve the parameters. Due to the increase in information, more control parameters need to be computed, and it is hard to find an optimal solution. So, the simulation results show that CAV with more information did not obtain a better fuel economy. [ABSTRACT FROM AUTHOR]

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively. [ABSTRACT FROM AUTHOR]

The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative spaceborne isoflux scanning digital phased array (ISDPA) design with two-stage digital beamforming (DBF) for geostationary satellites. To achieve isoflux scanning, a novel technique is presented to obtain an isoflux beam for the ISDPA equivalent element using a DBF completely shared subarray architecture and the differential evolution (DE) algorithm. By reutilizing the radiating elements of adjacent subarrays, the radiation aperture and element number are augmented, enhancing the degrees of optimization freedom. To validate the proposed design, a linear ISDPA with 16 DBF completely shared subarrays is optimized and analyzed using two sets of excitation coefficients in different DBF stages. The numerical results demonstrate that the proposed ISDPA can adaptively compensate for space loss variations during beam scanning for geostationary communications with low sidelobes better than −20 dB. [ABSTRACT FROM AUTHOR]

Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks. [ABSTRACT FROM AUTHOR]

To address the current difficulties and problems of short-term load forecasting (STLF), this paper proposes a combined forecasting method based on the improved sparrow search algorithm (ISSA), with fused Cauchy mutation and opposition-based learning (OBL), to optimize the hyperparameters of the long- and short-term-memory (LSTM) network. For the sparrow-search algorithm (SSA), a Sin-chaotic-initialization population, with an infinite number of mapping folds, is first used to lay the foundation for global search. Secondly, the previous-generation global-optimal solution is introduced in the discoverer-location update way, to improve the adequacy of the global search, while adaptive weights are added to reconcile the ability of the local exploitation and global search of the algorithm as well as to hasten the speed of convergence. Then, fusing the Cauchy mutation arithmetic and the OBL strategy, a perturbation mutation is performed at the optimal solution position to generate a new solution, which, in turn, strengthens the ability of the algorithm to get rid of the local space. After that, the ISSA-LSTM forecasting model is constructed, and the example is verified based on the power load data of a region, while the experimental comparison with various algorithms is conducted, and the results confirm the superiority of the ISSA-LSTM model. [ABSTRACT FROM AUTHOR]

Cryptosystems based on supersingular isogeny are a novel tool in post-quantum cryptography. One compelling characteristic is their concise keys and ciphertexts. However, the performance of supersingular isogeny computation is currently worse than that of other schemes. This is primarily due to the following factors. Firstly, the underlying field is a quadratic extension of the finite field, resulting in higher computational complexity. Secondly, the strategy for large-degree isogeny evaluation is complex and dependent on the elementary arithmetic units employed. Thirdly, adapting the same hardware to different parameters is challenging. Considering the evolution of similar curve-based cryptosystems, we believe proper algorithm optimization and hardware acceleration will reduce its speed overhead. This paper describes a high-performance and flexible hardware architecture that accelerates isogeny computation. Specifically, we optimize the design by creating a dedicated quadratic Montgomery multiplier and an efficient scheduling strategy that are suitable for supersingular isogeny. The multiplier operates on F p 2 under projective coordinate formulas, and the scheduling is tailored to it. By exploiting additional parallelism through replicated multipliers and concurrent isogeny subroutines, our 65 nm SMIC technology cryptographic accelerator can generate ephemeral public keys in 2.40 ms for Alice and 2.79 ms for Bob with a 751-bit prime setting. Sharing the secret key costs another 2.04 ms and 2.35 ms, respectively. [ABSTRACT FROM AUTHOR]

