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Edge bundling is a technique used to improve the readability of large graph drawings by grouping edges to reduce visual complexity. This paper treats this task as a clustering problem, using compatibility metrics to evaluate solutions in an optimization pipeline combined with a clustering ensemble approach. The aim is to present the Clustering Ensemble-based Edge Bundling (CEBEB) method for solving the General-based Edge Bundling (GBEB) problem and report results for some given graphs. The CEBEB method proved very promising and generated better solutions than an existing evolutionary algorithm. Additionally, the paper introduces a new ensemble algorithm, specific for the GBEB, and reviews some previous results. [ABSTRACT FROM AUTHOR]

Multiple criteria evolutionary algorithms, being essentially parallel in their character, are a natural instrument of finding a representation of entire Pareto set (set of solutions and outcomes non-dominated in criteria space) for vector optimisation problems. However, it is well known that Pareto sets for problems with more than two criteria might become complicated and their representation very time-consuming. Thus, the application of such algorithms is essentially limited to bi-criteria problems or to vector optimisation problems with more criteria but of simple structure. Even in such cases, there are problems related to various important aspects of vector optimisation, such as the uniformity of representation of Pareto set, stopping tests or the accuracy of representing Pareto set, that are not fully covered by the broad literature on evolutionary algorithms in vector optimisation. These problems and related computational tests and experience are discussed in the paper. In order to apply evolutionary algorithms for decision support, it would be helpful to use them in an interactive mode. However, evolutionary algorithms are in their essence global and of batch type. Nevertheless, it is possible to introduce interactive aspects to evolutionary algorithms by focusing them on a part of Pareto set. The results of experimental tests of such modifications of evolutionary algorithms for vector optimisation are presented in the paper. Another issue related to vector optimisation problems with more than two criteria is the computational difficulty of estimating nadir points of Pareto set. The paper describes the use of diverse variants of evolutionary algorithms to the estimation of nadir points, together with experimental evidence. [ABSTRACT FROM AUTHOR]

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]

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Sevenlevel, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements. [ABSTRACT FROM AUTHOR]

Recent studies showed that deep learning techniques and image processing can identify the distinguishing design principles in architectural façades. However, predicting the strength of a principle is still a challenging task, as it requires a huge amount of annotated design variations. The difficulties in both searching such big numbers of data – and its labelling by experts – slow down the research. This paper proposes a computation approach for obtaining this type of data faster. With the help of parametric modelling and evolutionary algorithms, we could manipulate the design elements, and thereby generate different solutions. An integrated fuzzy logic decision mechanism could enable to carry human knowledge in the judging and labelling of alternatives automatically. The final synthetic data developed from real building images could be used for machine learning applications to enhance our understanding of artistic expression. [ABSTRACT FROM AUTHOR]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependencyaware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms rely on the population initialization strategy to determine a set of candidate solutions, which provide the preliminary knowledge of the problem landscape for the subsequent evolutionary process. However, the distribution of the initial population is rarely concerned in the multimodal evolutionary community. Moreover, a large initial population has not been attractive enough for evolutionary multimodal optimization, because it is difficult and challenging to achieve a balance between diversity and convergence within limited computing resources. As an extended version of our previous conference paper, this paper focuses on the construction of a large initial population that is beneficial for evolutionary multimodal optimization. First, we propose an improved density-based population initialization strategy to generate a uniform initial population with a certain degree of randomness. Then, after acquiring the raw fitness landscape of the problem, a fitness-weighted density-based population initialization strategy is proposed to explore potential global peaks. Finally, we design a multi-species cooperative coevolution algorithm for multimodal optimization, which balances the exploration and exploitation of such a population in the evolutionary process. Experimental results demonstrate that the proposed algorithm is better than the mainstream algorithms on the CEC2013 multimodal optimization benchmark. [ABSTRACT FROM AUTHOR]

