Search

Novel vertical take-off and landing advanced air mobility aircraft which are overactuated and transition between vertical and forward flight modes pose unique challenges for the design of safe and robust full-envelope fly-by-wire flight control systems. This paper presents a methodology for designing and optimizing a control system architecture for a tilt-wing urban air mobility concept. It features the total energy control system algorithm, which is extended to be applicable to hover and transitioning flight and explicit model following inner loops. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on closed-loop dynamic stability and control response characteristics. Control system performance is demonstrated by simulation of lateral, longitudinal, and directional maneuvering for hover, transition, and forward flight conditions, followed by simulation of departure and arrival transitions while tracking a prescribed flightpath and speed profile in calm and turbulent air. The results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

Optimization problems aim to minimize or maximize an objective function while fulfilling related constraints. This objective function may be a single or multi-objective optimization. Many studies have been conducted on using these optimization problems in civil and construction engineering, especially for the various machine learning techniques and algorithms that have been developed for fiber reinforced polymer (FRP) applications in the rehabilitation and design of RC structures. FRP is considered the most effective and superior technique for strengthening and retrofitting due to its significant benefits over traditional methods, which have numerous drawbacks, as well as the importance of structural strengthening as a cost-effective and practical option. In this research, an insight into how to apply algorithms and machine learning approaches to optimize FRP applications in civil and construction engineering is presented, as well as a detailed analysis of the various optimization strategies used and their findings. A total of 18 case studies from previous research were discussed and critically evaluated, and they were categorized into six groups according to the algorithm or machine learning technique utilized. Based on the case studies investigated in this study, the genetic algorithm was found to be the optimal algorithm utilized for optimizing FRP applications. The result of this research provides a useful guideline for future researchers and specialists. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of next generation communication. Resource scheduling for virtual network function service chain (VNF-SC) mapping is the key issue of the NFV. A virtual network function service chain placement multi-objective optimization model and algorithm based on improved genetic algorithm is proposed. Firstly, a multi-objective optimization model, which minimizes deployment cost, transmission delay and maximizes the load balance, is established. On this basis, an improved genetic algorithm based on MOEA/D is proposed to solve the established multiobjective model. In this algorithm, the combination scheme and mapping scheme of the service request are coded by the two-level coding method in the mapping process, and then the improved sparrow search algorithm is used to obtain the service function chain deployment scheme of the request and calculate the mapping weight. In addition, an efficient individual generation strategy is proposed to generate some superior individual. Finally, some simulation experiments are conducted and the experimental results show that the algorithm can effectively reduce deployment cost and transmission delay than the compared algorithm. [ABSTRACT FROM AUTHOR]

This study presents an enhanced model based on a new swarm intelligence algorithm called jellyfish search optimizer (JSO). The suggested model is called chaotic jellyfish search with enhanced swap operator (CJSESOS). The CJSESOS algorithm has two modifications to the original JSO algorithm, the first is the chaotic sequence generated by iterating a logistic map which is named CJSO. This enhancement discovers a creative solution by directing particles to different location of the search space, the second is enhanced swap sequence operator which increased the CJSO algorithm's ability to escape from local minimum by diversifying the results. The performance of the CJSESOS is evaluated using fourteen benchmark functions. The proposed model is applied to solve a discreate real life problem, named team formation (TF) problem which considered one of the most significant problems in computer science and optimization. TF problem is defined as creating the most effective team of experts in social network to carry out a task with the lowest possible cost. The proposed CJSESOS algorithm was tested for solving the TF problem with varying number of skills in different datasets. In addition, the proposed algorithm is compared to well-known optimization algorithms such as jellyfish search optimizer (JSO), particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), heap based optimizer (HBO), aquila optimizer (AO) and pelican optimization algorithm (POA). The simulation results show that the proposed model outperformed all the compared algorithms on the term of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance. [ABSTRACT FROM AUTHOR]

In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance. [ABSTRACT FROM AUTHOR]