Talent resources are a primary resource and an important driving force for economic and social development. At present, researchers have conducted studies on talent introduction, but there is a paucity of research work on the stability of talent introduction. This paper presents the first study on talent stability in higher education, aiming to design an intelligent prediction model for talent stability in higher education using a kernel extreme learning machine (KELM) and proposing a differential evolution crisscross whale optimization algorithm (DECCWOA) for optimizing the model parameters. By introducing the crossover operator, the exchange of information regarding individuals is facilitated and the problem of dimensional lag is improved. Differential evolution operation is performed in a certain period of time to perturb the population by using the differences in individuals to ensure the diversity of the population. Furthermore, 35 benchmark functions of 23 baseline functions and CEC2014 were selected for comparison experiments in order to demonstrate the optimization performance of the DECCWOA. It is shown that the DECCWOA can achieve high accuracy and fast convergence in solving both unimodal and multimodal functions. In addition, the DECCWOA is combined with KELM and feature selection (DECCWOA-KELM-FS) to achieve efficient talent stability intelligence prediction for universities or colleges in Wenzhou. The results show that the performance of the proposed model outperforms other comparative algorithms. This study proposes a DECCWOA optimizer and constructs an intelligent prediction of talent stability system. The designed system can be used as a reliable method of predicting talent mobility in higher education. [ABSTRACT FROM AUTHOR]

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks. [ABSTRACT FROM AUTHOR]

Digital platforms have begun to rely more on algorithms to perform basic tasks such as pricing. These platforms must set prices that coordinate two or more sides that need each other in some way (e.g., developers and users or buyers and sellers). Therefore, it is essential to form correct expectations about how both sides behave. The purpose of this paper was to study the effect of different levels of information on two biology-inspired metaheuristics (differential evolution and particle swarm optimization algorithms) that were programmed to set prices on multisided platforms. We assumed that one platform always formed correct expectations (human platform) while the competitor always used a generic version of particle swarm optimization or differential evolution algorithms. We tested different levels of information that modified how expectations were formed. We found that both algorithms might end up in suboptimal solutions, showing that algorithms needed to account for expectation formation explicitly or risk setting nonoptimal prices. In addition, we found regularity in the way algorithms set prices when they formed incorrect expectations that can help practitioners detect cases in need of intervention. [ABSTRACT FROM AUTHOR]

Federated learning is a hot area of concern in the field of privacy protection. There are local model parameters that are difficult to integrate, poor model timeliness, and local model training security issues. This paper proposes a blockchain-based differential optimization federated incremental learning algorithm, First, we apply differential privacy to the weighted random forest and optimize the parameters in the weighted forest to reduce the impact of adding differential privacy on the accuracy of the local model. Using different ensemble algorithms to integrate the local model parameters can improve the accuracy of the global model. At the same time, the risk of a data leakage caused by gradient update is reduced; then, incremental learning is applied to the framework of federated learning to improve the timeliness of the model; finally, the model parameters in the model training phase are uploaded to the blockchain and synchronized quickly, which reduces the cost of data storage and model parameter transmission. The experimental results show that the accuracy of the stacking ensemble model in each period is above 83.5% and the variance is lower than 10 − 4 for training on the public data set. The accuracy of the model has been improved, and the security and privacy of the model have been improved. [ABSTRACT FROM AUTHOR]

To address the poor searchability, population diversity, and slow convergence speed of the differential evolution (DE) algorithm in solving capacitated vehicle routing problems (CVRP), a new multistrategy-based differential evolution algorithm with the saving mileage algorithm, sequential encoding, and gravitational search algorithm, namely SEGDE, is proposed to solve CVRP in this paper. Firstly, an optimization model of CVRP with the shortest total vehicle routing is established. Then, the saving mileage algorithm is employed to initialize the population of the DE to improve the initial solution quality and the search efficiency. The sequential encoding approach is used to adjust the differential mutation strategy to legalize the current solution and ensure its effectiveness. Finally, the gravitational search algorithm is applied to calculate the gravitational relationship between points to effectively adjust the evolutionary search direction and further improve the search efficiency. Four CVRPs are selected to verify the effectiveness of the proposed SEGDE algorithm. The experimental results show that the proposed SEGDE algorithm can effectively solve the CVRPs and obtain the ideal vehicle routing. It adopts better search speed, global optimization ability, routing length, and stability. [ABSTRACT FROM AUTHOR]

