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Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems. [ABSTRACT FROM AUTHOR]

Recently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown environment and restricted by some obstacles is one of the most problems facing the operation of drones. The problem of path planning is not only limited to searching for an appropriate path from the starting point to the destination but also related to how to choose an ideal path among all available paths and provide a mechanism for collision avoidance. By considering how to construct the best path, several related issues need to be taken into account, that relate to safety, obstacle avoidance, response speed to overtake obstacles, etc. Swarm optimization algorithms have been used to provide intelligent modeling for drone path planning and enable to build the best path for each drone. This is done according to the planning and coordination dimensions among the swarm members. In this paper, we have discussed the features and characteristics of different swarm optimization algorithms such as ant colony optimization (ACO), fruit fly optimization algorithm (FOA), artificial bee colony (ABC), and particle swarm optimization (PSO). In addition, the paper provides a comprehensive summary related to the most important studies on drone path planning algorithms. We focused on analyzing the impact of the swarm algorithm and its performance in drone path planning. For that, the paper presented one of the most used algorithms and its models employed to improve the trajectory of drones that rely on swarm intelligence and its impact on the optimal path cost of drones. The results of performance analysis for the ACO algorithm in a 3D and 2D-dimensional environment are illustrated and discussed, and then the performance evaluation of the ACO is compared to the enhanced ACO algorithm. The proposed algorithm achieves fast convergence, accelerating the process of path planning. [ABSTRACT FROM AUTHOR]

Computations that mimic nature are known as nature-inspired computing. Nature presents a wealthy source of thoughts and ideas for computing. The use of natural galvanized techniques has been found to provide machine solutions to complex problems. One of the challenging issues among researchers is high-dimensional data which contains a large number of unwanted, redundant, and irrelevant features. These redundant or unwanted features reduce the accuracy of machine learning models. Therefore, to solve this problem nowadays metaheuristic techniques are being used. The paper presents both surveys as well as comparison of five metaheuristic algorithms for feature selection. A wrapper-based feature selection approach using five nature-inspired techniques for feature selection has been applied. The binary version of the five swarm-based nature-inspired algorithms (NIAs), namely particle swarm optimization, whale optimization algorithm (WOA), grey wolf optimization (GWO), firefly algorithm, and bat algorithm. WOA and GWO are recent algorithms used for finding optimal feature subsets when there is no empirical information. The S-shape transfer function has been used to convert the continuous value to binary form and K-nearest neighbor is used to calculate the classification accuracy of selected feature subsets. To validate the results of the selected NIAs eleven benchmark datasets from the UCI repository are used. The strength of each NIA has been verified using a nonparametric test called the Friedman rank and Holm test. p value obtained shows that WOA is statistically significant and performs better than other models. [ABSTRACT FROM AUTHOR]

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particularly, in a knowledge spillover problem (KSP), cooperation strategies among more than hundreds of individuals need to be simulated. At the same time, the brain storm optimization (BSO) algorithm, which is a data-driven and model-driven hybrid paradigm, has the potential to simulate the complex behaviors in a group of simple individuals. In this paper, a modified BSO algorithm has been used to solve KSP from the perspective of evolutionary game theory. Knowledge spillover (KS) is the sharing or exchanging of knowledge resources among individuals. Firstly, the KS and evolutionary game theory were introduced. Then, the KS model and KS optimization problems were built from the evolutionary game perspective. Lastly, the modified BSO algorithms were utilized to solve KS optimization problems. Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. More efficient algorithms could be designed to solve different real-world evolutionary game problems. [ABSTRACT FROM AUTHOR]

This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a multi-objective fitness dependent optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose: classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC-2019) multi-modal multi-objective functions. MOFDO results are compared to the latest variant of multi-objective particle swarm optimization, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider. [ABSTRACT FROM AUTHOR]

Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented. [ABSTRACT FROM AUTHOR]

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper. [ABSTRACT FROM AUTHOR]

Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods. [ABSTRACT FROM AUTHOR]

