Search

Logistics refers to the process of moving goods or materials from the point of origin to the point of destination, with the utilization of various resources including personnel, vehicles, facilities and equipment. In the paper entitled "Robust multi-product inventory optimization under support vector clustering-based data-driven demand uncertainty set", a robust multi-product inventory optimization approach is developed with an uncertainty set constructed from the available data using support vector clustering. Product sales forecasting would directly impact inventory management and customer service level and thus plays an important role in logistics systems. [Extracted from the article]

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand; at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%. [ABSTRACT FROM AUTHOR]

Recent developments in fuzzy logic have presented new theories, concepts and algorithms extending the original ideas of the pioneering work of Prof. Zadeh. In this case, extensions to type-1 like interval type-2 fuzzy logic, general type-2 fuzzy logic, intuitionistic fuzzy logic and other similar approaches have been recently proposed to tackle more complex real-world problems. In the 11th paper, by Fevrier Valdez and Cinthia Ochoa, a new approach for dynamic parameter adaptation in the harmony search algorithm for the optimization of interval type-2 fuzzy logic controllers was outlined with interesting results in the optimization of benchmark problems. [Extracted from the article]

Two goals of multi-objective evolutionary algorithms are effectively improving their convergence and diversity and making the Pareto set evenly distributed and close to the real Pareto front. At present, the challenges to be solved by the multi-objective evolutionary algorithm are to improve the convergence and diversity of the algorithm, and how to better solve functions with complex PF and/or PS shapes. Therefore, this paper proposes a gray wolf optimization-based self-organizing fuzzy multi-objective evolutionary algorithm. Gray wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on 15 problems of GLT1-6 and WFG1-9 and the algorithm proposed in this paper achieve the best on 18 values. And the result shows that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The electric power system must supply energy with quality, security, and reliability. However, the system can operate close to its critical limits due to environmental or economic reasons. Under these circumstances, voltage instability problems, which have been the cause of several significant blackouts worldwide in the last two decades, are prone to happen due to increasing power demand. For this reason, guaranteeing an acceptable voltage security margin is crucial for a safe and reliable power system operation. Over the years, many studies have been made to identify and control voltage instability problems, where voltage stability margin is a parameter used to identify these problems, calculated through continuation power flow. Besides that, to prevent or correct these problems, preventive or corrective controls may be used, such as capacitor switching, for example. Within this context, this paper proposes a method capable of defining a list of capacitor sets that mitigate the criticality of a set of contingencies and maximize the voltage stability margin of the power system under analysis as its main contribution. A continuation power flow embedded in a binary differential evolution algorithm calculates the voltage stability margin. This margin is optimized by selecting capacitor banks to be switched on/off after the contingency. The output of the method can be presented in a suitable format to the system operator, enabling fast choices of corrective control actions after detecting a contingency in real-time operation. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. To enhance identification accuracy, a genetic algorithm is used to iterate the feature sets of angles and line segments, and the optimum features are ultimately generated via cross evolution of generations. The character library is then utilized as an input item for average grouping for trials, and the resulting feature sets are placed in a position matrix and compared one by one to the samples in the database. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system. [ABSTRACT FROM AUTHOR]

Most production scheduling problems, including standard flexible job shop scheduling problems, assume that machines are continuously available. However, in most cases, due to preventive maintenance activities, machines may not be available for a certain time. Meanwhile, in the entire workshop production process, the transportation process of workpieces cannot be ignored. Therefore, the impact of transportation on the production planning should be considered in the scheduling process. To consider both preventive maintenance and transportation processes in the flexible job shop scheduling problem, this paper proposes a flexible job shop scheduling problem considering preventive maintenance activities and transportation processes and establishes a multi-objective flexible job shop scheduling model optimizing the total energy consumption and total makespan. Furthermore, a multi-region division sampling strategy-based multi-objective optimization algorithm integrated with a genetic algorithm and a differential evolution algorithm (MDSS-MOGA-DE) is proposed to solve the model. In the proposed algorithm, a multi-region division sampling strategy and two evaluation functions are utilized to improve the diversity of solutions. In addition, this paper combines a genetic operation and a differential operation to further enhance the search ability of the algorithm. The validity of the algorithm is verified by a real case. The computational results reveal that the proposed model and algorithm obtain appropriate results and have the potential to be applied to other similar problems. [ABSTRACT FROM AUTHOR]