In [[9]], the authors present a mobile sensor node for monitoring air and noise pollution, and the developed system was installed on an remote control drone, which could quickly monitor large areas. The system is composed of two micro-electromechanical systems (MEMSs) which evaluate the physiological parameters and eliminate the effects of external interference on the entire system. To improve the sound source identification performance in low-SNR cabin environments, the authors of [[5]] introduce cross-spectral matrix (CSM) reconstruction methods such as diagonal reconstruction (DRec), robust principal component analysis (RPCA), and probabilistic factor analysis (PFA), which have been widely studied in planar arrays, into spherical arrays-based functional delay and sum (FDAS). Firstly, empirical mode decomposition (EMD) and VMD methods were used to denoise simple signals, and the denoising effect of the VMD method was verified. [Extracted from the article]

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE's average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin. [ABSTRACT FROM AUTHOR]

As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers. [ABSTRACT FROM AUTHOR]

The iterative Fourier transform algorithm (IFTA) is widely used in various optical communication applications based on liquid crystal on silicon spatial light modulators. However, the traditional iterative method has many disadvantages, such as a poor effect, an inability to select an optimization direction, and the failure to consider zero padding or phase quantization. Moreover, after years of development, the emergence of various variant algorithms also makes it difficult for researchers to choose one. In this paper, a new intelligent hybrid algorithm that combines the IFTA and differential evolution algorithm is proposed in a novel way. The reliability of the proposed algorithm is verified by beam splitting, and the IFTA and symmetrical IFTA algorithms, for comparison, are introduced. The hybrid algorithm improves the defects above while considering the zero padding and phase quantization of a computer-generated hologram, which optimizes the directional optimization in the diffraction efficiency and the fidelity of the output beam and improves the results of these two algorithms. As a result, the engineers' trouble in the selection of an algorithm has also been reduced. [ABSTRACT FROM AUTHOR]

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches' operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration. [ABSTRACT FROM AUTHOR]

Feature selection plays a crucial role in establishing an effective speech emotion recognition system. To improve recognition accuracy, people always extract as many features as possible from speech signals. However, this may reduce efficiency. We propose a hybrid filter–wrapper feature selection based on a genetic algorithm specifically designed for high-dimensional (HGA) speech emotion recognition. The algorithm first utilizes Fisher Score and information gain to comprehensively rank acoustic features, and then these features are assigned probabilities for inclusion in subsequent operations according to their ranking. HGA improves population diversity and local search ability by modifying the initial population generation method of genetic algorithm (GA) and introducing adaptive crossover and a new mutation strategy. The proposed algorithm clearly reduces the number of selected features in four common English speech emotion datasets. It is confirmed by K-nearest neighbor and random forest classifiers that it is superior to state-of-the-art algorithms in accuracy, precision, recall, and F1-Score. [ABSTRACT FROM AUTHOR]

Safety is one concern that hinders the acceptance of ridesharing in the general public. Several studies have been conducted on the trust issue in recent years to relieve this concern. The introduction of trust in ridesharing systems provides a pragmatic approach to solving this problem. In this study, we will develop a trust-aware ridesharing recommender system decision model to generate recommendations for drivers and passengers. The requirements of trust for both sides, drivers and passengers, are taken into consideration in the decision model proposed in this paper. The decision model considers the factors in typical ridesharing systems, including vehicle capacities, timing, location and trust requirements, etc. The decision model aims to determine the shared rides that minimize cost while respecting the trust and relevant constraints. As the decision problem is a nonlinear integer programming problem, we combine a self-adaptive neighborhood search with Differential Evolution to develop an algorithm to solve it. To assess the effectiveness of the proposed algorithm, several other evolutionary computation approaches are also applied to solve the same problem. The effectiveness assessment is done based on the performance of applying different algorithms to find solutions for test cases, to provide a guideline for selecting a proper solution approach. [ABSTRACT FROM AUTHOR]

