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The pile bearing capacity is considered as the most essential factor in designing deep foundations. Direct determination of this parameter in site is costly and difficult. Hence, this study presents a new technique of intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialism competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity. In this advanced structure, the ICA role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. To develop this model, the results of 257 high strain dynamic load tests (performed by authors) were considered and used in the analysis. For comparison purposes, ANFIS and GMDH models were selected and built for pile bearing capacity estimation. In terms of model accuracy, the obtained results showed that the newly developed model (i.e., ANFIS-GMDH-ICA) receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models. The proposed ANFIS-GMDH-ICA can be utilized as an advanced, applicable and powerful technique in issues related to foundation engineering and its design.

Velocity of the acoustic signal generated by partial discharge (PD) in the transformer is considerably affected by temperature. Because of thermosiphon effect, the internal temperature of the transformer gradually decreases from the top to the bottom, which leads the acoustic signal to propagate faster in the upper part of the transformer. Besides, due to the complex internal structure of the transformer, it is difficult for the acoustic signal to propagate from the PD source to the sensor along a straight line. In order to further enhance the accuracy of PD localization results, this paper proposes a correction-iteration method with considering the influence of the insulation oil temperature rise and the internal structure of the transformer. Based on the coordinate of the PD source calculated in the last iteration, more accurate results of the next iteration can be estimated. Effectiveness of the proposed method is verified by simulations and experiments, and the obtained results indicate that the localization error is positively correlated with temperature rise. Compared with other present localization methods, such as the Newton's method, the genetic algorithm and the imperial competitive algorithm, the PD localization results estimated by the proposed correction-iteration method are more accurate.

Given a seller with k types of items and n single-minded buyers, i.e., each buyer is only interested in a particular bundle of items, to maximize the revenue, the seller must assign some amount of bundles to each buyer with respect to the buyer's accepted price. Each buyer b i is associated with a value function v i ( ⋅ ) such that v i ( x ) is the accepted unit bundle price b i is willing to pay for x bundles. In this paper, we assume that bundles can be sold fractionally. The single-minded item selling problem is proved to be NP-hard. Moreover, we give an O ( k ) -approximation algorithm. For the online version, i.e., buyers come one by one and the decision must be made immediately on the arrival of each buyer, an O ( k ⋅ ( log ⁡ h + log ⁡ k ) ) -competitive algorithm is given, where h is the highest unit item price among all buyers.

Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.

Drilling and blasting operations are one of the most effective techniques for rock removal in mines. However, these operations are associated with some environmental issues such as the problem of toe. This issue has a deep impact on blasting cost where there is a need to conduct secondary blasting due to this problem. Therefore, it is beneficial to predict and subsequently optimize this issue. This study attempts to predict toe resulting from blasting by developing artificial neural network (ANN) and decision tree (DT) techniques. To do this, 100 blasting were considered and collected in the Hawzak limestone mine, Iran. Then, the most effective parameters were considered as model inputs and many models of ANN and DT were developed to predict toe induced by blasting. For comparison purposes, multiple regression (REG) was also applied. Three performance indices were used to evaluate the developed models. Coefficients of determination (R2) of 0.76, 0.83 and 0.91 were obtained for testing datasets using the REG, DT and ANN models, respectively, which indicate that ANN can provide higher performance capacity for prediction of toe induced by blasting. In the optimization phase, imperialism competitive algorithm (ICA), which is a new optimization technique in this field, was applied to determine optimal blasting pattern parameters in order to minimize toe induced by blasting. By developing ICA technique, the amount of toe was reduced from 431 to 288 m3 (49.35%), which shows the excellent ability of ICA in optimizing the minimization of toe that arises from blasting operations.

