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Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

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]

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for selective harmonic elimination in a cascaded multilevel inverter (MLI) using teaching-learning based optimization (TLBO). The main objective in selective harmonic elimination (SHE) strategy is to eliminate low-order harmonics by solving nonlinear equations and reaching optimal solution, while the fundamental component is satisfied. In this paper, different optimization methods are compared. In this paper, the TLBO as a recently emerged nature-inspired algorithm is presented to provide better results for the SHE in comparison with Genetic algorithm (GA), Particle swarm optimization (PSO), Newton Raphson Method (NR) and Satin Bowerbird algorithm (SBO). For better comparison of those methods and influence of optimal DC source a 7-level inverter is chosen and MATLAB/Simulink software is used for optimization. Simulation results show the superiority of TLBO, higher precision and probability of convergence than other mentioned algorithms. [ABSTRACT FROM AUTHOR]

A key area of wireless communications is visible light communication which uses solid-state light-emitting-diode. This paper shows, a comparative study to optimize the received signal strength indication (RSSI) in the optical wireless link between two optimization algorithms. The optimization algorithm we used in the paper is a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. To, compute the maximum RSSI over the non-line-of-sight (NLOS) link, within an indoor environment, and to find the optimum NLOS route through distinct angles to achieve better communication quality, we have used those algorithms. Simulations were performed at matlab to check the feasibility of the scheme proposed. Also, we have compared the efficiency of the both system. In addition, GA and PSO detect the maximum RSSI of the receiver (Rx) in a specific direction instead of calculating RSSI at any random angle. The simulation result shows that the proposed system precisely detects the direction that provides an optimal RSSI for the Rx. This paper concludes that the GA is computationally effective while compared with PSO. After several iterations, the result estimates that the average elapsed time of the GA is 0.21 ms and the PSO is 30 ms, respectively. In the case of user positions 1 and 2, the percentage enhancement for GA as compared to PSO is almost 4.2% and 12.5% respectively. [ABSTRACT FROM AUTHOR]

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Power distribution is one of the major areas of electrical engineering. The issue of optimized power distribution is of great concern and here it is dealt with as a single- and multi-objective problem. We know that evolutionary algorithms have better efficiency in solving such problems. In this paper, we have applied heuristics non-dominated sorting genetic algorithm II (NSGA-II, multiple objective optimization algorithm) to optimize functions such as corona loss, efficiency, potential drop, resistive loss, and volume of the conductor. The NSGA-II has outperformed other algorithms involving the optimal solution. NSGA-II is not only simple in terms of programming but also achieves the desired high-quality optimal solutions in fewer iterations. After our experiment, we have optimized the various functions presented in this paper. [ABSTRACT FROM AUTHOR]

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 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]

In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid ac–dc microgrids. A novel two-stage iterative approach is proposed. In the first stage, a meta-heuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and, therefore, can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of ac and dc generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data. [ABSTRACT FROM AUTHOR]

Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of MaOEAs. Unfortunately, it is not easy to constantly maintain a population of solutions with both convergence and diversity. In this paper, an MaOEA based on two independent stages is proposed for effectively solving many-objective optimization problems (MaOPs), where the convergence and diversity are addressed in two independent and sequential stages. To achieve this, we first propose a nondominated dynamic weight aggregation method by using a genetic algorithm, which is capable of finding the Pareto-optimal solutions for MaOPs with concave, convex, linear and even mixed Pareto front shapes, and then these solutions are employed to learn the Pareto-optimal subspace for the convergence. Afterward, the diversity is addressed by solving a set of single-objective optimization problems with reference lines within the learned Pareto-optimal subspace. To evaluate the performance of the proposed algorithm, a series of experiments are conducted against six state-of-the-art MaOEAs on benchmark test problems. The results show the significantly improved performance of the proposed algorithm over the peer competitors. In addition, the proposed algorithm can focus directly on a chosen part of the objective space if the preference area is known beforehand. Furthermore, the proposed algorithm can also be used to effectively find the nadir points. [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]