Neural networks are increasingly used in recognition problems, including static and moving images, sounds, etc. Unfortunately, the selection of optimal neural network architecture for a specific recognition problem is a difficult task, which often has an experimental nature. In this paper we present the use of evolutionary algorithms to obtain optimal architectures of neural networks used for audio sample classification. We extend the Pytorch DNN Evolution tool implementing coevolutionary algorithms which create groups of neural networks that solve a given problem with a certain accuracy, with the support for problems in which training data consists of audio samples. In this paper we use the co-evolutionary approach to solve a sample sound classification problem. We describe how the sound data was prepared for processing with the use of the Mel Frequency Cepstral Coefficients (MFCC). Next we present the results of experiments conducted with the AudioMnist dataset. The obtained neural network architectures, whose classification accuracy is comparable to the classification accuracy attained by the AlexNet neural network, and their implications are discussed. [ABSTRACT FROM AUTHOR]

Association rule hiding is an efficient solution that helps organizations to avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. Cuckoo Optimization Algorithm (COA) sanitizes the transaction database but this method has limitation due to its slow convergence and exploitation capabilities. Hence in this paper, Enhanced Elephant Herding Optimization Algorithm for Association Rule Hiding (EEHOA4ARH) is proposed for association rule hiding. In EEHOA, two core functions such as clan updating operator and separating operator are used for association rule hiding that also realizes the fast convergence and exploitation capabilities. Moreover, the searching strategy in COA4ARH for the selection of best solution is highly time consuming. To reduce the time consumption for the selection of best solution, a Crowding Distance (CD) concept is combined with EEHOA4ARH. By continuously updating the best elephant and replacing the worst elephant in the population, EEHOA4ARH-CD sanitizes the transaction database effectively. Thus the proposed EEHOA4ARH achieves the less computation time, fast convergence and better exploitation capabilities by using crowding distance. The experimental results prove the effectiveness of the proposed EEHOA4ARH–CD method in terms of hiding failure, lost rule and execution time with 44.66 s. [ABSTRACT FROM AUTHOR]

This paper presents the implementation for the first time of a Multi-Particle Swarm Optimization (MPSO) algorithm in the tuning of a PID controller for Power Factor Correction (PFC), applied to a 100W AC-DC boost converter. MPSO algorithm navigates in a search space where each dimension of the space corresponds to the controller constants (Proportional, Integral, Derivative and the Derivative Filter), prioritizing communication over exploration in the algorithm. The controller parameters are randomly initialized in a reduced sector of the space [ K p , K i , K d , K n ], to optimize the search for a PID solution. In the first step, the algorithm is validated using a simulation model in Simulink and Matlab. Subsequently, a final implementation using a real converter is implemented with the PID tuned by MPSO, improving the PFC obtained in previous work. Although previous works have used evolutionary algorithms applied to heuristic optimization to tunning PID controllers, the MPSO algorithm is not usually used for this purpose, particularly to tunning a PID controller in a power electronics system. One advantage of MPSO over the PSO classical algorithm is the search at different points if the vectorial field looks for an optimal solution. PSO presents problems such as getting stuck in a locally optimal solution. The PID controller is trained offline, with the advantage of allowing the risk of damage in the Boost converter for transitory response, increasing the performance of the Power Factor Correction in the converter. This research opens the possibility to use the extended version of the PSO bioinspired algorithm to tune offline controllers to improve the power converter's performance, minimizing the risk presented in the real-time tuning process. [ABSTRACT FROM AUTHOR]

Rao algorithms that include three algorithms are very simple and parameter-less algorithms with effective and desirable performance. This paper modifies these three algorithms, merges them, and establishes a powerful group algorithm. In the first optimization step, the suggested algorithm is tested on 30 standard CEC2014 functions with 50 dimensions to compare it with main algorithms, several well-known algorithms, and modified versions of RAO algorithm. It becomes evident in the first test that the suggested optimizer is effective, and reliable for optimization of real-parameter functions, and it has shown its superiority to original RAO algorithm and several modern and modified versions of RAO algorithms for most of the test functions and achieved more acceptable results than them. Moreover, the suggested algorithm benefits a faster convergence characteristic than original RAO algorithms. The proposed Colonial Competitive RAO (CCRAO) has been applied on five popular engineering problems and its results have been compared with those of recent papers. According to the results, CCRAO is an effective, robust, and reliable optimizer for engineering design problems and can contain all useful features of RAO algorithms altogether. CCRAO has succeeded to converge to the best solution for these engineering problems and surpasses most of the other algorithms. [ABSTRACT FROM AUTHOR]