We present an optimally-competitive algorithm for the problem of maximum online perfect bipartite matching with i.i.d. arrivals. In this problem, we are given a known set of workers, a distribution over job types, and non-negative utility weights for each pair of worker and job types. At each time step, a job is drawn i.i.d. from the distribution over job types. Upon arrival, the job must be irrevocably assigned to a worker and cannot be dropped. The goal is to maximize the expected sum of utilities after all jobs are assigned. We introduce Dispatch, a 0.5-competitive, randomized algorithm. We also prove that 0.5-competitive is the best possible. When a job arrives, Dispatch first selects a “preferred worker” and assigns the job to this worker if it is available. The preferred worker is determined based on an optimal solution to a fractional transportation problem. If the preferred worker is not available, Dispatch randomly selects a worker from the available workers.

As blasting has some environmental constraints, ripping has got more prevalent as a method of breaking ground in both civil and mining engineering applications. A rippability model of a higher applicability is, therefore, needed to appropriately predict the ripping production (Q) prior to carrying out such tests. For the purpose of predicting the results of ripping production that were attained in three sites located in Johor state, Malaysia, the present study applied three hybrid intelligent techniques, i.e., neuro-bee, neuro-imperialism, and neuro-swarm, to predict Q. In fact, the effects of artificial bee colony (ABC), imperialism competitive algorithm (ICA), and particle swarm optimization (PSO) on weights and biases of neural networks were examined in the current research to receive better prediction/evaluation of ripping production. To do this, totally, 74 ripping tests were taken into consideration in the investigated regions and their influential parameters were assessed. Many parametric studies on ABC, ICA, and PSO parameters were conducted, and then, a comparison was done on the obtained results of the predictive hybrid models using several performance indices. As confirmed by the comparative results, the neuro-bee model proposed in this study estimated Q with a higher accuracy than the other hybrid models. The root-mean-square error (RMSE) values of 0.060, 0.076, and 0.094 were obtained for testing the data sets of neuro-bee, neuro-imperialism, and neuro-swarm techniques, respectively. This clearly shows that the newly developed hybrid model was superior to its rival in terms of predicting the ripping production. Furthermore, results of sensitivity analysis showed that weathering zone is the most influential factor on ripping production as compared to other inputs.

Cloud computing provides various services, like computation power, storage, platforms for developing applications, and software over the Internet. These services must be accessible to all its users all the time throughout the globe. Distributed denial of service (DDoS) attacks can be used by the attackers to disrupt the accessibility of these services. In these types of attacks, several systems are used to consume network or server resources that eventually results in inaccessibility of cloud services. In this paper, we propose a technique for detecting DDoS attacks using an artificial neural network (ANN) model. The model is trained using the imperialistic competitive algorithm. NSL KDD dataset is used to evaluate the performance of the proposed technique. Our proposed technique gives detection accuracy of 83.5% and 65% with KDDTest+ and KDDTest-21 datasets, respectively. Performance comparison with some other machine learning-based techniques and state-of-the-art techniques is also presented.

This research presents a solution to the family tourism route problem by considering daily time windows. To find the best solution for travel routing, the modified adaptive large neighborhood search (MALNS) method, using the four destructions and the four reconstructions approach, is applied here. The solution finding performance of the MALNS method is compared with an exact method running on the Lingo program. As shown by various solutions, the MALNS method can balance travel routing designs, including when many tourist attractions are present in each path. Furthermore, the results of the MALNS method are not significantly different from the results of the exact method for small problem sizes. For medium and large problem sizes, the MALNS method shows a higher performance and a smaller processing time for finding solutions. The values for the average total travel cost and average travel satisfaction rating derived by the MALNS method are approximately 0.18% for a medium problem and 0.05% for a large problem, 0.24% for a medium problem, and 0.21% for a large problem, respectively. The values derived from the exact method are slightly different. Moreover, the MALNS method calculation requires less processing time than the exact method, amounting to approximately 99.95% of the time required for the exact method. In this case study, the MALNS algorithm result shows a suitable balance of satisfaction and number of tourism places in relation to the differences between family members of different ages and genders in terms of satisfaction in tour route planning. The proposed solution methodology presents an effective high-quality solution, suggesting that the MALNS method has the potential to be a great competitive algorithm. According to the empirical results shown here, the MALNS method would be useful for creating route plans for tourism organizations that support travel route selection for family tours in Thailand.