In this paper, three different deployment antenna arrays with circular, triangular and rectangular shapes were used to optimize the simultaneous wireless information and power transfer (SWIPT) system for the Internet of Things (IoT). Ray-tracing was employed to channel the model for a real environment. Self-adaptive dynamic differential evolution (SADDE) was used to optimize the harvesting power ratio with bit error rate constrained by the two different resolutions of feed length (high resolution and low resolution). Numerical results show that those three antenna arrays can achieve the goal for information quality in both resolutions. The harvesting power ratio for the circular array is the best and the harvesting power ratio for the rectangular array is the worst. The harvesting power ratio for the low-resolution case is 25% lower than the high-resolution case. However, the circular antenna array is the best deployment in those three different arrays for both high and low resolutions. [ABSTRACT FROM AUTHOR]

Multi-task optimization (MTO) is related to the problem of simultaneous optimization of multiple optimization problems, for the purpose of solving these problems better in terms of optimization accuracy or time cost. To handle MTO problems, there emerges many evolutionary MTO (EMTO) algorithms, which possess distinguished strategies or frameworks in the aspect of handling the knowledge transfer between different optimization problems (tasks). In this paper, we explore the possibility of developing a more efficient EMTO solver based on differential evolution by introducing the strategies of a self-adaptive multi-task particle swarm optimization (SaMTPSO) algorithm, and by developing a new knowledge incorporation strategy. Then, we try to apply the proposed algorithm to solve the weapon–target assignment problem, which has never been explored in the field of EMTO before. Experiments were conducted on a popular MTO test benchmark and a WTA-MTO test set. Experimental results show that knowledge transfer in the proposed algorithm is effective and efficient, and EMTO is promising in solving WTA problems. [ABSTRACT FROM AUTHOR]

This paper used an artificial jellyfish search (AJFS) optimizer suitable for selective harmonic elimination-based modulation for multilevel inverter (MLI) voltage control application. The main objective was to remove the undesired lower-order harmonics in the output voltage waveform of an MLI. This algorithm was motivated by the behavior of jellyfish in the ocean. Jellyfish have the ability to find the global best position where a large quantity of nutritious food is available. The paper applied AJFS algorithm on five, seven, and nine levels of CHB-MLI. The optimum switching angle was calculated for the entire modulation range for the desired lower-order harmonics elimination. The problem formulated to achieve the objective was solved in a MATLAB environment. The total harmonic distortion (THD) values of five-, seven-, and nine-level inverters for various modulation indexes were computed using AJFS and compared with the powerful differential evolution (DE) algorithm. The comparison of THD results clearly demonstrated superior THD in the output of CHB-MLI of the AJFS algorithm over DE and GA algorithm for low and medium values of modulation index. The experimental results further validated the better performance of the AJFS algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid differential evolution and weight total least squares method (HDE-WTLSM) is proposed for antenna array pattern synthesis. A variable diagonal weight matrix is introduced in total least squares method. Then, the weight matrix is optimized by differential evolution (DE) algorithm to control the differences of the desired level and the obtained level in different directions. This algorithm combines the advantages of evolutionary algorithm and numerical algorithm, so it has a wider application range and faster convergence speed. To compare HDE-WTLSM with DE algorithm and typical numerical algorithms, these methods are applied to a linear antenna array and a conformal truncated conical array. Using our method, lower sidelobe levels and deeper nulls are obtained. The simulation results verify the validity and efficiently of HDE-WTLSM. [ABSTRACT FROM AUTHOR]