The 0–1, Multidimensional Knapsack Problem (MKP) is a classical NP-hard combinatorial optimization problem with many engineering applications. In this paper, we propose a novel algorithm combining evolutionary computation with the exact algorithm to solve the 0–1 MKP. It maintains a set of solutions and utilizes the information from the population to extract good partial assignments. To find high-quality solutions, an exact algorithm is applied to explore the promising search space specified by the good partial assignments. The new solutions are used to update the population. Thus, the good partial assignments evolve towards a better direction with the improvement of the population. Extensive experimentation with commonly used benchmark sets shows that our algorithm outperforms the state-of-the-art heuristic algorithms, TPTEA and DQPSO , as well as the commercial solver CPlex. It finds better solutions than the existing algorithms and provides new lower bounds for 10 large and hard instances. • We propose an efficient and practical algorithm for solving the 0–1 Multidimensional Knapsack Problem in this paper. • The algorithm takes advantages of Evolutionary Computation and Large Neighborhood Search. • It works well in solving the large and hard 0–1 MKP instances. • We provide new lower bounds (best known results) for 10 large and hard instances from the commonly used benchmark set. [ABSTRACT FROM AUTHOR]

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee pointbased reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems. [ABSTRACT FROM AUTHOR]

The human body is a vital source of data such as images and signals. The signals are collected from different human organs and utilized in diagnosing different diseases. The designing and implementation of intelligent computer programs that try to emulate human intelligence are a sign of the integration of various sciences and areas of knowledge. The development of technologies associated with Artificial Intelligence (AI) techniques that are applied to medicine, represents a novel perspective, which can reduce costs, time, and medical errors. Coronary artery disease (CAD) killed many people in the world, it is considered the most common type of heart disease. This paper uses different evolutionary algorithms to optimize the neural network parameters to enhance the classification process of Coronary Artery Disease (CAD). A hybrid system that combines a Genetic Algorithm (GAs), Biogeography-Based Optimization (BBO) with neural networks (NNs) [GAsBBO-MLPNNs] is proposed to enhance the accuracy of CAD. This paper concentrates on the medical classification system for heart disease on CAD using the Z-Alizadeh Sani dataset as a benchmark. The proposed GAsBBO-MLPNNs outperformed other hybrid models such as; Biogeography-Based Optimization (BBO) and particle swarm optimization (PSO) methods combined with NNs, and previous works, where the method performance parameters result represented as 94.5%, 96.4%, and 94.8% accuracy, sensitivity, and specificity respectively. [ABSTRACT FROM AUTHOR]

Modern society heavily depends on electricity for vital services, delivered to final users by Distribution Systems (DSs). Since DSs are exposed to permanent faults that can cause the non-supply of electricity to large areas, increases in their resilience have been a growing concern of researchers, public agents, and society. This paper proposes a methodology for analyses of two important metrics for resilience assessment, namely vulnerability and recovery capacity of real DSs for permanent faults (single and multiple faults). The methodology quantifies the single and multiple faults' probability of occurrence for estimating the DS vulnerability and the system's capacity to deal with the impacts of permanent faults through the Service Restoration (SR) process. It comprises a statistical analysis for finding the faults probabilities and a multi-objective evolutionary algorithm for solving the SR problem for large-scale DSs. The results from its application to a real large-scale Brazilian DS, composed of 49,938 buses, 4771 switches, 9 substations, and 95 feeders are provided. The study shares valuable utility experience towards new insights and on challenges faced in the real-time operation of a Brazilian DS. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior's integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN's objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language. With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight, accompanied by the slightest iteration error, which minimizes the objective function of SRBFNN-2SAT. [ABSTRACT FROM AUTHOR]