Constructing Bayesian network structures from data is an NP-hard problem. This paper presents a novel method for Bayesian network structure learning using a discrete Harris hawks optimization algorithm, named BNC-HHO. It uses the max-min parents and children algorithm, V-structure & log-likelihood function, and neighborhood structures to limit the search space during the initialization phase. Then, the Harris hawk optimization algorithm is extended from the continuous to the discrete domain by redefining the movement strategies of hawks using genetic operators in genetic algorithm. The crossover and mutation operations in the proposed method are controlled by an adaptive crossover and mutation rate based on the X-conditional cloud. To balance the exploration and exploitation phases, a nonlinear escaping energy curve is also designed. Finally, the quality of the solution is further improved using a local optimizer. Experiments on various standard networks demonstrate that the proposed algorithm can quickly get higher structure scores and better convergence accuracy in most cases compared to other state-of-the-art algorithms. It indicates that the proposed algorithm can be used as an effective and feasible method for learning Bayesian network structures. [ABSTRACT FROM AUTHOR]

Real-parameter single objective optimization has been studied for decades. In recent, a new setting is applied in this field based on the consideration that solving difficulty scales exponentially with the increase in dimensionality. Under the new setting, differential evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic—gaining–sharing knowledge-based algorithm, becomes a dark horse. Furthermore, ensemble of the above two types of algorithm is proposed in the literature. Although such ensemble shows good performance, provided that a more reasonable scheme is used for the communication between the constituent algorithms, better ensemble can be obtained. We believe that the new scheme should be with adaptiveness. In this paper, we propose an adaptive scheme for the communication. According to the scheme, individuals chosen based on fitness and lifetime are exchanged. In fact, in the field of DE, it is rare to consider lifetime of individual. However, lifetime is no less important than fitness in our scheme. In our experiment, our ensemble is compared with seven state-of-the-art algorithms. According to experimental results, our ensemble is comparable to one of the peers and better than the other ones. [ABSTRACT FROM AUTHOR]

Parameters identification of photovoltaic (PV) models is significant for forecasting power of PV system and simulating the PV models. To accurately identify the parameters of different PV models based on measured current–voltage characteristics, a novel algorithm inspired by triangle vertex and edge, called triangle search optimization (TSO), is proposed in the paper. The TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators for exploration and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the diversity of the population. The experiments on CEC 2017 test suite show that the proposed TSO performs better than the state-of-the-art algorithms in convergence accuracy. The novel algorithm, TSO, is employed to solve the parameters identification problems of single-diode PV model, double-diode PV model and PV module. Comprehensive experiments indicate that TSO can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

To enhance the solution quality and accelerate the convergence speed, teaching quality evaluation-based differential evolution algorithm with Levy flight and adaptive parameters is proposed in this paper, which contains two modifications. One is the new multi-population strategy inspired by teaching quality evaluation. In this multi-population strategy, first, the entire evolving population is partitioned into several subpopulations of equal size and each subpopulation is evaluated and assigned an evaluation score like the way in teaching quality evaluation, according to which new individuals are generated. Then the subpopulations integrate and are re-divided to make some individuals in the better subpopulations spread to other subpopulations, thus guiding the evolution of the entire population and accelerating the convergence speed. The other modification is the improved mutation strategy combined with Levy flight and adaptive parameters. Levy flight can avoid the algorithm stuck with the local optimum, and adaptive parameters can change dynamically during the evolutionary process, hence reducing the adverse effects caused by parameter sensitivity. To validate the effectiveness of the proposed algorithm, extensive test experiments on benchmark functions are carried out, and the two modifications are studied separately at length. At the end, the proposed algorithm is applied to synthesize linear sparse array antennas. The results show that it can reduce peak sidelobe level further and enhance the performance of linear sparse arrays. [ABSTRACT FROM AUTHOR]