This paper studies the design scalability of a Γ -shaped piezoelectric energy harvester (Γ EH) using the generalized classical Ritz method (GCRM) and differential evolution algorithm. The generalized classical Ritz method (GCRM) is the advanced version of the classical Ritz method (CRM) that can handle a multibody system by assembling its equations of motion interconnected by the constraint equations. In this study, the GCRM is extended for analysis of the piezoelectric energy harvesters with material and/or orientation discontinuity between members. The electromechanical equations of motion are derived for the PE harvester using GCRM, and the accuracy of the numerical simulation is experimentally validated by comparing frequency response functions for voltage and power output. Then the GCRM is used in the power maximization design study that considers four different total masses—15 g, 30 g, 45 g, 60 g—to understand design scalability. The optimized Γ EH has the maximum normalized power density of 23.1 × 103 kg·s·m−3 which is the highest among the reviewed PE harvesters. We discuss how the design parameters need to be determined at different harvester scales. [ABSTRACT FROM AUTHOR]

The secondary users (SUs) in cognitive radio networks (CRNs) can obtain reliable spectrum sensing information of the primary user (PU) channel using cooperative spectrum sensing (CSS). Multiple SUs share their sensing observations in the CSS system to tackle fading and shadowing conditions. The presence of malicious users (MUs) may pose threats to the performance of CSS due to the reporting of falsified sensing data to the fusion center (FC). Different categories of MUs, such as always yes, always no, always opposite, and random opposite, are widely investigated by researchers. To this end, this paper proposes a hybrid boosted tree algorithm (HBTA)-based solution that combines the differential evolution (DE) and boosted tree algorithm (BTA) to mitigate the effects of MUs in the CSS systems, leading to reliable sensing results. An optimized threshold and coefficient vector, determined against the SUs employing DE, is utilized to train the BTA. The BTA is a robust ensembling machine learning (ML) technique gaining attention in spectrum sensing operations. To show the effectiveness of the proposed scheme, extensive simulations are performed at different levels of signal-to-noise-ratios (SNRs) and with different sensing samples, iteration levels, and population sizes. The simulation results show that more reliable spectrum decisions can be achieved compared to the individual utilization of DE and BTA schemes. Furthermore, the obtained results show the minimum sensing error to be exhibited by the proposed HBTA employing a DE-based solution to train the BTA. Additionally, the proposed scheme is compared with several other CSS schemes such as simple DE, simple BTA, maximum gain combination (MGC), particle swarm optimization (PSO), genetic algorithm (GA), and K-nearest neighbor (KNN) algorithm-based soft decision fusion (SDF) schemes to validate its effectiveness. [ABSTRACT FROM AUTHOR]

Since permanent magnet direct current (DC) motors are mainly used in various industrial automation applications, the demand for electric motors is increasing rapidly. However, in the mass production of electric motors, often, only random inspections are used to check the specifications and performance of electric motors. For manufacturing or engineering application staff to have a more thorough understanding of the characteristics of the motor, it is necessary to conduct a full or quick inspection during the production process to ensure the quality of the electric motor. Based on this, this literature review reveals several methods and algorithms often used to estimate DC motor parameters, given the importance of knowing the parameters of the DC motor and the lack of research on estimating the parameters of the DC motor. [ABSTRACT FROM AUTHOR]

Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver's respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver's fatigue level detection than the traditional ELM and SVM. [ABSTRACT FROM AUTHOR]

Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method in digital watermarking. This paper proposes a novel blind image watermarking scheme developed in the spatial domain by quantization of invariant direct current (DC) coefficients. The cover image is redistributed and divided into non-overlapped square blocks and then the DC coefficients invariant to rotation, row and column flip operations, without utilization of the DCT transform, are directly calculated in the spatial domain. Utilizing the quantization parameter and watermark information, the modified DC coefficients and the difference between DC and modified DC coefficients are calculated to directly modify the pixel values to embed watermark bits in the spatial domain instead of the DCT domain. Optimal values of the quantization parameter, which plays a significant role in controlling the tradeoff between robustness and invisibility, are calculated through differential evolution (DE), the optimization algorithm. Experimental results, compared with the latest similar schemes, demonstrate the advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms. [ABSTRACT FROM AUTHOR]