Considering that the evolution operators of common optimization algorithms such as genetic algorithm has a poor convergence speed and solution accuracy in the interior ballistic performance optimization of artillery, an improved firefly algorithm is selected for the interior ballistic performance optimization of cased telescoped ammunition (CTA). It is applied to the interior ballistic mathematical model of CTA to carry out single‐objective optimization and multi‐objective optimization respectively. The results show that in the single‐objective optimization, the muzzle velocity obtained by using the improved firefly algorithm is larger than other commonly used optimization algorithms. Under the constraints of maximum chamber pressure and relative burnout point, the muzzle velocity can be increased by about 30 m/s. In the multi‐objective optimization, three sub‐objectives of muzzle velocity, ballistic efficiency and muzzle pressure are considered respectively, and the multi‐objective firefly algorithm is applied to comprehensively optimize the interior ballistic performance of CTA. Then, the results are compared with the commonly used multi‐objective optimization algorithms, such as non‐dominated sorting genetic algorithm version II (NSGA‐II) and non‐dominated sorting genetic algorithm version III (NSGA‐III), the solution results are a series of Pareto optimal solutions. The obtained optimization results improved the interior ballistic performance and launch safety, providing reference for the reasonable matching of charge structure parameters and the selection of optimization algorithm upon the interior ballistic performance optimization of CTA to some extent. [ABSTRACT FROM AUTHOR]

In order to improve the economy of the multi energy system and the efficiency of energy utilization, the research adopts the non dominated sorting genetic algorithms II (NSGA-II) to expand the population space. The elite strategy is introduced to improve the intelligent algorithm, and then the diversity of the population is retained to improve the optimization accuracy of the algorithm. In addition, the adaptive operator is introduced to improve the NSGA-II algorithm to improve the global search efficiency. The performance test of fast non dominated sorting genetic algorithm shows that the improved algorithm using elite strategy has better performance in coverage index, diversity index and convergence index. For example, in terms of convergence index, the improved NSGA-II algorithm has improved 0.0159, 0.822, 0.0243 and 0.0171 in four ZDT test functions. On the energy optimization operation for the integration of wind and hydrogen, the improved NSGA-II algorithm has obtained lower cost, with a total configuration cost of 606 million yuan, while the total system configuration cost corresponding to the unimproved NSGA-II algorithm is 624 million yuan, so the total system cost after the algorithm improvement has decreased by 18 million yuan. Therefore, this method has a better economy and higher energy efficiency. [ABSTRACT FROM AUTHOR]

Nonuniform irradiance on the Photovoltaic (PV) array would cause mismatch problem and decrease the generated power. To relieve the negative effects of partial shading, reconfiguring the interconnection of the PV array is a favorable solution. However, the current reconfiguration techniques are usually limited by different corresponding factors, e.g., not suit for various instantaneous shade cases, the shortage of experts' knowledge or large amounts of data, easy coverage to local optima, and the strict assumptions for application. Therefore, in this paper a unsupervised adaptive PV array reconfiguration technique is proposed with a Divide and Conquer Q-Learning (DCQL) based array reconfiguration scheme designed to decrease the influence of partial shades. To testify the performance of the proposed PV array reconfiguration scheme, mathematic theoretical evaluation and electric characteristic analyses are made and compared with those obtained by the conventional Total-Cross-Tied (TCT) arrangement, SuDoKu connection, Genetic Algorithm (GA) based scheme, as well as some lately proposed Grasshopper Optimization Algorithm (GOA), Harris Hawks Optimizer (HHO), and Lo Shu (LS) technique based reconfiguration methods. Besides, additional estimation indices, including mismatch loss, fill factor, percentage power loss and percentage power enhancement are calculated with substantial analyses and comparisons. Subsequently, the application of the proposed scheme in real-time conditions is evaluated, with energy saving and income analysis estimated in comparison with other eminent challenger reconfiguration schemes. At last, a comprehensive qualitative comparison assessment, with respect to dispersion ability under partial shading conditions (PSC), sensor requirement, wiring complexity and capital investment is carried out and proves the versatility of proposed methodology. • States, actions and rewards are designed in the Q-Learning based reconfiguration. • The combination of divide and conquer principle fastens the decision making process. • The proposed reconfiguration is estimated better performance through variety contrasts. [ABSTRACT FROM AUTHOR]