The economy of a country is directly impacted by agricultural productivity. If proper precautions are not taken in this area, plants suffer serious consequences, which have an impact on the quality, quantity, or productivity of the corresponding products. In this context, the farmers need an agriculture expert for the examination of the plant diseases, which takes a lot of time as well as continuous monitoring of plants. Hence, multilevel thresholding is a possible solution, which is useful for identifying the diseases in the crops by changing the color variation of a segmented image. However, it has a large range of applications in various domains such as remote sensing, the medical domain, the biometric domain. In this paper, an improved technique using horizontal and vertical crossover shuffled frog leaping algorithm (HVSFLA) is proposed for multilevel thresholding for the crop image based on a 2D histogram. The 2D histogram uses the grayscale value and non-local mean (NLM), whereas the one-dimension (1D) histogram uses only grayscale value. Therefore, in the proposed method, 2D Kapur's entropy integrating with non-local mean 2D histogram is exploited for multilevel thresholding of the crop images. To investigate the efficacy, the proposed method is compared with well-known optimization techniques like beta differential evolution, artificial bee colony, bacterial foraging optimization, and particle swarm optimization. It is evident from the experimental results that the proposed technique yields better results than the 1D histogram technique in terms of root-mean-square error, structural similarity index, peak signal-to-noise ratio, feature similarity index, coefficient of variation, and fitness function. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is among the best evolutionary algorithms for global optimization. However, the basic DE has several shortcomings, like the slow convergence speed, and it is more likely to be stuck at local optima. Additionally, DE's performance is sensitive to its mutation strategies and control parameters for mutation and crossover. In this scope, we present in this paper three mechanisms to overcome DE limitations. First, two novel mutations called DE/mean-current/2 and DE/best-mean-current/2 are proposed and integrated in the DE algorithm, and they have both exploration ability and exploitation trend. On the other hand, to avoid being trapped in local minima of hard functions, a new exploration operator has been proposed called Weibull flight based on the Weibull distribution. Finally, new adapted control parameters based on the Weibull distribution are integrated. These parameters contribute to the optimization process by adjusting mutation scale and alleviating the parameter setting problem often encountered in various metaheuristics. The efficacy of the proposed algorithms called meanDE, MDEW, AMDE, and AMDEW is validated through intensive experimentations using classical tests, some challenging tests, the CEC2017, CEC2020, the most recent CEC2022, four constraint engineering problems, and the data clustering problem. Moreover, comparisons with several popular, recent, and high-performance optimization algorithms show a high effectiveness of the proposed algorithms in locating the optimal or near-optimal solutions with higher efficiency. The experiments clearly indicate the effectiveness of the new mutations compared to the standard DE mutations. Moreover, the proposed Weibull flight has a great capacity to deal with the hard composition functions of CEC benchmarks. Finally, the use of adapted control parameters for the mutation scale helps overcome the parameter setting problem commonly encountered in various metaheuristics. [ABSTRACT FROM AUTHOR]

The dynamic spectrum management of cognitive technology can greatly increase the utilization of network spectrum, but while bringing convenience to our daily lives, this new technology will also greatly increase the consumption of nodes. At present, the short video industry is growing rapidly with the development of smart phones. Among them, the emergence of online popular music singing teaching platforms (mainly software for shooting short videos with music as the background) has brought significant changes to people's lifestyles. We can learn from the three-stage theory of flow experience to design and explore the method of plan implementation, and propose a targeted interaction design strategy for each flow characteristic, so that the overall design strategy becomes more practical and practical. In the design of this paper, the principle of cognitive wireless sensor network is applied in the technical aspect and the principle of interactive design is applied in the product design. When a user reaches a state of flow while using the online popular music singing teaching platform, it can be evaluated that the user experience of the app is the best. The research on the interaction design of the online pop music singing teaching platform under the flow experience is intended to propose an interactive design strategy that can guide the online pop music singing teaching platform to better create a flow experience for users. [ABSTRACT FROM AUTHOR]

The primary objective of airport management worldwide is always to make it easier to provide transportation services and minimize latency. This could be accomplished by controlling the movement of travelers through the airport's different checkpoints for passports, baggage handling, customs, and both departure and arrival lobbies. As one of the biggest passenger terminals around the world and among the most attractive destinations for visitors during the Hajj pilgrimage, this paper concentrates on enhancing the movement of travelers in the King Abdulaziz International Airport's pilgrimage station in the Kingdom of Saudi Arabia. Several optimization methods are used to better schedule the phases within the airport terminals as well as the assignment of arriving flights to vacant airport portals. These include the differential evolution algorithm (DEA), harmony search algorithm, genetic algorithm (GA), flower pollination algorithm (FPA), and black widow optimization algorithm. The findings demonstrated the potential sites for the development of airport stages, which may assist decision-makers in improving operational efficiency in the future. The simulation results showed that GA was more efficient in most of the experiments than the alternative algorithms for small population sizes in terms of the quality of the solutions obtained and the convergence rates. In contrast, DEA performed better in the larger population sizes. The outcomes also showed that FPA performed better than its rivals in identifying the optimal solution in terms of the overall duration of passenger waiting time. [ABSTRACT FROM AUTHOR]

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results. [ABSTRACT FROM AUTHOR]

This paper introduces a planning mechanism for optimal feeder reconfiguration, DSTATCOM allocation, and sizing in unbalanced radial distribution systems. The minimization of total active power losses (TACPL) is considered as the main objective of this work. A three phase forward–backward sweep load flow algorithm is utilized as a subroutine for the computation of the TACPL. The seagull optimization algorithm (SOA) is used as the solution methodology for this mixed integer non-linear planning problem. The simultaneous feeder reconfiguration, DSTATCOM installation obtains significant reduction in in the 19- and 25- bus unbalanced distribution network. Furthermore, the bus voltage magnitudes (pu) is also found to be enhanced. The SOA obtains lower TACPL in comparison to some of the well-known optimization algorithms like, differential evolution algorithm (DEA), moth-flame optimization (MFO) algorithm, and Jaya optimization algorithm (JOA). The proposed technique also attains lower TACPL in comparison to some of the established techniques reported in the literature while determining the optimal location, and rating of the DSTATCOM considering TACPL minimization. [ABSTRACT FROM AUTHOR]