Global optimization challenges are frequent in scientific and engineering areas where loads of evolutionary computation methods i.e., differential evolution (DE) and particle-swarm optimization (PSO) are employed to handle these problems. However, the performance of these algorithms declines due to expansion in the problem dimension. The evolutionary algorithms are obstructed to congregate with the Pareto front rapidly while using the large-scale optimization algorithm. This work intends a large-scale multi-objective evolutionary optimization scheme aided by the determinantal point process (LSMOEA-DPPs) to handle this problem. The proposed DPP model introduces a mechanism consisting of a kernel matrix and a probability model to achieve convergence and population variety in high dimensional relationship balance to keep the population diverse. We have also employed elitist non-dominated sorting for environmental selection. Moreover, the projected algorithm also demonstrates and distinguishes four cutting-edge algorithms, each with two and three objectives, respectively, and up to 2500 decision variables. The experimental results show that LSMOEA-DPPs outperform four cutting-edge multi-objective evolutionary algorithms by a large margin. [ABSTRACT FROM AUTHOR]

The world is once again facing massive energy- and environmental challenges, caused by global warming. This time, the situation is complicated by the increase in energy demand after the pandemic years, and the dramatic lack of basic energy supply. The purely "green" energy is still not ready to substitute the fossil energy, but this year the fossil supplies are heavily questioned. Consequently, engineering must take flexible, adaptive, unexpected directions. For example, even the natural gas power plants are currently considered "green" by the European Union Taxonomy, joining the "green" hydrogen. Through a tight integration of highly intermittent renewable, or other distributed energy resources, the microgrid is the technology of choice to guarantee the expected impacts, making clean energy affordable. The focus of this work lies in the techno-economic optimization analysis of Combined Heat and Power (CHP) Multi-Micro Grids (MMG), a novel distribution system architecture comprising two interconnected hybrid microgrids. High computational resources are needed to investigate the CHP-MMG. To this aim, a novel nature-inspired two-layer optimization-simulation algorithm is discussed. The proposed algorithm is used to execute a techno-economic analysis and find the best settings while the energy balance is achieved at minimum operational costs and highest revenues. At a lower level, inside the algorithm, a Sequential Least Squares Programming (SLSQP) method ensures that the stochastic generation and consumption of energy deriving from CHP-MMG trial settings are balanced at each time-step. At the upper level, a novel multi-objective self-adaptive evolutionary algorithm is discussed. This upper level is searching for the best design, sizing, siting, and setting, which guarantees the highest internal rate of return (IRR) and the lowest Levelized Cost of Energy (LCOE). The Artificial Immune Evolutionary (AIE) algorithm imitates how the immune system fights harmful viruses that enter the body. The optimization method is used for sensitivity analysis of hydrogen costs in off-grid and on-grid highly perturbed contexts. It has been observed that the best CHP-MMG settings are those that promote a tight thermal and electrical energy balance between interconnected microgrids. The results demonstrate that such mechanism of energy swarm can keep the LCOE lower than 15 c€/kWh and IRR of over 55%. [ABSTRACT FROM AUTHOR]

This paper explores a set of learned convolutional kernels which we call Jet Features. Jet Features are efficient to compute in software, easy to implement in hardware and perform well on visual inspection tasks. Because Jet Features can be learned, they can be used in machine learning algorithms. Using Jet Features, we make significant improvements on our previous work, the Evolution Constructed Features (ECO Features) algorithm. Not only do we gain a 3.7× speedup in software without loosing any accuracy on the CIFAR-10 and MNIST datasets, but Jet Features also allow us to implement the algorithm in an FPGA using only a fraction of its resources. We hope to apply the benefits of Jet Features to Convolutional Neural Networks in the future. [ABSTRACT FROM AUTHOR]