Efficient traffic management in wireless networks is crucial for optimizing resource utilization and enhancing overall network performance. This paper introduces a novel approach to dynamic routing algorithms utilizing evolutionary algorithms for effective wireless traffic management. The proposed system leverages the adaptability and optimization capabilities of evolutionary algorithms to dynamically adjust routing paths based on real-time network conditions. Our algorithm employs a genetic programming framework to evolve and refine routing strategies, considering factors such as network congestion, link quality, and traffic load. This dynamic approach enables the network to autonomously adapt to changing conditions, ensuring optimal route selection for data transmission. The evolutionary nature of the algorithm allows it to continually learn and improve, making it well-suited for the dynamic and unpredictable nature of wireless environments. The effectiveness of the proposed algorithm is evaluated through extensive simulations, demonstrating significant improvements in terms of throughput, latency, and overall network efficiency compared to traditional static routing approaches. The system ability to handle diverse traffic patterns and adapt to varying network scenarios positions it as a robust solution for nextgeneration wireless networks. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the identification efficiency of non-linear systems using the Kalman filter (KF), which is optimised with the Artificial Electric Field (AEF) algorithm. The conventional KF suffers from the proper tuning of its parameters, which leads to a divergence problem. This issue has been solved to a great extent by the meta-heuristic AEF algorithm assisted Kalman filter (AEF-KF). This paper proposes three steps for the identification of the systems while solving the problem as mentioned above. Firstly, it converts the identification model to a measurement problem. Next, the AEF algorithm optimises the KF parameters by considering the fitness function with the KF equations. The third step is to identify the model using conventional KF algorithm with the optimised KF parameters. To evaluate the performance of the proposed method, parameter estimation error, mean squared error (MSE), fitness (FIT) percentage, statistical information and percentage improvement are considered as the performance metrics. To validate the performance of the proposed method, five distinct non-linear models are identified with the Volterra model using KF and the AEF-KF techniques under various noisy input conditions. Besides, the practical applicability of the proposed approach is also tested on two non-linear benchmark systems using experimental data sets. The obtained simulation results confirm the efficacy and robustness of the proposed identification method in terms of the convergence speed, computational time and various performance metrics as compared to KF, Kalman smoother (KS) which is optimised using different state-of-the-art evolutionary algorithms and also other existing recently reported similar types of stochastic algorithms based approaches. • The proposed AEF-KF method resolves the proper tuning problem of Kalman filter. • The role of AEF algorithm is to obtain the optimised set of all the KF parameters. • The proposed method is insensitive to various additive noise input levels. • Simulations carried out on five Volterra models along with two benchmark systems. • AEF-KF outperforms KF and all other reported results with higher convergence speed. [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]

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]

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works. [ABSTRACT FROM AUTHOR]

More and more multi-objective evolutionary algorithms are proposed and used to solve many-objective optimization problems and large-scale optimization problems. However, most of the existing algorithms use fixed crossover probability (pc) and mutation probability (pm) in the generation process of offspring, which makes the performance of the algorithm poor when dealing with complex problems. In this paper, by analyzing the complex non-linear relationship between performance metrics and pc and pm in the search process of many-objective evolutionary algorithms. A fuzzy inference system is constructed to dynamically update the pc and pm in the iterative process. Therefore, a fuzzy adaptive NSGA-III algorithm is proposed and used to solve large-scale optimization problems. For the construction of fuzzy systems, this paper takes the number of iterations, convergence metric, and diversity metric as inputs, and pc and pm as outputs. Four different fuzzy systems are obtained, and the best fuzzy system is selected through experiments. In order to further verify the effectiveness of the algorithm, the proposed algorithm and the existing literature are tested on LSMOP problems. The results show that the fuzzy system can well describe the complex non-linear relationship between the performance metrics and the pc and the pm in the search process of the many-objective evolutionary algorithm. It also effectively improves the performance of the algorithm when solving large-scale optimization problems, resulting in maintenance of the convergence and diversity of the population. [ABSTRACT FROM AUTHOR]