Among the distribution network planning problems, feeder routing is one of the most important and challenging issues. Feeder routing is defined as the optimal determination of the feeders' routes by minimizing the investment cost and satisfying the network constraints. To the best knowledge of the authors, solving feeder routing problem in multi-objective framework is rarely investigated in the previous literature. This paper develops a bi-objective modeling and optimization framework to efficiently solve the feeder routing problem of distribution network considering cost and power losses. Since feeder routing is a complex, high-dimensional and combinatorial optimization problem, a multi-objective differential crow search algorithm (MO-CSAdif) is proposed to obtain a well-distributed and widely spread Pareto front. On a real large distribution network, simulation results confirm the usefulness of the proposed optimization framework. It can be drawn that MO-CSAdif can be a promising method to optimally solve the feeder routing problem. [ABSTRACT FROM AUTHOR]

This paper suggests day-ahead scheduling of microgrid with and without demand side management considering hydrogen storage and plug-in electric vehicles. This is a highly constrained mixed integer nonlinear programming problem which is solved by using quasi-oppositional fast convergence evolutionary programming (QOFCEP) technique. The concept of the quasi-opposition-based learning is incorporated in fast convergence evolutionary programming (FCEP) to improve the efficiency and quality of the solution. QOFCEP employs quasi-oppositional based learning (QOBL) for population initialization and generation jumping. The studied microgrid comprises three diesel generators, one mini-hydro power plant, two wind turbine generators, two solar PV plants, one hydrogen storage system and plug-in electric vehicles. Simulation outcomes of the suggested QOFCEP technique have been compared with those obtained by FCEP and differential evolution (DE). It is seen from numerical results that the cost obtained with DSM is about 0.81% lower than the cost obtained without DSM. It is also seen that the cost obtained from QOFCEP is about 0.52% and 0.78% lower than the cost obtained from FCEP and DE, respectively. The suggested QOFCEP technique has the ability to bestow superior-quality solution. [ABSTRACT FROM AUTHOR]

The explosion of wireless network services has led to a need for new technologies and resource allocation schemes. Device-to-device (D2D) can reduce the access pressure of base station (BS) equipment, and non-orthogonal multiple access (NOMA) can multiplex network spectrum resources. The integration of them can further improve the performance of multi-cell networks. However, users still face more interference in multi-cell networks. This paper analyzes the relationship between transmission power and serial interference cancellation decoding order. Aiming at maximizing the sum rate of D2Ds, we propose three feasible frameworks based on the differential evolution (DE) algorithm. First, we invoke a coevolution DE-based resource allocation (CDRA) framework, which encodes the RB assignment and power allocation into the same individual for evolution. Second, we propose an iteration-combining DE-based resource allocation (IDRA) framework, which adopts a two-step DE algorithm to solve optimal power allocation and RB assignment scheme iteratively. Lastly, we invoke the power-repairing DE-based resource allocation (PDRA) framework, which can perform power repair on individuals with failed evolution. Simulation results demonstrate that: (1) the integration of D2D and NOMA techniques is capable of enhancing the achievable sum rate of D2Ds; (2) the three proposed frameworks for multi-cell NOMA networks can effectively optimize the objective function. Compared with CDRA and IDRA, PDRA performs better in terms of convergence speed and maximum D2D rate. [ABSTRACT FROM AUTHOR]

Niching techniques have been widely incorporated into differential evolution (DE) to improve the diversity of the search. Existing niching-based DE schemes, however, typically employ a certain niching technique during the entire evolution. Since different niching techniques possess different diversity preserving properties, employing a fixed niching technique during DE evolution may have limited performance. In this paper, we propose an adaptive niching selection-based DE for global optimization problems. In the proposed method, instead of employing a certain fixed niching technique, an adaptive niching selection scheme has been devised. In this scheme, multiple niching techniques are employed and adaptively used during the DE evolution, thus properly preserving the population diversity. Further, to appropriately facilitate the adaptive niching selection, both the fitness improvement and entropy of population resulting from niching techniques have been considered to measure their effectiveness. The performance of the proposed method has been evaluated on multi-modal and hybrid composition test functions and compared with related methods. The results show that our proposed method can deliver a satisfying performance and is competitive with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces an improved mutation and crossover approach (to prevent untimely convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) is inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6- and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10- and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances. [ABSTRACT FROM AUTHOR]