From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions' set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, interest in DC networks and converters has increased due to the high number of applications in renewable energy systems. Consequently, the importance of improving the efficiency of the hybrid converters has been highlighted. An optimal control strategy is a significant solution to handle the challenges of controlling the hybrid interleaved boost–Cuk converter. In this article, a modern-optimization-methods-based PWM strategy for a hybrid power converter is developed. In order to improve the performance of the hybrid converter, four modern optimization algorithms—namely, Manta ray foraging optimization (MRFO), Marine Predators Algorithm (MPA), Jellyfish Search Optimizer (JS), and Equilibrium Optimizer (EO)—are employed to minimize the input current ripple under different operation scenarios. The results of the proposed modern optimization algorithms have shown more efficient converter performance and balanced power-sharing compared with conventional strategies and the literature on optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization (PSO). In addition, the results of all operation cases presenting the proposed optimal strategy successfully reduced the input current ripple and improve the performance of power-sharing at the converter compared with the conventional methods. [ABSTRACT FROM AUTHOR]

The radio stripe (RS) system is a practical implementation of cell-free mMIMO, in which a set of multi-antenna access points (APs) serves at the same time-frequency resources the user equipment (UE) in the network. The APs are sequentially connected in a stripe, sharing the same fronthaul link to the central processing unit. This work considers an uplink power optimization problem that aims to enhance the network spectral efficiency (SE) by considering two metrics—the max–min fairness and the max–sum rate. We employ a meta-heuristic based on the differential evolution algorithm to solve the bi-objective optimization problem. The SE performances of the full power along with the single-objective and multiple-objective scenarios are analyzed and compared for the optimal sequential linear processing detection scheme. The bi-objective approach is able to unveil the trade-offs to identify solution balancing the SE distribution resulting from the optimization of the max–min fairness and the max–sum rate objective functions. [ABSTRACT FROM AUTHOR]

Recommendation systems are a powerful tool that is an integral part of a great many websites. Most often, recommendations are presented in the form of a list that is generated by using various recommendation methods. Typically, however, these methods do not generate identical recommendations, and their effectiveness varies between users. In order to solve this problem, the application of aggregation techniques was suggested, the aim of which is to combine several lists into one, which, in theory, should improve the overall quality of the generated recommendations. For this reason, we suggest using the Differential Evolution algorithm, the aim of which will be to aggregate individual lists generated by the recommendation algorithms and to create a single list that will be fine-tuned to the user's preferences. Additionally, based on our previous research, we present suggestions to speed up this process. [ABSTRACT FROM AUTHOR]

This study attempts to explore the dynamic scheduling problem from the perspective of operational research optimization. The goal is to propose a rescheduling framework for solving distributed manufacturing systems that consider random machine breakdowns as the production disruption. We establish a mathematical model that can better describe the scheduling of the distributed blocking flowshop. To realize the dynamic scheduling, we adopt an "event-driven" policy and propose a two-stage "predictive-reactive" method consisting of two steps: initial solution pre-generation and rescheduling. In the first stage, a global initial schedule is generated and considers only the deterministic problem, i.e., optimizing the maximum completion time of static distributed blocking flowshop scheduling problems. In the second stage, that is, after the breakdown occurs, the rescheduling mechanism is triggered to seek a new schedule so that both maximum completion time and the stability measure of the system can be optimized. At the breakdown node, the operations of each job are classified and a hybrid rescheduling strategy consisting of "right-shift repair + local reorder" is performed. For local reorder, we designed a discrete memetic algorithm, which embeds the differential evolution concept in its search framework. To test the effectiveness of DMA, comparisons with mainstream algorithms are conducted on instances with different scales. The statistical results show that the ARPDs obtained from DMA are improved by 88%. [ABSTRACT FROM AUTHOR]