Proper fixture design is crucial to workpiece quality assurance in manufacturing. Incorrect fixture design may lead to workpiece deformation during machining. The fixture configuration design is one of the important aspects of fixture design. This paper deals with fixture layout optimization problem. The objective is to minimize the norm of all the passive contact forces satisfying Coulomb friction constraint, work-piece static equilibrium constraint and contact constraint, for the entire cutting operation. To solve this problem, the paper proposes Genetic Algorithm with Learning Automata (GALA) algorithm, which is a population based interconnected learning automata algorithm incorporating genetic operators. The algorithm enjoys the good characteristics of both GA and LA. It is validated with an example of face milling operation. The optimal layout is found to be in tune with empirical facts. Also, for the further investigation of the algorithm, it has been tested on a different problem sets and a comparative study is carried out. [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 a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic algorithm (R-NSGA-II) was proposed for such problem-solving tasks. R-NSGA-II aims to finding Pareto-optimal points close, in the sense of Euclidean distance in the objective space, to the supplied aspiration points, instead of finding the entire Pareto-optimal set. In this paper, R-NSGA-II method is modified using recently proposed Karush–Kuhn–Tucker proximity measure (KKTPM) and achievement scalarization function (ASF) metrics, instead of Euclidean distance metric. While a distance measure may not produce desired solutions, KKTPM-based distance measure allows a theoretically-convergent local or global Pareto solutions satisfying KKT optimality conditions and the ASF measure allows Pareto-compliant solutions to be found. A new technique for calculating KKTPM measure of a solution in the presence of an aspiration point is developed in this paper. The proposed modified R-NSGA-II methods are able to solve as many as 10-objective problems as effectively or better than the existing R-NSGA-II algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. [ABSTRACT FROM AUTHOR]

Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis. [ABSTRACT FROM AUTHOR]

This study designs a new variant of the capacitated vehicle routing problem (CVRP) under a fuzzy environment. In CVRP, several vehicles start their journey from a central depot to provide services to different cities and finally return to the depot. This paper introduces an additional time beyond the service time at each city to fulfill the pre-ordered demands. The need for this excess service time is to provide the services to new customers who are not enlisted at the start of the process. It is a market enhancement step. The proposed model's main objective is to find the maximum time-dependent profit by using the optimum number of vehicles in an appropriate route and spending optimum excess service time in each city. The model considers travel time and travel cost as fuzzy numbers. An expected value model (EVM) is formulated using the credibility approach on fuzzy variables. A hybrid meta-heuristic method combining a genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) is designed to solve the proposed model. The proposed model is explained with the help of some numerical examples. Sensitivity analyses based on different independent parameters of the algorithms are also conducted. [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.)

Deep-water offshore drilling vessels, such as a semisubmersible drilling rig, use the dynamic positioning (DP) system and the thruster-assisted position-mooring system for maintaining a stationary position. In the DP system, the thrust allocator is used to distribute the desired generalized forces computed by the motion controller among the thrusters. However, to ensure safe operation of the vessel despite the thruster failure, the vessel is equipped with redundant thruster configuration and, therefore, is overactuated. For overactuated vessels, the choice of a particular solution for thrust allocation is found using some kind of optimization process. In this paper, the thrust allocator tries to minimize the power consumption and takes forbidden/spoil zones into account. The formulated thrust allocation problem becomes nonconvex due to thrust direction constraints on azimuth thrusters. The conventional methods get trapped in local minima and fail to find the optimum solution for the formulated nonconvex thrust allocation problem. In this paper, an improved harmony search (IHS) algorithm for solving the nonconvex thrust allocation problem is proposed. IHS is a variant of the harmony search (HS) algorithm. The HS algorithm is a music-based meta-heuristic optimization method, which is analogous with the music improvisation process where a musician continues to polish the pitches to obtain better harmony. Obtained numerical results show that the IHS algorithm has better convergence property when compared to the HS algorithm and the genetic algorithm (GA). Moreover, the power consumption for thrust allocation using the IHS algorithm is lower when compared with HS, GA, and Mincon (sequential quadratic programming) algorithms. The percentage savings in total power consumption for thruster allocation as compared to the Mincon algorithm for GA, HS, and IHS methods are 44.96%, 48.39%, and 51.58%, respectively. [ABSTRACT FROM PUBLISHER]

This paper demonstrates the application of particle swarm optimization (PSO) to the programming of pavement maintenance activities at the network level. Furthermore, the application of the PSO technique and its relevance to solving the programming problem in a pavement management system (PMS) is discussed. The robustness and quick search capability of PSO enables it to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of PSO in accommodating different forms of objective functions and comparing the results with the genetic algorithm (GA). This paper compares PSO and GA with respect to rate of convergence and accuracy of modeling PMS using an example problem. The results of this paper confirmed the potential of PSO to successfully model the PMS. [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]

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]