A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. The constructed Ls-AOA algorithm was then proposed to design a proportional-integral-derivative (PID) controller employed in a functional electrical stimulation (FES) system for the first time. The initial statistical and convergence profile assessment showed better performance of the proposed algorithm. The comparative analyses for transient and frequency responses were performed for the PID-controlled FES system using the original AOA, sine–cosine and particle swarm optimization algorithms and the traditional Ziegler-Nichols tuning scheme. Similarly, the FES system tuned with the latter methods was also assessed for disturbance rejection and noise elimination. All the comparative analyses demonstrated that the proposed Ls-AOA has the greater capability for the challenging biomedical FES system. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms (MOEAs) have been successfully employed to solve many scientific and engineering problems. However, many algorithms perform ill in maintaining diversity and convergence simultaneously. In this paper, we devised a novel operator selection framework based on two collaborative indicators, generational distance (GD) and maximum spread (MS) to improve the diversity while maintaining a good convergence. By calculating the variation of GDs and MSs over the past 7 iterations, an instruction is conveyed to select a proper operator to execute next 7 iterations. This process is repeated until it reaches the maximum iteration. Two operators are embedded in this algorithm which are differential evolution operator (DE/rand/1) and our proposed crow search operator which is deemed to be efficient in explorating the search space. MOEA/D is utilized as the basis framework of our proposed algorithm. Experiments indicate that our proposed algorithm is valid and outperforms other famous algorithms in terms of diversity and convergence. In the end, a particular aeroengine maintenance scheduling problem is solved by our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramér–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the geological adaptive control of tunneling boring machine (TBM). To deal with the issue that the geological condition is uncertain, clustering analysis is used to identify geological types based on two important indices indicating the strength of rock. Based on the results from above, the principles of variation and range of tunneling parameters under different rock condition are determined. Furthermore, considering several vital performances of TBM during tunneling operation, a multi-objective optimization problem is proposed. In the light of non-dominated sorting and crowded distance evaluation concepts in non-dominated sorting genetic algorithm-II, the proposed multi-objective optimization problem is solved by using differential evolution algorithm. Based on the practical construction data, the simulation results show that the proposed method is effective in improving the performance of tunneling operation of TBM, compared with unoptimized operating actions from current systems. [ABSTRACT FROM AUTHOR]

This paper proposes an improved denoising method based on the cascaded arrangement of filters. The different combinations of filters are obtained optimally through the improved performance of the salp swarm algorithm and cascading four filters out of twelve different types of filters. The searching ability of standard salp swarm algorithm is enhanced following the strategies in differential evolution, and hence the algorithm is named as differential evolution-based salp swarm algorithm (DESSA). Most of the existing image denoising algorithms are suitable to remove either Gaussian, Salt & Pepper, or Speckle noise. Alternatively, due to the optimal combination of filters in the cascaded arrangement, the proposed denoising method exhibits its effectiveness in the removal of all three noises and the denoised images are better in terms of both quantitative analysis and visual quality. The denoising performance of the proposed method is also tested on the mixed noise which demonstrates the significant improvements compared to state-of-the-art algorithms. Further, the experiment on CEC 2014 benchmark functions indicates that the proposed DESSA achieves better optimal solutions than existing algorithms. [ABSTRACT FROM AUTHOR]

Research that focuses on the performance improvement of metaheuristics is important to gain understanding on the characteristic of specific algorithm. Better understanding of metaheuristics method will bring benefit when it comes to implement the methods to solve real problems. This paper studies the effect of local information sharing on the performance of soccer game optimization with suitable control parameter settings. A novel method, called soccer game optimization with local information sharing (SGOLS), has been proposed in this paper. The method implements local information derived from several nearby players to conduct move forward. Therefore, the move forward will consider the position of the ball dribbler, the previous best player position as well as the position of players nearby. The proposed method is evaluated based on 20 unconstraint continuous problems, consisting of unimodal function and multimodal functions, and compared to SGO, PSO and DE. The experiment result reveals that the local information sharing could enhance intensification search in SGOLS. The proposed methods perform better in high-dimensionality problems than SGO, PSO and DE. However, the use of local information consumes more computational times than the SGO and PSO, but it is still faster than DE. [ABSTRACT FROM AUTHOR]