In CMOS integrated circuit (IC) design, operational amplifiers are one of the most useful active devices to enhance applications in analog signal processing, signal conditioning and so on. However, due to the CMOS technology downscaling, along the very large number of design variables and their trade-offs, it results difficult to reach target specifications without the application of optimization methods. For this reason, this work shows the advantages of performing many-objective optimization and this algorithm is compared to the well-known mono- and multi-objective metaheuristics, which have demonstrated their usefulness in sizing CMOS ICs. Three CMOS operational transconductance amplifiers are the case study in this work; they were sized by applying mono-, multi- and many-objective algorithms. The well-known non-dominated sorting genetic algorithm version 3 (NSGA-III) and the many-objective metaheuristic-based on the R2 indicator (MOMBI-II) were applied to size CMOS amplifiers and their sized solutions were compared to mono- and multi-objective algorithms. The CMOS amplifiers were optimized considering five targets, associated to a figure of merit (FoM), differential gain, power consumption, common-mode rejection ratio and total silicon area. The designs were performed using UMC 180 nm CMOS technology. To show the advantage of applying many-objective optimization algorithms to size CMOS amplifiers, the amplifier with the best performance was used to design a fractional-order integrator based on OTA-C filters. A variation analysis considering the process, the voltage and temperature (PVT) and a Monte Carlo analysis were performed to verify design robustness. Finally, the OTA-based fractional-order integrator was used to design a fractional-order chaotic oscillator, showing good agreement between numerical and SPICE simulations. [ABSTRACT FROM AUTHOR]

A recurrent neural network (RNN) and differential evolution optimization (DEO) based nonlinear model predictive control (NMPC) technique is proposed for position control of a single-link flexible-joint (FJ) robot. First, a simple three-layer recurrent neural network with rectified linear units as an activation function (ReLU-RNN) is employed for approximating the system dynamic model. Then, using the RNN predictive model and model predictive control (MPC) scheme, an RNN and DEO based NMPC controller is designed, and the DEO algorithm is used to solve the controller. Finally, comparing numerical simulation findings demonstrates the efficiency and performance of the proposed approach. The merit of this method is that not only is the control precision satisfied, but also the overshoots and the residual vibration are well suppressed. [ABSTRACT FROM AUTHOR]

While implementing Multiple Instance Learning (MIL) through Deep Neural Networks, the most important task is to design the bag-level pooling function that defines the instance-to-bag relationship and eventually determines the class label of a bag. In this article, Differential Evolutionary (DE) pooling—an MIL pooling function based on Differential Evolution (DE) and a bio-inspired metaheuristic—is proposed for the optimization of the instance weights in parallel with training the Deep Neural Network. This article also presents the effects of different parameter adaptation techniques with different variants of DE on MIL. [ABSTRACT FROM AUTHOR]

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input. [ABSTRACT FROM AUTHOR]

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control. [ABSTRACT FROM AUTHOR]

In this article, various modulation strategies and hysteresis-band control of a recently introduced dual-boost nine-level inverter (DB9LI) are implemented. DB9LI is a switched capacitor based inverter capable of generating symmetrical nine output voltage levels by employing a single DC source. The topology generates a boosted bipolar voltage at the output side without applying end-side H-bridge (usually employed to generate negative levels), which contrasts with some of the switched-capacitor topologies. The capacitors employed have an inherent self-balancing feature. Additionally, due to lower individual and total standing voltages, switches of low voltage ratings are required. As a result, the cost of switches and the inverter reduces considerably. Metaheuristic-based selective harmonic elimination and mitigation (SHE and SHM) and various sine-triangular pulse width modulation techniques are implemented and compared on various parameters. Finally, a robust eight-band hysteresis control is designed and implemented, which helps to obtain sinusoidal load current with a unity power factor. The modulation strategies and the hysteresis control are validated on the MATLAB simulation environment and an experimental prototype. [ABSTRACT FROM AUTHOR]

This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes—wall-following mode (WFM) and toward-goal mode (TGM)—based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location. [ABSTRACT FROM AUTHOR]