Summary: The optimization of indoor femtocell base stations (FBS) placement is a highly complex problem in which multiple performance figures can be considered. In this paper, we propose a biobjective optimization approach based on non‐dominated sorting genetic algorithm II (NSGA‐II) to solve the indoor FBS deployment problem in residential scenarios where a single cell is deployed within the coverage of a single dominant interfering external macro base station (MBS). The optimization problem is formulated with the objective of jointly minimizing outage probability and maximizing normalized downlink average throughput. Several conditions are simultaneously considered under the framework of Monte Carlo simulations, such as the effect of co‐channel interference in uplink and downlink, buildings with arbitrary layouts, realistic transmission schemes and antenna radiation patterns, random user distributions, and a well‐established empirical path loss model. The proposed algorithm, which determines the location and orientation of the FBS's antenna that provides a suitable compromise between the two objective functions, is evaluated in this paper. First, two different experiments are proposed to investigate the algorithm convergence; then, statistical analyses are formulated to characterize the relation between the values achieved for both objective functions; finally, the computational load is evaluated. Results indicate the convergence of the proposed method towards the real Pareto front and a significant reduction of the computational time with respect to a reference method based on exhaustive search, while pointing at new criteria for the selection of the objective functions. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is a core problem in many applications. Various optimization algorithms such as genetic algorithm and particle swarm optimization (PSO) have been used to solve this problem, where all of them are adapted to work in the bioinformatics domain. This paper defines the MSA problem, suggests a novel MSA algorithm called 'NestMSA' and evaluates it in two domains: Web data extraction and removing different URLs with similar text (DUST). The suggested algorithm is inspired by the PSO optimization algorithm. It is not a generalization of a two-sequence alignment algorithm as it processes all the sequences at the same time. Therefore, it looks globally at the same time on all sequences. Different from other PSO-based alignment algorithms, swarm particles in the proposed NestMSA algorithm are nested inside the sequences and communicated together to align them. Therefore, global maximum is guaranteed in our algorithm. Furthermore, this work suggests a new objective function which both maximizes the number of matched characters and minimizes the number of gaps inserted in the sequences. The running time complexity and the efficiency of NestMSA are addressed in this paper. The experiments show an encouraging result as it outperforms the two approaches DCA and TEX in the Web data extraction domain (95% and 96% of recall and precision, respectively). Furthermore, it gives a high-performance result in the DUST domain (95%, 93% and 92% of recall, precision and SPS score, respectively). [ABSTRACT FROM AUTHOR]

Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop intelligent search heuristics to solve n-jobs, m-machines lot streaming problem in a flow shop with equal size sub-lots where the objective is to minimise makespan and total flow time independently. Improved sheep flock heredity algorithm (ISFHA) and artificial bee colony (ABC) algorithms are applied to the problem above mentioned. The performance of these algorithms is evaluated against the algorithms reported in the literature. The computational analysis shows the better performance of ISFHA and ABC algorithms. [ABSTRACT FROM AUTHOR]

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization. [ABSTRACT FROM AUTHOR]

In this paper, work concerning robust D-stability analysis is considered for discrete descriptor systems with structured uncertainties and multiple time delays. A genetic algorithm is used to make the stability test of the main result more efficient. A delay-dependent robust D-stability criterion is proposed to ensure that all poles of the discrete multiple time-delay descriptor systems subject to uncertainties are located within a disk contained in the unit circle. It will be seen that the system considered in this paper is more general and the proposed robust D-stability criterion is less conservative and easier to check than those presented in recent research. Moreover, a delay-dependent criterion is derived to guarantee that the system is regular, impulse-free, and D-stable. Since the boundary test occurs during the check of the criteria, a genetic algorithm is used to make it more precise. Finally, an example is introduced to illustrate our main results. [ABSTRACT FROM AUTHOR]

In the last decade a new variety of nature inspired optimization algorithms has been appeared. After the swarm based models, researchers turned their inspiration in nature phenomena and laws of science. In this way a new category of algorithms was born, equally effective or even sometimes superior to known algorithms for optimization problems, like genetic algorithms and swarm intelligence schemes. The present survey depicts the evolution of research on nature inspired optimization algorithms related to physical phenomena and laws of science and discusses the possibilities of using the presented approaches in a number of different applications. An attempt has been made to draw conclusions on what algorithm could be used in which different problem areas, for those approaches that this information could be extracted from the related papers studied. The paper also underlines the usage of this kind of nature inspired algorithms in industrial research problems, due to their better confrontation with optimization problems represented with nodes and edges. [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]