Generally, the performance of evolutionary algorithms (EAs) greatly depends on the setting of their control parameters. Many adaptive setting technologies have been developed to solve the time-consuming task of parameter settings. However, the adaptive technologies for the grey prediction evolution algorithm (GPE), which was proposed by Hu et al. in 2020 with two control parameters, have not been investigated. This paper proposes four adaptive GPE algorithms according to three typical adaptive technologies, i.e. probability distributions, statistics and mathematical models in the field of EAs. Four proposed adaptive GPE are separately based on the uniform distribution (aGPEu), normal distribution (aGPEn), average fitness (aGPEa) and chaotic sequences (aGPEc). The performance of the four proposed adaptive GPE algorithms is evaluated on CEC2014, CEC2017 benchmark functions and a suite of eight engineering constrained design problems. The experimental results show the superiority and competitiveness of all the four proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, weighted differential evolution (WDE)-based evolutionary heuristic computing is presented for parameter estimation of nonlinear dynamics of the electrically stimulated muscle system based on a Hammerstein model structure as an element of rehabilitation program for the stroke patient. The approximation theory is defined on the basis of mean square error of actual and estimated responses for the system's fitness function. Optimization technique based on WDE is employed to find the optimal parameters of ESM model with sigmoidal, polynomial and spline as input nonlinear functions. Statistical analysis verified the worth of WDE as computational heuristic technique for identification of electrically stimulated muscle system. [ABSTRACT FROM AUTHOR]

With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, an efficient multi-modality image fusion model is proposed to fuse multi-modality medical images. To tune the hyper-parameters of the proposed model, a multi-objective differential evolution is used. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. Performance of the proposed model is compared with nine competitive models over fifteen benchmark images. Performance analyses reveal that the proposed model outperforms the competitive fusion models. [ABSTRACT FROM AUTHOR]

Autonomous air combat is an inevitable trend in the development of unmanned combat aerial vehicle (UCAV) equipment. Its purpose is to generate maneuver trajectory so that UCAVs obtain better air combat situation. Therefore, aiming to solve a tactical maneuver trajectory optimization problem for an UCAV in autonomous air combat, this paper proposed a novel method by converting the problem to an optimization problem of characteristic parameters. On the one hand, the paper analyses the tactical maneuver trajectory and combines the situation evaluation model to construct a tactical maneuver trajectory optimization function based on characteristic parameters. On the other hand, multi-population rotation strategy differential evolution (MPRDE) algorithm is designed to search for the optimal characteristic parameters. The experimental results showed that the MPRDE algorithm has outstanding performance in convergence speed, global optimization ability and robustness, and the method based on characteristic parameters could effectively and quickly represent the tactical maneuver trajectory of UCAV by using MPRDE. Meanwhile, it satisfies the real-time requirements for generating tactical manoeuvring trajectory for UCAV autonomous air combat. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-population adaptive version of inflationary differential evolution algorithm. Inflationary differential evolution algorithm (IDEA) combines basic differential evolution (DE) with some of the restart and local search mechanisms of Monotonic Basin Hopping (MBH). In the adaptive version presented in this paper, the DE parameters CR and F are automatically adapted together with the size of the local restart bubble and the number of local restarts of MBH. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The algorithm has been extensively tested over more than fifty test functions from the competitions of the Congress on Evolutionary Computation (CEC), CEC 2005, CEC 2011 and CEC 2014, and compared against all the algorithms participating in those competitions. For each test function, the paper reports best, worst, median, mean and standard deviation values of the best minimum found by the algorithm. Comparisons with other algorithms participating in the CEC competitions are presented in terms of relative ranking, Wilcoxon tests and success rates. For completeness, the paper presents also the single population adaptive IDEA, that can adapt only CR and F, and shows that this simpler version can outperform the multi-population one if the radius of the restart bubble and the number of restarts are properly chosen. [ABSTRACT FROM AUTHOR]

This paper proposes the use of the Differential Evolution algorithm with fuzzy logic for parameter adaptation in the optimal design of fuzzy controllers for nonlinear plants. The Differential Evolution algorithm is enhanced using Type-1 and Interval Type-2 fuzzy systems for achieving dynamic adaptation of the mutation parameter. In this paper, four control optimization problems in which the Differential Evolution algorithm optimizes the membership functions of the fuzzy controllers are presented. First, the experiments were performed with the original algorithm, second the experiments were performed with the Fuzzy Differential Evolution (in this case the mutation parameter is dynamic), and last, experiments were performed applying noise to the control plant by using Fuzzy Differential Evolution. [ABSTRACT FROM AUTHOR]

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter FS to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed. [ABSTRACT FROM AUTHOR]

Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers' perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has been applied to solve various optimization problems due to its simplicity and high search efficiency. However, researchers have confirmed that it still has some shortcomings such as premature convergence and slow convergence, especially when dealing with complex optimization problems. To address these concerning issues, this paper proposes a hybrid particle swarm-differential evolution algorithm (HPSDE). Firstly, to enhance the optimization performance, a modified updating scheme named particle-swarm mutation strategy is designed and an improved control parameters adaption is developed. Then, DE/rand-to-rand/1 mutation strategy is adopted to increase the population diversity and enhance the ability of particles escaping away from local optima. To achieve an improved DE variant with rapid convergence and fine stability, a random mutation framework is designed to combine the two mutation strategies mentioned above. To evaluate the efficiency of HPSDE algorithm, four different experiments have been taken on twenty-nine benchmark functions. The numerical results validate that HPSDE has better overall performance than the other competitors. Additionally, HPSDE is successfully applied to solve five typical engineering optimization problems. [ABSTRACT FROM AUTHOR]