One of the main concerns in rank aggregation tasks for metasearch service is how to retrieve and aggregate the large-scale candidate search results efficiently. Much work has been done to implement metasearch service engines with different rank aggregation algorithms. However, the performance of these metasearch engines can hardly be improved. In this paper, we transform the top-k ranking task into a multi-objective programming problem when user preferences are considered along with user queries. We build an improved discrete multi-objective programming model to make the aggregate rankings satisfy user queries and user preferences both, and then propose a user preferences-based rank aggregation algorithm accordingly. Based on discrete particle swarm optimization algorithm, we improve the encoding scheme, the initialization methods, the position and velocity definition, the integrating updating process, the turbulence operator, and the external archive updating and leader selection strategy to make sure the candidate results that fit the user's preferences can be located quickly and accurately in a large-scale discrete solution space. We have our proposed algorithm tested on three different benchmark datasets: a public dataset, the real-world datasets and the synthetic simulation datasets. The experimental results demonstrate the efficacy and convergence efficiency of the proposed algorithm over the baseline rank aggregation methods especially when dealing with large amount of candidate results. And when the set of candidate results is of normal size, the proposed algorithm is proved to perform not worse than the baseline methods. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

It may not be easy to implement complex AI models in edge devices without strong computing capacity (e.g. GPU). The Universal Approximation Theorem states that a shallow neural network (SNN) can represent any nonlinear function. In this paper, we focus on the learnability and robustness of SNNs, obtained by a greedy tight force heuristic algorithm [a Performance Driven Back-Propagation (PDBP)] and a loose force meta-heuristic algorithm [a variant of particle swarm optimization (VPSO)]. From the engineering prospective, all sensors are well justified for a specific task. Hence, all sensor readings should be strongly correlated to the target, and the structure of an SNN should depend on the dimensions of a problem space. The key findings of the research are summarized as follows: (1) The number of hidden neurons of an SNN depends on the nonlinearity of the training data, and the number of hidden neurons up to the dimension number of a problem space could be enough; (2) The learnability of SNNs, produced by error-driven PDBP, is always better than that of SNNs, optimized by error-driven VPSOs; (3) The performances of SNNs, obtained by PDBPs and VPSOs, do not change much for different training rates; and (4) Comparing with other classic machine learning algorithms, such as C4.5, NB and NN in literature, the SNNs, obtained by accuracy-driven PDBPs, win for all tested data sets, and the improvement percentage is up to 32.86%. Hence, the research could provide valued guidance for the implementation of edge intelligence. [ABSTRACT FROM AUTHOR]

Recently established Harris Hawks optimization (HHO) has natural behaviour for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris Hawks optimizer algorithm is poor. In the present research, an improved version of the Harris Hawks optimization algorithm, which combined HHO with Particle Swarm Optimization and named as ameliorated Harris Hawks optimizer algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, non-convex and highly constrained engineering design problem. In the proposed research, the exploitation phase of the existing HHO algorithm is improved using a particle swarm optimization algorithm and its performance tested for CEC2005, CECE2017 and CEC2018 benchmark problems. Also, discrete algorithms such as FFT algorithms, convolution and image processing algorithm use multiply and accumulate (MAC) unit as a critical component. The efficiency of a MAC is mainly dependent upon the speed of operation, power dissipation and chip area along with the complexity level of the circuit. In this research paper, a power-efficient signed floating-point MAC (SFMAC) is proposed using universal compressor-based multiplier (UCM). Instead of having a complex design architecture, a simple multiplexer-based circuit is used to achieve signed floating output. The 8 × 8 SFMAC can take 8-bit mantissa and 3-bit exponent. And therefore, the input to the SFMAC can be in the range of − (7.96875)10 to + (7.96875)10. The design and implementation of the proposed architecture is done on the Cadence Spectre tool in GPDK 90 nm and TSMC 130 nm technologies. The analysis has proved that the proposed SFMAC architecture has consumed the least power than the recent MAC architectures available in the literature. [ABSTRACT FROM AUTHOR]

Internet is a new type of information exchange tool that has developed with the times. Now, it has been integrated into all aspects of our study and life. At the same time, in the era of the rise of social media, there are more and more platforms on the Internet. A variety of critical texts have also exploded in these platforms. In these opinions and comments, the subjective opinions of the presenters are included, and the emotional tendencies of the commenters are expressed. Nowadays, subjective text information resources are huge. One of the major problems to be solved by management objects facing information management is how to manage them effectively so that users can quickly and accurately find the required information. Therefore, classifying these text tendencies and mining the potential value in the text has broad application prospects. Based on the text granularity and processing efficiency, this paper conducts multi-granularity emotional block partitioning on network texts and compares the sentiment analysis under different granularities. Furthermore, a random subspace integrated learning text sentiment classification method based on BPSO (binary particle swarm optimization) is proposed to analyze text orientation. By simulating news site comments and e-commerce website reviews such as Taobao, the convergence analysis of BPSO in the optimization of the number of base classifiers shows that the BPSO algorithm can be applied to the random subspace method well, and the accuracy of the classification results is high. [ABSTRACT FROM AUTHOR]