In order to solve the problem of power supply frequency instability after large‐scale grid connection of new energy, a multi‐objective programming optimization algorithm for large‐scale grid connection of new energy considering frequency stability is proposed. Before planning, the limit evaluation method for grid connection of new energy considering the frequency stability of the power system is used to judge the frequency change limit of the power system to be connected to the grid. Through the real‐time measurement method of power system frequency under the large‐scale grid connection of new energy, the real‐time change value of power system frequency under the large‐scale grid connection of new energy is calculated. Combined with the mastered frequency change limit and real‐time frequency change value of power system, an optimization decision‐making model for multi‐objective planning of large‐scale grid connection of new energy is constructed, and the improved genetic algorithm is used to solve the grid connection strategy that the frequency control quantity does not exceed the limit and the auxiliary cost of multi‐objective planning for grid connection is the minimum. It is verified that the frequency of IEEE‐30 standard node system is stable and within the limit after the algorithm is used, and the auxiliary cost of multi‐objective planning for grid connection is significantly reduced. [ABSTRACT FROM AUTHOR]

Confronting the challenges provided by jumbo container vessels, managers of world-class seaports must take into consideration water depths-related constraints when making the vessel berthing decisions. That is, when the water depths satisfy the requirement of jumbo vessels' drafts, these vessels are allowed to sail through the navigation channel. In this paper, we study a tactical berth allocation problem under the consideration of time-varying water depths. For the resolution, we propose an integer programming model, a set partitioning formulation and an improved differential evolutionary algorithm. Computational experiments demonstrate that the set partitioning formulation outperforms the integer programming model and can be applied to solve small- and medium-sized problems. The improved differential evolutionary algorithm is recommended for large-sized problems. [ABSTRACT FROM AUTHOR]

The purpose of this article is to develop a competitive supply chain network (SCN) in the face of uncertainty. The objective of the leader chain is to maximize total network profits by strategically locating suppliers, manufacturers, distribution centers, and retailers. Additionally, the follower chain seeks to maximize the network's profit. Both factors, optimal flow allocation to different echelons of the SCN and product pricing, are examined in the leader chain and follower chain. The KKT conditions are used in this article to convert a bi-level model to a one-level model. Additionally, a fuzzy programming technique is used to control the problem's uncertain parameters. According to the results obtained using the fuzzy programming technique, increasing the uncertainty rate increases demand while decreasing the OBFV and average selling price of products. Finally, the problem was untangled using a novel hybrid genetic and ant-lion optimization algorithm (HGALO). The results of problem solving in larger sizes demonstrate HGALO's superior efficiency in comparison with the other algorithm. [ABSTRACT FROM AUTHOR]

Many new, nature-inspired optimization algorithms are proposed these days, and these algorithms are gaining popularity day by day. These algorithms are frequently preferred for these real-world problems as they need less information, are reliable and robust, and have a structure that can easily be applied to discrete problems. Too many algorithms result in difficulty choosing the correct technique for the problem, and selecting an unwise method affects the solution quality. In addition, some algorithms cannot be reliable for some specific real-world problems but very successful for others. In order to guide and give insight into the practitioners and researchers about this problem, studies involving the comparison and evaluation of the performance of algorithms are needed. In this study, the performances of six nature-inspired methods, which included five new implementations of differential evolutionary algorithms (DE), scatter search (SS), equilibrium optimizer (EO), marine predators algorithm (MPA), and honey badger algorithm (HBA) applied to land redistribution problem and genetic algorithms (GA), were compared. In order to compare the algorithms in detail, various performance indicators were used as problem based and algorithm based. Experimental results showed that DE and SS algorithms have a more successful performance than the other methods by solution quality, robustness, and many problem-based indicators. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time. [ABSTRACT FROM AUTHOR]