Automatic tooth arrangement and path planning play an essential role in computer-aided orthodontic treatment. However, state-of-the-art methods have some shortcomings: low efficiency, excessive cost of displacements or collisions and insufficient accuracy. To address these issues, this paper proposes an innovative orthodontic path planning method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, the tooth model is preprocessed to obtain initial pose in which each tooth is segmented and built up with an oriented bounding box. Next, the target pose of each tooth is determined through the ideal dental arch curve and the optimal jaw principle. Finally, the path from the initial pose to the target pose of each tooth is planned based on IGWO, which is improved mainly from three aspects: (1) The greedy idea is adopted to initialize the gray wolf population based on dental interpolation. (2) The linear convergence factor in the traditional gray wolf optimization algorithm (GWO) is replaced with a nonlinear convergence factor. (3) We propose a position update strategy based on a dynamic weighting approach, which introduces a learning rate for each gray wolf. The experimental results show that our OPP-IGWO method outperforms the state-of-the-art methods. Compared with improved genetic algorithm, multiparticle improved swarm optimization, normal simplified mean particle swarm optimization algorithm, improved artificial bee colony algorithm, chaotic gray wolf optimization algorithm, hybrid gray wolf optimization algorithm and differential evolution algorithm, random walk gray wolf Optimization algorithm and reinforcement learning gray wolf optimization algorithm, the OPP-IGWO has an improvement on performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively. [ABSTRACT FROM AUTHOR]

Basic science is becoming more computing intensive with the incremental need for large-scale computing and storage resources. Cloud computing provides great potentials for hosting and executing scientific applications, which can be represented as workflows for automatic execution and run time provisioning. However, workflow scheduling is highly challenging under dynamic cloud environments since certain runtime QoS. In this paper, we propose an improved swarm search algorithm, i.e., an Owl Search Algorithm embedded with a newly designed Mutation strategy (OSAM) for scheduling workflows with makespan minimized under budget constraints. A population update mechanism, where each particle is updated in term of the impact of current best solution, is modified for an Owl Search Algorithm (OSA) to address discrete sequential optimization problems. We then adjust a step parameter β such that it decreases adaptively with the number of iterations to enhance OSAM's convergence speed. To further increase the diversity of population and enlarge OSAM's global search ability, we embed a mutation strategy into OSA's evolutionary process for better balancing between exploitation and exploration. A series of experiments are conducted to verify the proposed algorithm by comparing with benchmarking algorithms over well-known scientific workflows with different types and sizes through WorkflowSim. Experimental results show that in almost all the cases, the proposed OSAM outperforms the existing algorithms in the solution quality and constraint satisfiability, i.e., it can find near-optimal solutions that meet the tight and loose budget constraints within an acceptable time interval, especially in addressing large-scale applications, e.g., its RSR values are at least 0.10 greater than other algorithms in these cases. Besides, a nonparametric statistical hypothesis test, i.e., Wilcoxon signed-rank test, is applied to the resulting solutions, and the testing results demonstrate that our OSAM is significantly different from its peers. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants. [ABSTRACT FROM AUTHOR]