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases. [ABSTRACT FROM AUTHOR]

Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine–cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine–cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine–cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, several nature-inspired optimization methods have been proposed and applied on various classes of problems. The applicability of the recently developed socio-inspired optimization method referred to as multi-cohort intelligence (Multi-CI) is validated by solving real-world problems from manufacturing processes domain, viz. non-traditional manufacturing processes. The problems are minimization of surface roughness for abrasive water jet machining (AWJM), electro-discharge machining (EDM), micro-turning and micro-milling processes. Furthermore, the taper angle for the AWJM, relative electrode wear rate for EDM, burr height and burr thickness for micro-drilling, flank wear for micro-turning process, machining time for micro-milling processes were minimized. It is important to mention that for the micro-drilling and micro-milling process different tool specifications were used. In addition, for EDM the material removal rate was maximized. The performance of the algorithm has been validated by comparing the results with other variations of CI algorithm and several contemporary algorithms such as firefly algorithm, genetic algorithm, simulated annealing and particle swarm optimization. In AWJM, Multi-CI achieved 5–8% and 8–23% minimization for surface roughness and taper angle, respectively. For EDM, 47–80% maximization of material removal rate; 2–13% and 92–98% minimization of surface roughness and relative electrode wear rate, respectively, have been attained. Furthermore, for micro-turning 2% minimization of flank wear and for micro-milling, 2–6% minimization of machining time were attained. For micro-drilling, 24% and 16–34% minimization of burr height and burr thickness were attained. In addition, the performance is compared with the regression and response surface methodology approaches and experimental solutions. The analysis regarding the convergence of all the algorithms is discussed in detail. The contributions in this paper have opened up several avenues for further applicability of the Multi-CI algorithm for solving real-world problems. [ABSTRACT FROM AUTHOR]

Cloud computing provides infinite resources and various services for the execution of variety of applications to end users, but still it has various challenges that need to be addressed. Objective of cloud users is to select the optimal resource that meets the demand of end users in reasonable cost and time, but sometimes users pay more for short time. Most of the proposed state-of-the-art algorithms try to optimize only one parameter at a time. Therefore, a novel compromise solution is needed to make the balance between conflicting objectives. The main goal of this research paper is to design and develop a task processing framework that has the decision-making capability to select the optimal resource at runtime to process the applications (diverse and complex nature) at virtual machines using modified particle swarm optimization (PSO) algorithm within a user-defined deadline. Proposed algorithm gives non-dominance set of optimal solutions and improves various influential parameters (time, cost, throughput, task acceptance ratio) by series of experiments over various synthetic datasets using Cloudsim tool. Computational results show that proposed algorithm well and substantially outperforms the baseline heuristic and meta-heuristic such as PSO, adaptive PSO, artificial bee colony, BAT algorithm, and improved min–min load-balancing algorithm. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a well-known population-based stochastic optimization algorithm intended by collective and communicative behavior of bird flocks looking for food. Being a very powerful tool for obtaining the global optimal solution, PSO has experienced a multitude of enhancements during the last three decades. The algorithm has been modified, hybridized and extended by various authors in terms of structural variations, parameters selection and tuning, convergence analysis and meta-heuristics. In this article, hybridized PSO has been proposed to solve balanced transportation problem, a discrete optimization problem, of any number of decision variables converging to the global optima. Two additional modules have been embedded within the PSO, in order to repair the negative and/or fractional values of the decision variables, and tested with variants of parameters present therein. The proposed algorithm generates an optimal solution even without considering the rigid conditions of the traditional techniques. The paper compares the performance of different variants of inertia weight, acceleration coefficients and also the population size with respect to the convergence to the optimal solution. The performance of the proposed algorithm is statistically validated using the paired t test. [ABSTRACT FROM AUTHOR]