The multi-energy complementary microgrid system has multi-dimensional variables and multiple constraints, which leads to the complex and high-cost calculation of the system comprehensive annual cost. In the calculation process, because the system needs power balance, it is easy to cause the problem that the output of various equipment exceeds the specified limit. In view of this problem, an improved particle swarm optimization algorithm is proposed. The algorithm comprehensively considers the power balance of electricity-heat-cooling multiple loads in the system, and introduces a secondary limit on the basis of the particle swarm algorithm. The algorithm pulls the equipment output beyond the power limit back to the constraint boundary, and dynamically optimizes the reduction coefficient of the equipment output to avoid the over-limit problem caused by the internal power balance of the system. Through the analysis of examples, the genetic algorithm, the algorithm before the improvement, and the algorithm after the improvement are applied to the multi-energy complementary microgrid system, and the equipment output and the comprehensive annualized cost in different scenarios are compared and analyzed. The experimental results prove the effectiveness and universal applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. However, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations were suggested to overcome this problem. This paper presents a modified grasshopper optimization algorithm (MGOA)-based genetic algorithm to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the control parameter, Cmax, to escape from the local minimum and move the search process to an improved point. Parameter C is one of the essential parameters in GOA, where it balances the exploration and exploitation of the search space. These modifications aim to speed up the convergence rate by reducing the repeated solutions and the number of iterations. Both the original GOA and the proposed algorithms are tested with 19 main test functions to investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve five different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Promising results are achieved compared to the original GOA. The proposed approach shows an average percentage of improvement of 96.18 as illustrated in the detailed results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important model of machine learning, Bayesian networks (BNs) have received a lot of attentions since they can be used for classification via probabilistic inference. However, since it is a complicated combination optimization problem, BN structure learning cannot be solved with classic convex optimization algorithms. Hence, evolutionary algorithms provide an alternative way to find a global solution to BN structure learning problem. In this paper, we improve the biased random-key genetic algorithm to solve the BN structure learning problem. Meanwhile, we apply a local optimization model as its decoder to improve the performance of the proposed algorithm. Finally, we conduct our experiments on nine benchmark networks and a real dataset of cross-site scripting (XSS) attack. Experimental results show that the proposed algorithm can obtain more accurate solutions than other state-of-the-art algorithms and achieve a good performance in XSS attack detection for web security. [ABSTRACT FROM AUTHOR]

A methodology considering Chaboche's isotropic‐kinematic hardening (CIKH) model and genetic algorithm optimization technique is examined here to simulate the cyclic‐plastic response for materials exhibiting cyclic hardening or softening behavior. The aim of the present report is to illustrate the inherent potential of this approach by assessing the closeness of fit of the obtained predictions to the experimental results for several materials like Z2CND18.12N, and 25CDV4.11 steels, OFHC copper, and INCONEL718 alloy. The obtained results were further subjected to comparative examinations with the available predictions to demonstrate the adoptability of the suggested methodology for the cyclic hardening or softening materials. This proposed approach provides a single set of CIKH model parameters to replicate the stabilized hysteresis loops and cyclic hardening or softening behavior under symmetric strain‐controlled loading, as well as ratcheting characteristics under asymmetric stress‐controlled cycling, and is thus generalized in nature. [ABSTRACT FROM AUTHOR]

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system. [ABSTRACT FROM AUTHOR]