Space debris has become a great threat to space activities. We study and optimize space missions to remove multiple space debris. These missions are efficient and economical in stabilizing the orbital environment. In this paper, we propose an intelligent global optimization algorithm named the evolving elitist club algorithm (EECA), and we study its application in multiple debris removal missions. The crucial design element of EECA is the elitist club, which is a population created using ant colony optimization (ACO) and then evolved using genetic algorithm operators. We describe two types of multiple debris removal missions: 1) single spacecraft removal mission; and 2) multiple spacecraft removal mission. We optimize them using EECA. The proposed algorithm is compared with the conventional tree search method and other ACO algorithms. We show that EECA is more efficient than other algorithms. Furthermore, a time distribution (TD) EECA is proposed to handle time-dependent problems, and the time-continuous update mechanism of time-varying pheromone in TD EECA presents a novel idea for combinatorial-continuous problems. [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]

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about (where = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately previously has been demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. Two big advantages of the CMBGA are its simplicity and the fact that it requires the tuning of fewer parameters compared with other GA variants. [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 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]

This paper presents an elite opposition-based cognitive behavior optimization algorithm (ECOA). The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of ECOA compared to other algorithms in the literature. [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]

Power distribution systems (PDS) in urban areas suffer from different types of problems. One such major problem is accidental or scheduled interruption. In electrical networks, effects of interruptions are usually quantified using a set of reliability indices, namely the System Average Interruption Frequency Index and the System Average Interruption Duration Index. Installation cost (fixed cost) and cost due to temporary and/or permanent faults during interruptions (variable cost) are also major issues to be considered while achieving a cost efficient, fault-tolerant PDS. Formalization of an optimization problem that jointly minimizes the afore-mentioned reliability indices as well as the cost of a PDS by optimal allocation of different protective devices and switches has always been a challenging task. This paper presents a hybrid single as well as joint-objective function optimization technique to minimize different reliability indices (mixed integer minimization problems), as well as the operational cost of a PDS in urban areas. In the proposed technique, two well-known meta-heuristic search techniques, namely genetic algorithms (GA) and ant colony optimization (ACO), have been hybridized after modifying different participating operators. The effectiveness of the proposed algorithm is examined, and each PDS is tested in a different environment of constrained optimization. In addition, the presented simulation results are compared with existing approaches that solve this problem. The simulation results show the superiority of the proposed hybrid GA-ACO model, as compared to other established heuristic approaches. [ABSTRACT FROM AUTHOR]

In most real-world settings, designs are often gradually adapted and improved over time. Consequently, there exists knowledge from distinct (but possibly related) design exercises, which have either been previously completed or are currently in-progress, that may be leveraged to enhance the optimization performance of a particular target optimization task of interest. Further, it is observed that modern day design cycles are typically distributed in nature, and consist of multiple teams working on associated ideas in tandem. In such environments, vast amounts of related information can become available at various stages of the search process corresponding to some ongoing target optimization exercise. Successfully exploiting this knowledge is expected to be of significant value in many practical settings, where solving an optimization problem from scratch may be exorbitantly costly or time consuming. Accordingly, in this paper, we propose an adaptive knowledge reuse framework for surrogate-assisted multiobjective optimization of computationally expensive problems, based on the novel idea of multiproblem surrogates. This idea provides the capability to acquire and spontaneously transfer learned models across problems, facilitating efficient global optimization. The efficacy of our proposition is demonstrated on a series of synthetic benchmark functions, as well as two practical case studies. [ABSTRACT FROM AUTHOR]

The paper presents a genetic algorithm for multicriteria optimization of parameters of a technological process, which finds a solution not from a certain point but from a specified population. The objective functions are used without their derivatives and probabilistic choice rules are applied. The multicriteria optimization is based on finding a solution that simultaneously optimizes the machining parameters defined by functions of productivity, tool consumption, tool cost, etc. [ABSTRACT FROM AUTHOR]

The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers. [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]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]

Many problems today need the computing power that is only available by using large-scale parallel processing. For a significant number of these problems, the density of the global communications between the individual processors dominates the performance of the whole parallel implementation on a distributed memory multiprocessor system. In these cases, the design of the interconnection network for the processors is known to play a significant part in the efficient implementation of real problems. Important criteria to optimise the efficiency of a configuration are the maximum and average distance a message has lo travel between processors. Minimum path systems are irregular multiprocessor computer architectures which optimise these criteria. These architectures provide an efficient alternative to the more common regular topologies for solving real applications in parallel. This paper presents new results for two combinatorial problems that occur during the generation of these optimal irregular configurations. These are: (1) The design of the optimum interconnection network between the processors for configurations containing up to 128 processors. (2) The design of the routing tables to provide the optimal routing of messages within these irregular networks. The paper shows how these combinatorial problems have been solved, using genetic algorithms for the first problem and a random local search procedure for the second. It also includes a comparison with the results obtained for regular topologies, for example: hyper-cubes, tori, and rings. [ABSTRACT FROM AUTHOR]