Mutation is the main operator of differential evolution (DE), as it is responsible for combining the information of distinct solutions to generate a donor vector. Aiming at improving the search effectivity of DE, previous research incorporated the calculation of centroids into the DE mutations. In some of the existing methods, the centroids are simply calculated as the center of some selected solutions (or, the entire population); in other cases, one-step clustering is used to perform a local search, or the centroids themselves are used as actual solutions in the population. As opposed to these methods, in this paper we extend some traditional DE mutation strategies to incorporate centroids calculated by deterministic hierarchical clustering. Experimental results on two sets of well-known benchmark problems show that the proposed cluster-centroid-based mutation strategies outperform, in general, the traditional rand/1 strategy, as well as several metaheuristics from the literature. Therefore, the use of clustering is an effective way to improve the search performance that could be exploited also in other population-based metaheuristics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a heuristic-based algorithm to improve the initial seeding of the k-means clustering algorithm. The proposed algorithm primarily aims to improve the initial choice of the centroids used by the k-means algorithm and also ensure that the requisite number of clusters is always returned in every run of the algorithm. Thus, the use of the proposed algorithm significantly reduces the possibility of k-means converging to a locally optimal solution. The paper explores the genetic algorithm framework to obtain the original seed points and couples this with the use of the differential evolution heuristic to obtain the requisite number of clusters. We have examined the performance of the proposed algorithm in the case of clustering text documents as such corpus often have significantly large number of data points and also require the formation of a large number of clusters. The results obtained have been compared with basic implementations of the k-means algorithm using standard parameters. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimization problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distributions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17 benchmark functions, and the experimental results demonstrated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness landscape, then speeds up convergence to the global optimum, and avoids falling into local optima. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has been widely applied to complex global optimization problems. Different search strategies have been designed to find the optimum conditions in a fitness landscape. However, none of these strategies works well over all possible fitness landscapes. Since the fitness landscape associated with a complex global optimization problem usually consists of various local landscapes, each search strategy is efficient in a particular type of fitness landscape. A reasonable approach is to combine several search strategies and integrate their advantages to solve global optimization problems. This paper presents a new self-feedback strategy differential evolution (SFSDE) algorithm based on fitness landscape analysis of single-objective optimization problem. In the SFSDE algorithm, in the analysis of the fitness landscape features of fitness-distance correlation, a self-feedback operation is used to iteratively select and evaluate the mutation operators of the new SFSDE algorithm. Moreover, mixed strategies and self-feedback transfer are combined to design a more efficient DE algorithm and enhance the search range, convergence rate and solution accuracy. Finally, the proposed SFSDE algorithm is implemented to optimize soil water textures, and the experimental results show that the proposed SFSDE algorithm reduces the difficulty in estimating parameters, simplifies the solution process and provides a novel approach to calculate the parameters of the Van Genuchten equation. In addition, the proposed algorithm exhibits high accuracy and rapid convergence and can be widely used in the parameter estimation of such nonlinear optimization models. [ABSTRACT FROM AUTHOR]

Since the highly diversified consumer demands and changing market environment pose a great challenge for the automobile industry, this paper presents an extreme learning machine and knowledge base-based dynamic scheduling method to solve the dynamic scheduling problem of material handling for mixed-model assembly lines based on line-integrated supermarkets. First, the dynamic material handling scheduling problem is described and a mathematical model is established to maximize the weight sum of the throughput of the assembly line and the number of logistics workers under the condition of variable product ratio and weights of scheduling criteria, considering the random failure of the equipment and the instability of the cycle time. Subsequently, an extreme learning machine and knowledge base-based dynamic scheduling method is constructed, consisting of a knowledge base-based scheduling rule selection and an extreme learning machine (ELM)-based product mix matching method. Afterward, considering the defects of extreme learning machine, an elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) is proposed to optimize the parameters of ELM. Elite opposition-based learning and self-adaptive operators are employed to the EOADE-ELM to improve the performance. Finally, the simulation results prove the feasibility and effectiveness of the proposed dynamic scheduling method in the dynamic scheduling process. [ABSTRACT FROM AUTHOR]

Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel Harris hawks optimization (HPHHO) algorithm is proposed to address the problem. HPHHO aims to enhance the performance of existing Harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem. [ABSTRACT FROM AUTHOR]

The widely employed mutation operator D E / c u r r e n t - t o - p b e s t / 1 in the differential evolution algorithm (DE) is further developed to a new version D E / c u r r e n t - t o - p b e s t / 1 - X in this paper. To test its performance, it has been embedded in the novel successful history-based adaptive DE (L-SHADE) and compared with other recently proposed mutation operators. In D E / c u r r e n t - t o - p b e s t / 1 - X , the updated parameter memories in each generation are not adopted when the initial value can still maintain an acceptable successful rate of finding better offspring. Also, the generated worse offsprings with acceptable fitness values are partially archived to generate differential vectors. The experimental results show that D E / c u r r e n t - t o - p b e s t / 1 - X has a comparable performance than D E / c u r r e n t - t o - p b e s t / 1 , D E / c u r r e n t - t o - o r d _ p b e s t / 1 and D E / c u r r e n t - t o - o r d _ b e s t / 1 . [ABSTRACT FROM AUTHOR]

Artificial Bee Colony Optimization Algorithm (ABCA) is a powerful optimization scheme that is suitable for a number of complex applications in which iteratively the best solution is to be created from the viable candidate solution. This ABCA applicability can be used as an ad hoc vehicle for minimizing DDoS attacks. A Variant Artificial Bee Colony Algorithm (VABCA) is available in this paper for optimizing the selection of a vehicle node for substitution of the damaged DDoS vehicle node. VABCA is an improved ABCA version which uses two search strategies based on differential evolution in the onlooker bee and an integrated Chaotic and opposition learning in scout bee. The principal goal of VABCA is to increase the global optimum detection point in DDoS attacks and to have a good degree of convergence rate and efficiency in order to distinguish the best solutions from the workable solutions. The VABCA simulation findings show that DDoS mitigation is potent by encouraging an approximately 22% rate higher in convergence than in the comparative research baseline mitigation schemes. [ABSTRACT FROM AUTHOR]