The paper proposes optimization of square split-ring resonator (SRR) metamaterial unit cell using modified hybrid bacterial foraging–particle swarm optimization (BF-PSO). Optimized metamaterial unit cells are loaded into novel designed square fractal antenna for its bandwidth enhancement. The presented research is alienated in three phases: Novel design of microstrip line-fed square fractal antenna with defected ground structure is proposed in the initial phase that provides dual band performance. In second phase, with the aim of bandwidth enhancement, quasi-static model of SRR unit cell is used to optimize its structural parameters so that optimized structure resonates at desired frequency region. Modifications are included in hybrid BF-PSO algorithm as per size constraints of SRR unit cell to be optimized and for improving the convergence behavior of algorithm. The performance of modified hybrid BF-PSO algorithm is assessed against four other evolutionary techniques named as classical BFO, chaos PSO, IWO and ABC. In later phase, optimized SRR unit cells are loaded into initially designed square fractal antenna that results in broadband performance suitable for upper S-band and lower C-band wireless applications. The designed square fractal antenna without and with SRR unit cells is fabricated and tested to verify the experimental results. [ABSTRACT FROM AUTHOR]

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs; in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm. [ABSTRACT FROM AUTHOR]

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO2, NOx, and CO2 are considered as separate three objectives, thus making it a four-objective (many-objective) optimization problem. A unit-wise price penalty factor is considered here to convert all the objectives into a single objective in order to compare the final results with other previously used methods like Lagrangian relaxation (LR), particle swarm optimization, and simulated annealing. QBA is applied in six-unit power generation system for four different loads. The obtained results show QBA successfully solve many-objective CEED problem with greater superiority than other methods found in the literature in terms of quality results, robustness, and computational performance. In the end of this paper, a detailed future research direction is provided based on the simulation results and its analysis. The outcome of this research demonstrates that the inclusion of quantum computing idea in metaheuristic technique provides a useful and reliable tool for solving such many-objective optimization problem. [ABSTRACT FROM AUTHOR]

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm. [ABSTRACT FROM AUTHOR]

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector. [ABSTRACT FROM AUTHOR]

To increase the quality of loans provision and reduce the risk involved in this process, several credit scoring models have been developed and utilized to improve the process of assessing credit worthiness. Credit scoring is an evaluation of the risk connected with lending to clients (consumers) or an organization. The Gustafson-Kessel (GK) algorithm has become one of the most valuable tools for credit scoring. However, this algorithm demonstrates a relatively poor capability to identify a subset of features from a large dataset. Most methods that use the GK algorithm require a predefined number of clusters. This paper presents a new GK-based modified binary particle swarm optimization (MBPSO) approach to increase the classification accuracy of the GK algorithm. The proposed MBPSO consists of three parts. First, the figure of particles is utilized to determine the optimal number of clusters automatically and overcome the drawback of the GK algorithm that requires a predefined number of clusters. A subset of features is identified because the same dataset may contain influencing features or a high level of noise. The two procedures are then combined in the same optimization method to increase the classification accuracy of the GK algorithm. Second, the updating function uses velocity and position to update the next position for every particle in the swarm. Third, a kernel fuzzy clustering method (KFCM) is used as the fitness function because this function can analyze high- dimensional data. These modifications are utilized as preprocessing steps before the classification of credit data is performed. Internal measures of clustering are conducted on Australian, German, and Taiwan standard datasets that contain 690, 1,000, and 30,000 instances, respectively, with several feature properties. Results show that the GK algorithm is good at separating the data into clusters. Furthermore, the fuzzy Rand validity measures of the three credit datasets derived by using the proposed method of combining the GK algorithm with a MBPSO are greater than the values of the two other compared methods. This finding means that fuzzy partitioning (classification) is robust therefore, the risk associated with loans provision can be reduced when the proposed method is used. [ABSTRACT FROM AUTHOR]

Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in early studies concerned with improving operational efficiency and waste minimization. In recent decades, however, JIT principles have also connected to logistics efficiency particularly for distribution of raw materials and finished goods. In the literature, several attempts have been made to optimize JIT logistics networks. On the one hand, most studies have typically focused on deterministic and small-scale problems which have been solved by exact algorithms. On the other hand, when large-scale problems were considered and usually were solved by metaheuristics algorithms, uncertainty sources and fine-tuning of the metaheuristics parameters were generally ignored. In this paper, we develop a mixed-integer linear optimization model to investigate a large-scale JIT logistics problem with 15 different sizes. To deal with different uncertainty sources, the customers demand and suppliers’ capacity as the two main sources of uncertainty in practice are considered as triangular fuzzy parameters. The proposed model aims to minimize total logistics cost including costs of transportation, inventory holding and backorders. A particle swarm optimization algorithm is applied to solve the problem, and its results are then validated by a harmony search algorithm. Both algorithms parameters are tuned using response surface methodology and Taguchi method. Finally, the conclusion and some directions for future research are proposed. [ABSTRACT FROM AUTHOR]

Application of back-propagation (BP) artificial neural network (ANN) as an accurate, practical and quick tool in indirect estimation of uniaxial compressive strength (UCS) of rocks has recently been highlighted in the literature. This is mainly due to difficulty in direct determination of UCS in laboratory as preparing the core samples for this test is troublesome and time-consuming. However, ANN technique has some limitations such as getting trapped in local minima. These limitations can be minimized by combining the ANNs with robust optimization algorithms like particle swarm optimization (PSO). This paper gives insight into development of a hybrid PSO-BP predictive model of UCS. For this reason, dataset comprising the results of 228 laboratory tests including dry density, moisture content, P wave velocity, point load index test, slake durability index and UCS was prepared. These tests were conducted on 38 sandstone samples which were taken from two excavation sites in Malaysia. Findings showed that PSO-BP model performs well in predicting UCS. Nevertheless, to compare the prediction performance of the PSO-BP model, the UCS is predicted using ANN-based PSO and BP models. The correlation coefficient, R, values equal to 0.988 and 0.999 for training and testing datasets, respectively, suggest that the PSO-BP model outperforms the other predictive models. [ABSTRACT FROM AUTHOR]

The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna. [ABSTRACT FROM AUTHOR]

Physics informed neural network (PINN) has become a promising method for solving partial differential equations (PDEs). The loss function of PINN is a weighted sum of multiple items. This makes it easy to fall into local optima, especially the gradient pathologies when solving high frequency problems. The value of penalty coefficients has a crucial impact on the prediction results. Therefore, a new PINN with adaptive penalty coefficients iteratively optimized by biased multiobjective coevolutionary algorithm (BC-PINN) is presented. In BC-PINN, a two-stage optimization mechanism is used to search for parameters of neural network and penalty coefficients respectively. This method involves constructing the fitness function of penalty coefficients based on the biased dominance ranking by data item and regularization item. Compared with the previous works of others, the accuracy of fitting the initial conditions and boundary conditions is considered to be given priority, which is more conducive to PINN converging to the particular solution of PDE. In addition, the set of penalty coefficients is divided into multiple populations to improve the optimization efficiency through coevolutionary algorithm. The empirical results show that: (1) Our method can improve the gradient pathologies and effectively capture the high-frequency features. (2) Compared to the original PINN, it reduces the MSE by 1–6 orders of magnitude in our benchmark functions. [ABSTRACT FROM AUTHOR]

Hybrid cloud platforms offer an attractive solution to organizations interested in implementing integrated private and public cloud applications to meet their profitability requirements. However, this can only be achieved by utilizing available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud while allocating others to the public cloud. In this context, the current work aims to minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. To date, several evolutionary algorithms have been applied to solve the challenging service placement problem by dealing with complex solution spaces to provide an optimal placement with relatively short execution times. In particular, the standard BPSO algorithm has been found to display a significant disadvantage, namely getting trapped in local optima and demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome the critical shortcomings associated with the standard BPSO, an enhanced binary particle swarm optimization (E-BPSO) algorithm is proposed, comprising a modification inspired by the continuous PSO for the particle position updating equation. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches using a real benchmark task in terms of both cost and execution time. [ABSTRACT FROM AUTHOR]

Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointed out that the relationship between the performance indicators of the Stirling engine and its input variables was nonlinear. This study analyses the prediction performance of power and torque in a Stirling engine system using soft computing techniques—artificial neural network (ANN) and Fuzzy Mamdani Model (FMM) and hybrid algorithms—adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO). The performance of these approaches has been discussed using a dataset from a test conducted on an existing Stirling engine. The performance indicators of the different models considering the power and the torque were predicted and analysed. A parametric analysis has been performed for the ANN-PSO model to identify the best model configuration considering the number of neurons in hidden layers, the number of swarm size and acceleration factors. A detailed description of the process leading to the identification of the best networks architecture for the power and torque model has been provided. The comparison of the four approaches indicates that FMM exhibits the highest performance prediction considering the power while the ANN-PSO and ANFIS model exhibit the highest performance considering the torque. This study demonstrates the suitability of soft computing techniques and hybrid algorithms for the prediction of Stirling engine characteristics and its potential to optimize time and experimental cost. [ABSTRACT FROM AUTHOR]

The experts' decisions and evaluating the patients' data are the most significant parts affecting the breast cancer analysis. For early breast cancer detection, numerous techniques of machine learning not only can assist in examining and diagnosis the medical data quickly but also decrease the potential errors that could be occurred due to inexpert or unskilled decision-makers. Support vector machine is one of the famous classifiers that has already made an important contribution to the field of cancer classification. However, configurations of different kernel function and their parameters can significantly affect the performance of the SVM classifier. To further improve the classification accuracy of the SVM classifier for breast cancer diagnosis, an intelligent cancer classification method is proposed based on selecting a feature subset and optimizing the relevant parameters (i.e., penalty factor parameter (c ) and kernel parameter γ ) of the SVM classifier concurrently through an intelligent algorithm using the Jaya algorithm. Then, this method (Jaya-SVM) was applied to precisely characterize the breast cancer dataset, including 699 samples, which are 458 and 241 for benign and malignant, respectively. Furthermore, to evaluate the effectiveness of the proposed Jaya-SVM classifier, it is compared in terms of the computational complexity and the classification accuracy with several combinatorial metaheuristic classifiers, namely the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and cuckoo search (CS) based-SVM. Apart from this, a Breast Cancer Coimbra Dataset taken from the UCI library is used to validate the effectiveness of the proposed method. The results are presented, explained, and conclusions are drawn. [ABSTRACT FROM AUTHOR]

A Gaussian based Particle Swarm Optimization Gravitational Search Algorithm (GPSOGSA) is being proposed for extensive feature selection that serves highly in making effective predictions. GPSOGSA helps to overcome the problem of being stuck into the local optima and influences the local searching ability, thus it aims to bridge the gap of exploration and exploitation. The algorithm also limits the usage of too many parameters like acceleration factors, maximum velocity, inertia weight that plays a vital role in PSO, GSA and PSOGSA. The efficacy of the algorithm has been tested upon unimodal and multimodal benchmark functions. We have also evaluated the performance of the algorithm by applying it on various benchmark datasets. The algorithm uses a wrapper-based approach that includes Support Vector Machine as a learner algorithm, and improves both the execution time and the performance accuracy. The findings show that the proposed algorithm could escape from local optimum and converges faster than the PSO, GSA and PSOGSA algorithms. [ABSTRACT FROM AUTHOR]

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets. [ABSTRACT FROM AUTHOR]

The underwater sonar image has the characteristics of complex background and heavy noise pollution. By using multi-bit quantum system to encode particles, a new sonar image segmentation algorithm based on quantum-inspired particle swarm and fuzzy clustering is proposed. Based on its own optimal particles and global optimal particles, the rotation angle is determined. By calculating the variance of the particle group fitness, the multi-bit quantum revolving gate is used to update the particle position in real time. The output of improved particle swarm optimization is used to initialize the K-mean clustering center to converge to the global optimal solution. Based on the idea of fuzzy membership matrix in FCM, combined with the isolated spatial information characteristics of the noise, the sonar image segmentation and the denoising are carried out. The experimental results show that the proposed algorithm can improve the global search ability of particle swarm optimization effectively. It is better than the quantum fuzzy clustering and quantum genetic algorithm in image segmentation. The analysis results of multiple real underwater sonar images show that the new optimization algorithm has faster convergence speed, better optimizing capacity and better segmentation results for sonar images. [ABSTRACT FROM AUTHOR]