The industrial wireless sensor domain has undergone a shift in paradigm as a consequence of Internet of Things (IoT), a thriving technology that has been leading the way in short-range and fixed wireless sensing. One of the problems associated with industrial wireless sensor networks (IWSNs) is finding the optimal solution for minimizing defect time in superframe scheduling. This paper proposed a method based on the use of evolutionary algorithms, namely particle swarm optimization (PSO), orthogonal learning PSO, genetic algorithm (GA), and modified GA for optimizing the superframe scheduling. Additionally, we evaluated a contemporary method, deadline monotonic scheduling, on the ISA 100.11a protocol. The use of this standard as a case study means that the presented 72 simulations are object-oriented, with numerous variations in the number of timeslots and wireless sensor nodes. The simulation results show that the use of GA and modified GA can improve the performance in terms of idle, missed deadlines, memory consumption, and processing time comparing to other metaheuristic algorithms. A comprehensive analysis and detailed performance evaluation are provided in the paper. [ABSTRACT FROM AUTHOR]

The role of cost, time and labor intensity in the construction process is very important, but there is a problem of poor optimization effect. The standard genetic algorithm cannot solve the problem of index optimization in the construction process, and the optimization rationality is low. Therefore, this paper proposes an improved genetic algorithm to study the objectives of construction projects. Firstly, the k-value clustering theory is used to analyze the characteristic values of the index and divide them according to the changing trend to reduce the disturbance factors in optimization. Then, the k-value clustering theory is used to search for the child-parent node of the indicator to form the final optimization result and complete the multi-objective optimization. MATLAB simulation shows that under certain optimization criteria, improving the genetic algorithm can improve the rationality of multi-objective optimization, and the optimization time is shorter than that standard improves genetic algorithms. [ABSTRACT FROM AUTHOR]

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

Big data analytics often involves complex decision-making processes that require finding efficient cost-performance tradeoffs. Evolutionary algorithms (EAs) have proven to be effective in solving multi-objective optimization problems by exploring the Pareto front, which represents the optimal tradeoffs between conflicting objectives. In this paper, we propose an evolutionary algorithm-based approach for Pareto front exploration in big data analytics. Our approach employs a novel fitness function that incorporates both cost and performance metrics, allowing the algorithm to simultaneously optimize for both objectives. We introduce several mutation and crossover operators tailored for big data analytics, ensuring effective exploration of the solution space. To validate the effectiveness of our approach, we conduct experiments using real-world big data analytics scenarios. The results demonstrate that our evolutionary algorithm-based approach successfully explores the Pareto front, enabling decision-makers to identify optimal costperformance tradeoffs in big data analytics. [ABSTRACT FROM AUTHOR]