The optimization algorithm is a very fast method for modeling magnetic anomalies from an ideal geological model. In addition, these models can be used to explore and estimate mineral deposits. However, in exploratory geophysics, it is common to use modeling methods with regular geometric shapes to estimate anomalous parameters such as shape, material, depth, and elongation angles. In this study, the parameters of the magnetic model are estimated using a hybrid PSO-GA method, which is an evolutionary algorithm based on a combination of particle swarm optimization (PSO) and genetic algorithms (GA). In this method, the PSO algorithm improves the magnetic data, while the GA modifies the decision to estimate the model parameters. Moreover, the balance between exploration and exploitation capabilities improves the performance of this method by combining genetic operators in the hybrid PSO-GA algorithm. Here, Gaussian white noise has been added to the artificial data with different percentages in the range of 0–25% to analyze the error of the obtained model. The results show that the proposed method can provide valuable outcomes in estimating model parameters up to 25% noise. Moreover, the accurate data from an aeromagnetic survey in the Basiran region in South Khorasan province are used to validate the estimated model parameters. The results show that the estimation of the parameters obtained from the current method is consistent with the parameters given from the commercial software and the published geological results. [ABSTRACT FROM AUTHOR]

Copyright of Inventum. Ingenieria, Tecnologia e Investigacion is the property of Corporacion Universitaria Minuto de Dios and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms' performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA. [ABSTRACT FROM AUTHOR]

The multidepot capacitated arc routing problem (CARP) is investigated with the hybrid optimization algorithm of the Dijkstra algorithm and genetic algorithm. The complex multidepot CARP is transformed into multiple single depot CARP by systematic clustering analysis. After completing the system clustering, the Dijkstra algorithm is used to adjust the boundary arc locally and merge it to a reasonable depot, while in the genetic algorithm, the structure of the chromosome is reset to use the path as the way of real coding, and the elite selection is used to decode to obtain the optimal path optimization scheme. Finally, Lanzhou road network data as experimental data, through Matlab to achieve the practicability of the algorithm in sprinkler applications. The results show that the improved genetic algorithm can successfully solve the multi-segment CARP with a certain road network scale, ensuring the correctness and feasibility of the algorithm. In addition, the efficiency of the algorithm in the later iteration is basically controlled at about 0.5 seconds, indicating that the efficiency of the algorithm is worth identifying. [ABSTRACT FROM AUTHOR]

It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of “duplicate overrides” and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the original monarch butterfly optimization algorithm is easy to fall into the local optimal solution and has poor convergence, an adaptive monarch butterfly optimization algorithm based on mutation reverse learning is proposed. Firstly, the mutation idea of genetic algorithm is combined with the reverse learning strategy to replace the original transfer operator to improve the global convergence. Then, an adaptive strategy is integrated into the adjustment operator of the original monarch butterfly optimization algorithm to make the population more diverse. Finally, Cauchy mutation will be carried out among the last five monarch butterflies in the updated population, which will generate greater disturbance near the mutated individuals and make the whole population search for optimization in a larger range. In order to verify the improved monarch butterfly optimization algorithm, benchmark function and Wilcoxon rank sum test are used to test it. The experimental results show that the convergence speed and optimization accuracy of the improved algorithm are greatly improved. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An optimal and smooth trajectory for industrial robots has a positive impact on reducing the execution time in an operation and the vibration in their joints. In this paper, a methodology for the time-optimal and jerk-continuous trajectory planning of industrial robots is proposed. The entire trajectory is interpolated in the joint space utilizing fifth-order B-splines and then optimized by a hybrid whale optimization algorithm and genetic algorithm (WOA-GA). Two objective functions, including the integral of the squared jerk along the entire trajectory and the total execution time, are minimized to obtain the optimal entire trajectory. A fifth-order B-spline interpolation technique enables the achievement of a jerk-continuous trajectory, while respecting the kinematic limits of jerk, acceleration and velocity. WOA-GA is utilized to solve the time-jerk optimal trajectory planning problem with nonlinear constraints. The proposed hybrid optimization algorithm yielded good results and achieved the time-jerk optimal trajectory better under kinematic constraints compared to the genetic algorithm, whale optimization algorithm, improved whale optimization algorithm with particle swarm optimization and adaptive cuckoo search algorithm. The numerical results show the competent performances of the proposed methodology to generate trajectories with high smooth curves and short total execution time. [ABSTRACT FROM AUTHOR]