Meta-heuristic optimization algorithms have been used to solve mathematically unidentifiable problems. The main purpose of the optimization methods on problem-solving is to choose the best solution in predefined conditions. To increase performance of the optimization methods, chaotic maps for instance Logistic, Singer, Sine, Tent, Chebyshev, Circle have been widely used in the literature. However, hybrid 1D chaotic maps have higher performance than the 1D chaotic maps. The hybrid chaotic maps have not been used in the optimization process. In this article, 1D hybrid chaotic map (logistic-sine map)-based novel swarm optimization method is proposed to achieve higher numerical results than other optimization methods. Logistic-sine map has good statistical result, and this advantage is used directly to calculate global optimum value in this study. The proposed algorithm is a swarm-based optimization algorithm, and the seed value of the logistic-sine map is generated from local best solutions to reach global optimum. In order to test the proposed hybrid chaotic map-based optimization method, widely used numerical benchmark functions are chosen. The proposed chaotic optimization method is also tested on compression spring design problem. Results and comparisons clearly show that the proposed chaotic optimization method is successful. [ABSTRACT FROM AUTHOR]

Existing classification techniques that are proposed previously for eliminating data inconsistency could not achieve an efficient parameter reduction in soft set theory, which effects on the obtained decisions. Meanwhile, the computational cost made during combination generation process of soft sets could cause machine infinite state, which is known as nondeterministic polynomial time. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of searching domain space using a developed Markov chain model. Furthermore, this study introduces an efficient soft set reduction-based binary particle swarm optimized by biogeography-based optimizer (SSR-BPSO-BBO) algorithm that generates an accurate decision for optimal and sub-optimal choices. The results show that the decision partition order technique is performing better in parameter reduction up to 50%, while other algorithms could not obtain high reduction rates in some scenarios. In terms of accuracy, the proposed SSR-BPSO-BBO algorithm outperforms the other optimization algorithms in achieving high accuracy percentage of a given soft dataset. On the other hand, the proposed Markov chain model could significantly represent the robustness of our parameter reduction technique in obtaining the optimal decision and minimizing the search domain. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) is a one of the most efficient evolutionary for global optimization and widely applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. To cope with these issues, a new cuckoo search algorithm extension based on self-adaptive knowledge learning (I-PKL-CS) is proposed. In this study, learning model with individual history knowledge and population knowledge is introduced into the CS algorithm. Individuals constantly adjust their position by using historical knowledge and communicate with each other by using their own knowledge in the optimization process. In order to reduce complexity of the I-PKL-CS algorithm, the optimal learning model is selected to exploit the potential of individual knowledge learning and population knowledge learning by adopting threshold statistics learning strategy, which provides a good trade-off between the exploration and exploitation. The accuracy and performance of the proposed approach are evaluated by eighteen classic benchmark functions and CEC 2013 test suite. Statistical comparisons of the experimental results showed that the proposed I-PKL-CS algorithm made an appropriate trade-off between exploration and exploitation. Comparing the proposed I-PKL-CS with various CS algorithms, variants of differential evolution, and improved particle swarm optimization algorithms, the results demonstrate that I-PKL-CS is a competitive new type of algorithm. [ABSTRACT FROM AUTHOR]

League championship algorithm is a recently proposed population-based evolutionary algorithm for finding global optimal solutions in continuous optimization problems. The proposed work adopts the algorithm by modifying the team formation step for solving real-time task scheduling problem in heterogeneous multiprocessors. Two different objectives: tardiness and energy consumption were considered for scheduling. Our proposed algorithm is implemented using Java and tested using the graphs generated by the benchmark tools: task graph for free and task graph generator. Simulation results prove the performance of the proposed algorithm is better in terms of the objective functions over the other existing metaheuristic algorithms such as genetic algorithm, ant colony optimization and particle swarm optimization. [ABSTRACT FROM AUTHOR]

This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker's group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm. [ABSTRACT FROM AUTHOR]

Due to fast variation of desired variables in vehicle handling problem, design of an accurate applicable economic and considerably quick responsible controller for steering control of such systems has attracted much attention in the literature. The problem becomes more complicated, if the variation of road condition comes into play also. In this study, by combination of a simple PID and an optimized adaptive neural network controller, an arrangement for active front steering control of vehicles in different road frictions is proposed. A general PID controller is picked up and then optimized using the particle swarm optimization algorithm. After that, a neural network is added consecutively and trained by the outputs of PID controller and neural network toolbox of MATLAB software. The proposed controller fulfills both the applicability and efficiency due to dual use of PID and neural network controllers. Simulation results confirm the rightness of suggested controller in active steering control of vehicles even for unpredictable road friction. [ABSTRACT FROM AUTHOR]