The contemporary paradigm for developing novel distribution grids is centered on the integration of distributed generations (DGs) and shunt capacitors (SCs) within the distribution infrastructure. Unreasonable DG and SC configurations may have a negative impact on grid line loss and voltage quality. The goal of optimal allocation research of distribution network (OARDN) is to allocate distributed generators (DGs) and shunt capacitors (SCs) in the distribution network in a rational manner to minimize power losses, enhance voltage quality, and ensure secure and stable power supply to the loads. Aiming at the critical OARDN problems, such as minimizing power loss and improving voltage quality, this paper proposes an improved sand cat swarm optimization algorithm (ISCSO). To address the issue of the original SCSO being prone to local optima and low search accuracy, this method introduces a tent map-based chaotic strategy and reverse learning approach to augment the precision of the optimization, and proposes a cross-learning mechanism to expand the global search ability, and this method is used to solve the single-objective problem. Based on ISCSO, the sensitivity analysis method and pareto non-inferior sorting method are introduced, which is extended to the MOISCSO algorithm to solve multi-objective OARDN problem. Simulation experimental results on IEEE33, IEEE69, and IEEE119 test systems and comparison with other scholars' research results show that the proposed method is practical and competitive for solving single-objective and multi-objective OARDN problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel unsupervised text feature selection technique that combines the multi-objective evolutionary algorithm NSGA II with a local Hill Climbing based search. The objective functions in NSGA II are an adapted version of the Mean Absolute Difference criterion and the Holes projection pursuit index, both of which incorporate information regarding the cardinality of the selected feature subset. The local Hill Climbing procedure aims to further improve the solutions provided by NSGA II through elimination/replacement of redundant features. The redundancy level of a feature F in a chromosome is measured via the lexical and semantic similarities between F and the other features represented in the chromosome. Experiments conducted on three real-world textual subdatasets indicate that our proposed approach achieves very good results in comparison to four state-of-the-art evolutionary unsupervised feature selection techniques and to one classical feature extraction method with proven effectiveness (Principal Component Analysis). [ABSTRACT FROM AUTHOR]

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a largescale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms. [ABSTRACT FROM AUTHOR]

The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. In this domain, many real-life decision problems need to be solved repeatedly with changing data and parameters. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, to deal with such problems, a new evolutionary framework with multiple novel mechanisms is proposed. The new mechanisms are for (1) dealing with both linear and non-linear components in the constraint functions, (2) identifying the rate of change in the coefficients of the variables and (3) updating the population efficiently after every change occurs in the problem. To evaluate the performance of the proposed algorithm, we designed a new set of 13 dynamic benchmark problems, each of which consists of 20 dynamic changes and 3 different scenarios. The results demonstrate that the proposed algorithm significantly contributes in achieving good quality solutions, high feasibility rates and fast convergence in rapidly changing environments. In addition, the framework shows its capability of using different meta-heuristics to solve dynamic problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust differential game scheme to solve the collision avoidance problem for networked multi-agent systems (MASs), subject to linear dynamics, external disturbances and limited observation capabilities. Compared with the existing differential game approaches only considering obstacle avoidance objectives, we explicitly incorporate the trajectory optimization target by penalizing the deviation from reference trajectories, based on the artificial potential field (APF) concept. It is proved that the strategies of each agent defined by individual optimization problems will converge to a local robust Nash equilibrium (R-NE), which further, with a fixed strong connection topology, will converge to the global R-NE. Additionally, to cope with the limited observation for MASs, local robust feedback control strategies are constructed based on the best approximate cost function and distributed robust Hamilton–Jacobi–Isaacs (DR-HJI) equations, which does not require global information of agents as in the traditional Riccati equation form. The feedback gains of the control strategies are found via the ant colony optimization (ACO) algorithm with a non-dominant sorting structure with convergence guarantees. Finally, simulation results are provided to verify the efficacy and robustness of the novel scheme. The agents arrived at the targeted position collision-free with a reduced arrival time, and reached the targeted positions under disturbance. • The proposed scheme can optimize trajectories with the artificial potential field. • Local control strategies are constructed to cope with the limited observation. • The feedback gains of control strategies are found via the heuristic algorithm. • The robustness of control strategies is considered by optimizing the worst-case cost. [ABSTRACT FROM AUTHOR]