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-Objective Dragonfly Algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms. [ABSTRACT FROM AUTHOR]

The study of optimization methods for reliability-redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability-redundancy allocation problems. Results from a literature survey show the best results found for series, series-parallel, bridge, and applied case problems (e.g., over-speeding gas turbine benchmark). Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance. However, this improvement has been difficult to achieve for high-reliability applications. Insights and future challenges in reliability-redundancy allocation problems optimization are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Consecutive-k-out-of-n (Con/k/n) system, a reconfigurable system, can improve the system performance by adjusting the redundancy and assignment of components. This paper aims to determine the optimal defensive strategy of Con/k/n systems under external risks. The defensive capability of Con/k/n systems is evaluated based on real-time system reliability, and a defensive importance measure (DIM) is constructed to optimize components’ redundancy locally. To solve the proposed optimization model effectively, a DIM-based genetic algorithm (DIGA) is developed by integrating the advantages of DIM’s local search with the global search ability of the classical genetic algorithm (CGA). The numerical experiment under 36 scenarios illustrates that DIGA is more effective than CGA verified by average defensive capability, robustness, and convergence generations. Moreover, the redundancy distribution analysis of Con/k/5 systems in the optimal defensive strategy shows that the redundancy of F(G) systems is in a spaced (continuous) way under spacing k-1 risk or continuous k risk. [ABSTRACT FROM AUTHOR]

As compared with other optimization algorithms (e.g., genetic algorithm, ant colony algorithm, and particle swarm algorithm), FA is relatively simple to be realized. It does not require strict continuous and differentiable conditions, requires less prior knowledge. However, it still cannot effectively avoid slow convergence and poor stability. To optimize FA for the attraction model, a new FA with mean condition partial attraction is proposed (mcFA) in this paper. McFA, characterized by fast computing power, high precision, and easy implementation, is capable of remedying the defect that the FA is easy to converge slowly. As opposed to standard FA, mcFA has determined excellent model parameter values, and the mean condition partial attraction model is more suitable for different dimensional solutions than the full attraction model. Lastly, as verified by the theoretical and experimental results, mcFA outperforms other algorithms on most of the test functions. Moreover, the mean condition partial attraction model is shown to yield better solutions than the full attraction model. [ABSTRACT FROM AUTHOR]

To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. [ABSTRACT FROM AUTHOR]

A Dandelion algorithm (DA) inspired by the seed dispersal process of dandelions has been proposed as a newly intelligent optimization algorithm. For improving its exploration ability as well as reducing the probability of its falling into a local optimum, this work proposes to add a novel competition mechanism with historical information feedback to current DA. Specifically, the fitness value of each dandelion in the next generation, which is calculated by linear prediction, is compared with the current best dandelion, and the loser is replaced by a new offspring. Current DA generates new offsprings without considering historical information. This work improves its offspring generation process by exploiting historical information with an estimation-of-distribution algorithm. Three historical information models are designed. They are best, worst, and hybrid historical information feedback models. The experimental results show that the proposed algorithms outperform DA and its variants, and the proposed algorithms are superior or competitive to nine participating algorithms benchmarked on 28 functions from CEC2013. Finally, the proposed algorithms demonstrate the effectiveness on four real-world problems, and the results indicate that the proposed algorithms have better performance than its peers. [ABSTRACT FROM AUTHOR]

In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed. [ABSTRACT FROM AUTHOR]

Conventional recommender systems often utilize similarity formulas to identify similarities between active users and others to predict the rating of the unseen items. Existing optimization algorithms seek to find the weights and coefficients affecting these similarities. Our proposed method, implemented in R in the GACFF package, shifts away from this view and directly uses the continuous genetic algorithm to find optimal similarities in big data (e.g., Movielens 1M and Netflix datasets) to improve the performance of user‐based collaborative filtering recommendation systems. First, by identifying the users who are the nearest neighbors along with their number, the number of genes in a chromosome is determined. Each gene represents the similarity between a neighboring user and an active user. This genetic algorithm is independent of the size of the data. Our method provides optimal solutions more quickly by estimating the starting points. Moreover, the genetic metric provides better results and recommendations than previous ones in terms of runtime and quality measures (i.e., mean absolute error, coverage, precision, and recall). [ABSTRACT FROM AUTHOR]

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidirectional mutation genetic BPNN algorithm is applied to the intelligent optimization of English teaching courses. The simulation shows that the multidirectional mutation genetic BP neural network algorithm can solve the local optimization problem of traditional BP neural network. Finally, a control group and an experimental group are set up to verify the role of multidirectional mutation genetic algorithm and its optimization neural network in the intelligent optimization system of English teaching courses through the combination of summative and formative teaching evaluations. The data show that MMGA-BP algorithm can significantly improve the scores of academic students in English courses and has better teaching performance. The effect of vocabulary teaching under the guidance of MMGA-BP optimization theory is very significant, which plays a certain role in the intelligent curriculum optimization of the experimental class. [ABSTRACT FROM AUTHOR]

In this article, a novelvertical wire-bonding interconnect structure is intelligently modeled and optimized with general artificial neural network (GRNN) and genetic algorithm (GA) for multilayered system in package. A compensation structure is constructed with a hybrid inductive and capacitive technique, while a capacitive stripline with series inductive short-end via is designed underneath the 50-Ω transmission line. The GRNN algorithm is employed to build the electromagnetic model databases during the procedure of GA optimization. In comparison with the conventional optimization algorithm, the output performances can be directly achieved with collaboratively combined methods, which can significantly reduce the calculation time. From the measurement results, the return loss is improved significantly while the parasitic inductive behavior of the bonding wire is eliminated with the presented design. Moreover, compared with the traditional compensation techniques, no additional area is occupied on the surface plane of the wire-bonding interconnection, which is more suitable for the high-integrated circuit system. [ABSTRACT FROM AUTHOR]

In this paper, pixelated Frequency Selective Surface (FSS) layer embedded in two carbonyl iron-filled silicon rubber (CISR) sheets are used for design of broadband microwave absorber in frequency range of 3.95 to 8.2 GHz. Top and bottom layers of CISR sheets are prepared with 24% and 33% of Carbonyl Iron (CI) powder by volume, respectively. Reflectivity of microwave absorber, complex permittivity and complex permeability of CISR sheets are evaluated using two Rectangular Dielectric Waveguide (RDWG) measurement systems in 3.95 to 5.85 GHz frequency band (WR-187) and 5.85 to 8.2 GHz frequency band (WR-137). An algorithm was developed using Geometry-Refinement Method (GRM) and Genetic Algorithm (GA) optimization technique to design pixelated FSS layer. Measured as well as simulated reflectivity of composite microwave absorber is better than −10 dB at normal incidence in the frequency range of 3.95–8.2 GHz. For oblique incidence from 0 ∘ to 40 ∘ , simulated reflectivity of TE and TM polarizations carried out for all angles of incidence. [ABSTRACT FROM AUTHOR]

A decentralized control strategy can effectively solve the control problem of the large-scale time delayed structures. In this paper, combining the overlapping decentralized control method, linear matrix inequality (LMI) method, and H∞ control algorithm, overlapping decentralized H∞ control approach of the time delayed structures has been established. The feedback gain matrixes of all subsystems are obtained by this method based on genetic algorithm optimization tools and the specific goal of optimization control. The whole vibration control system of the time delayed structures is divided into a series of overlapping subsystems by overlapping decentralized control strategy. The feedback gain matrixes of each subsystem can be obtained by using H∞ control algorithm to calculate each subsystem. The vibration control of a twenty layers' antiseismic steel structure Benchmark model was analyzed with the numerical method. The results show that the proposed method can be applied to control system with time delay. The overlapping decentralized control strategies acquire the similar control effects with that of the centralized control strategy. Moreover, the flexibility of the controller design has been enhanced by using overlapping decentralized control strategies. [ABSTRACT FROM AUTHOR]