This paper proposes the application of control by state feedback using the linear quadratic regulator (LQR) optimized by metaheuristics to damp low-frequency electromechanical oscillations in electrical power systems. The current sensitivity model was used to represent the single machine infinite bus (SMIB) system in the time domain. The weighting matrices of the LQR were adjusted using four different algorithms: (i) the genetic algorithm, (ii) the differential evolution algorithm, (iii) the particle swarm optimization algorithm, and (iv) the gray wolf optimization (GWO) algorithm. In the cases considered, disturbances were applied to the electrical power system and, then, performances comparisons associated with each metaheuristic were statistically analyzed, in which the number of iterations, error, and time to achieve convergence of each algorithm were compared. From the results, it was possible to conclude that the algorithms were efficient in adjusting the weighting matrices of the LQR, providing additional damping to the poles of interest of the system. It was also possible to conclude that the GWO algorithm presented the best performance, accrediting it as a powerful tool in the study of small-signal stability for the analyzed case. • Design of an LQR for small-signal stability analysis of power systems. • Implementation metaheuristics for adjusting the parameters of the LQR. • Comparison of the performance of evolutionary and swarm-based algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative co-evolutionary improved particle swarm genetic (CCIPSG) algorithm for ship branch pipe route design (SBPRD) based on the strategy of first decomposition and then reconstruction. SBPRD is a common type of ship pipe route design connecting one start point and several end points with various performance constraints in 3-D space. The traditional optimization method of SBPRD needs to select the laying sequence of branch pipelines, determine the branch points, and finally conduct the pipeline layout, which is full of uncertainty. The CCIPSG algorithm proposed in this paper aims to avoid the uncertainty of laying sequence and branch points by using the strategy of decomposition before reconstruction. The branch pipe route is deemed as a system; through the process of decomposing, the branch pipe route is decomposed as several single pipe routes with a common start point and different end points. After obtaining the optimal solutions of each single pipe route by using the improved particle swarm genetic algorithm, the co-evolutionary mechanism and overlapped potential energy value method are used to reconstruct the branch pipeline with the minimum total path length and elbows. Compared with the conventional method, the CCIPSG algorithm could not only automatically determinate the laying sequence and branch points but also improve the convergence speed and the quality of the solution. Finally, the simulation result demonstrates the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper feasibility-preserving genetic operators for hybrid algorithms using TSP solvers for the Inventory Routing Problem (IRP) are studied. The IRP is a problem of jointly optimizing delivery schedules and routes for vehicles transporting products from a supplier to a number of retailers. This optimization problem is highly constrained, because limits on inventory levels as well as the vehicle capacity have to be taken into account. Moreover, the IRP is a generalization of the TSP and solving the TSP effectively is an important part of obtaining good solutions to the IRP. In this paper evolutionary algorithms are used for solving the IRP, but finding good routes is delegated to a state-of-the-art TSP solver. Therefore, genetic operators used in this paper focus on constructing the delivery schedule and not on optimizing the routes. In the experimental part of the paper an evolutionary algorithm using feasibility-preserving genetic operators is compared to the Infeasibility Driven Evolutionary Algorithm (IDEA) that uses the selective pressure to obtain feasible solutions. Presented results suggest that designing good feasibility-preserving genetic operators is important, because allowing the optimization algorithm to generate infeasible solutions and handling infeasibility in IDEA using the selective pressure leads to inferior results. [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]

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]

Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time. [ABSTRACT FROM AUTHOR]

The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (ϕ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge computing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable-length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Manual design of Complementary Metal Oxide Semiconductor (CMOS) based analog circuit design becomes more challenging and tedious task due to very complex physical models and variation in the fabrication process as technology scale down. In this continuously changing era, the demand of mixed signal System on Chip (SoC) increasing day by day which digital and analog circuits integrated on same silicon chip. For the digital circuit design, many mature computer based automated tools have been established and limited research efforts made towards automization of the analog circuit design. This gap opens the ample research space for the researcher in the field of analog circuit design. Automization of analog circuit design makes the mixed signal SoC is the best approach to cope up with this problem, cost considerations and the time to market. This motivates the analog circuit designer to explore more automated computer aided tools in the field of analog circuit design. In this review paper, performance evaluation of various evolutionary algorithms is compared. The comparison includes most used Differential Evolution (DE) algorithm, Cuckoo Search (CS) algorithm, Particle Swarm Optimization (PSO) algorithm, hybrid CSPSO algorithm. The performance evaluations of these algorithms are compared with the different standard benchmark functions and the convergence graphs of these standard benchmark functions are compared to test number of runs with respect to number of iterations. [ABSTRACT FROM AUTHOR]

Disturbance-observer-based control is a well-known technique for the rejection of unmeasurable disturbances. The application of disturbance-observer-based control is well established for single-input and single-output systems but presents two challenges when applied to multivariable systems with time delays. The first challenge is the calculation of the inverse of the nominal model. The second challenge is the optimal synthesis of the disturbance observer filter to obtain a trade-off between the disturbance rejection and measurement noise attenuation. In this paper, we propose strategies to address these two challenges. To facilitate the design and implementation of the disturbance observer, we propose to approximate the inverse of the nominal model by employing the concept of equivalent transfer function. The equivalent transfer function approximates the inverse of a multivariable model by a matrix whose elements are the inverse of first-order plus time delay transfer functions, that are simpler to deal with. To tackle the second challenge, we propose a synthesis formulation based on a multi-objective optimization problem. Through a multi-objective evolutionary optimization algorithm, it is possible to obtain a set of efficient solutions with different trade-offs between the two objectives. We apply the proposed strategies to two well-known problems in the field of process control to demonstrate its efficiency. We verify that the use of the equivalent transfer function presents the best results in comparison to other methodologies considering the three control objectives: tracking of reference signals, rejection of disturbances, and attenuation of measurement noises. [ABSTRACT FROM AUTHOR]

Antenna arrays have various applications, such as radar, military systems, and wireless communications. The performance of the communication system depends heavily on the antenna array design process. The major challenges in designing such arrays are the high sidelobe levels and the enormous number of elements. Recently, optimization techniques that exploit the evolutionary algorithms have been used extensively for thinning the antenna arrays to reduce power consumption and improve the radiation pattern by decreasing the sidelobe levels. However, due to the stochastic nature of the resolution algorithms, achieving a global optimal for such algorithms is not assured. Based on the combination theory, this paper adopts an element-by-element removing strategy for thinning planar arrays. Without loss of generality, a uniformly excited 25 elements rectangular array with isotropic elements is investigated as an example. The proposed thinning method examines all the possible arrangements of the placements of 25 elements in the phased array aperture. The optimum position of elements is selected to achieve the desired characteristics of the radiation pattern. The results show that with the 40% thinning percentage, the same radiation pattern for the full array (without thinning) can be witnessed. Consequently, a considerable reduction in resource utilization can be achieved in terms of power consumption, the number of phase shifters, and the number of attenuators. [ABSTRACT FROM AUTHOR]

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency. [ABSTRACT FROM AUTHOR]

The characteristic of multi-modal multi-objective optimization problems (MMOPs) is that multiple equivalent Pareto solution sets (PSs) in the decision space correspond to the same Pareto front (PF) in the objective space. The difficulty in solving the MMOPs lies in how to maintain the distribution in space. Many multi-modal multi-objective evolutionary algorithms (MMEAs) take convergence as the primary selection criterion, which makes it difficult for the algorithm to find all PSs in the decision space. In view of this situation, this paper proposes a partitioned knowledge-guided MMEA with multi-stage. The algorithm makes stage changes according to the proportion of evaluation consumed by the algorithm during the evolution, and adjusts the environment selection strategy as the stage changes. At the beginning of evolution, region division is carried out to prevent the solutions on each PS from interfering with each other and evolving independently. When the evaluation consumption reaches a certain proportion, it enters the middle stage. The information of the obtained solutions are used to guide the evolutionary direction of the population, and the deleted promising solutions are reclaimed. In the later stage, the steady state updating is performed to improve the distribution of population. The experimental results on four multi-modal multi-objective test suites with different features show that the proposed algorithm is more competitive than other seven excellent algorithms. • The regional division strategy makes the population move closer to potential PSs. • The solution reclaim strategy reclaims deleted promising solutions. • The environment selection makes stage changes by proportion of evaluation consumed. [ABSTRACT FROM AUTHOR]