The discounted {0–1} knapsack problem may be a kind of backpack issue with gathering structure and rebate connections among things. A moth-flame optimization algorithm has shown good searchability combined with an effective solution presentation designed for the discounted {0-1} knapsack problem. A new encoding scheme used a shorter length binary vector to help reduce the search domain and speed up the computing time. A greedy repair procedure is used to help the algorithm have fast convergence and reduce the gap between the best-found solution and the optimal solution. The experience results of 30 discounted {0-1} knapsack problem instances are used to evaluate the proposed algorithm. The results demonstrate that the proposed algorithm outperforms the two binary PSO algorithms and the genetic algorithm in solving 30 DKP01 instances. The Wilcoxon rank-sum test is used to support the proposed declarations. [ABSTRACT FROM AUTHOR]

Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people's personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time. [ABSTRACT FROM AUTHOR]

In order to solve the unfair individual payment costs problem in the low-altitude unmanned aerial vehicle (UAV) conflict resolution process, a multi-UAV conflict resolution algorithm based on the cooperative game concept "coalition complaint value" is proposed. Firstly, based on the low-altitude multi-UAV conflict scene characteristics, according to the "coalition complaint value" concept, the UAV conflict resolution payment matrix is established. Secondly, combined with the advantages of the artificial potential field (APF) method and the genetic algorithm (GA), a hybrid solution strategy for conflict resolution based on APF-GA is proposed. The final simulation results show that the APF-GA hybrid solution strategy has the best efficiency by combining the three evaluation indicators of calculation time, feasibility, and system efficiency. The reliability of the proposed algorithm is verified based on the Monte Carlo algorithm. The solution strategy based on the cooperative game "coalition complaint value" can improve individual fairness to a certain extent. At the same time, it can achieve the rapid planning goal with priority drones at the expense of a small amount of overall benefits. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal. [ABSTRACT FROM AUTHOR]

In this study, we develop a 3D geostatistical seismic inversion method based on turning bands simulation and co-simulation to estimate acoustic impedance (AI) from seismic and well data. The proposed method uses an iterative approach based on cross-over concept in genetic algorithm optimization to perform global stochastic inversion. The objective function of the optimization algorithm is the measure of correlation coefficients between the modeled and observed seismic data. In the first iteration of the proposed algorithm, the seismic cubes corresponding to the AI realizations generated by the turning bands simulation are compared to the observed seismic data. Subsequently, the local areas of AI models producing the highest correlation coefficients to the observed seismic data are merged to construct a new supplementary cube that is further used as the secondary variable in the turning bands co-simulation for the next iteration. Generation of the seismic cubes and picking the best AI cube via cross-over genetic algorithm approach is performed iteratively until convergence to a constant correlation coefficient between the modeled and observed seismic data. The proposed method is applied to a synthetic dataset that includes 20 wells with known AI logs and 3D stacked seismic data. According to the results, the algorithm converges to a constant correlation coefficient after a few iterations. In addition, it is observed that employing multi-attribute analysis outputs (meta-attributes) during turning bands co-simulation in the initialization step would improve the final global correlation coefficient from 0.774 to 0.815. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) algorithm has certain disadvantages; for instance, the convergence viscosity of the algorithm is reduced in the post evolution phase, the optimization search efficiency is also reduced, the algorithm is easy to be inserted with a local extremum during the calculation of a complex problem of a high-dimensional multiple extremum, and the convergence thereof is low. To compensate for the PSO disadvantage, we propose a particle swarm optimization of the comprehensive improvement strategy, which is a simple particle swarm optimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second-order to a first-order difference equation. The evolutionary process is controlled by the particle position variables only. The hybridization operation of increasing the extremum disturbance and introducing a genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and the plurality of a comparative experiment provide the following information: the improved particle swarm optimization is a simple and effective optimization algorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity. [ABSTRACT FROM AUTHOR]