Real parameter optimization is one of the active research fields during the last decade. The performance of LSHADE-SPACMA was competitive in IEEE CEC’2017 competition on Single Objective Bound Constrained Real-Parameter Single Objective Optimization. Besides, it was ranked fourth among twelve papers were presented on and compared to this new benchmark problems. In this work, an improved version named ELSHADE-SPACMA is introduced. In LSHADE-SPACMA, p value that controls the greediness of the mutation strategy is constant. While in ELSHADE-SPACMA, p value is dynamic. Larger value of p will enhance the exploration, while smaller values will enhance the exploitation. We further enhanced the performance of ELSHADE-SPACMA by integrating another directed mutation strategy within the hybridization framework. The proposed algorithm has been evaluated using IEEE CEC’2017 benchmark. According to the comparison results, the proposed ELSHADE-SPACMA algorithm is better than LSHADE and LSHADE-SPACMA. Besides, The comparison results between ELSHADE-SPACMA and the best three algorithms from the IEEE CEC’2017 Competition indicate that ELSHADE-SPACMA algorithm shows overall better performance and it is highly competitive algorithm for solving global optimization problems.

In combination with successful constraint handling techniques, a Matrix Adaptation Evolution Strategy (MA-ES) variant (the $\epsilon$MAg-ES) turned out to be a competitive algorithm on the constrained optimization problems proposed for the CEC 2018 competition on constrained single objective real-parameter optimization. A subsequent analysis points to additional potential in terms of robustness and solution quality. The consideration of a restart scheme and adjustments in the constraint handling techniques put this into effect and simplify the configuration. The resulting BP-$\epsilon$MAg-ES algorithm is applied to the constrained problems proposed for the IEEE CEC 2020 competition on Real-World Single-Objective Constrained optimization. The novel MA-ES variant realizes improvements over the original $\epsilon$MAg-ES in terms of feasibility and effectiveness on many of the real-world benchmarks. The BP-$\epsilon$MAg-ES realizes a feasibility rate of 100% on 44 out of 57 real-world problems and improves the best-known solution in 5 cases.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

The \begin{document}$k$\end{document} -center clustering is one of the well-studied clustering problems in computer science. We are given a set of data points \begin{document}$P\subseteq R^d$\end{document} , where \begin{document}$R^d$\end{document} is \begin{document}$d$\end{document} dimensional Euclidean space. We need to select \begin{document}$k≤ |P|$\end{document} points as centers and partition the set \begin{document}$P$\end{document} into \begin{document}$k$\end{document} clusters with each point connecting to its nearest center. The goal is to minimize the maximum radius. We consider the so-called online \begin{document}$k$\end{document} -center clustering model where the data points in \begin{document}$R^d$\end{document} arrive over time. We present the bi-criteria \begin{document}$(\frac{n}{k}, (\log\frac{U^*}{L^*})^2)$\end{document} -competitive algorithm and \begin{document}$(\frac{n}{k}, \logγ\log\frac{nγ}{k})$\end{document} -competitive algorithm for semi-online and fully-online \begin{document}$k$\end{document} -center clustering respectively, where \begin{document}$U^*$\end{document} is the maximum cluster radius of optimal solution, \begin{document}$L^*$\end{document} is the minimum distance of two distinct points of \begin{document}$P$\end{document} , \begin{document}$γ$\end{document} is the ratio of the maximum distance of two distinct points and the minimum distance of two distinct points of \begin{document}$P$\end{document} and \begin{document}$n$\end{document} is the number of points that will arrive in total.

In the generalized caching problem, we have a set of pages and a cache of size k . Each page p has a size w p ≥ 1 and fetching cost c p for loading the page into the cache. At any point in time, the sum of the sizes of the pages stored in the cache cannot exceed k . The input consists of a sequence of page requests. If a page is not present in the cache at the time it is requested, it has to be loaded into the cache, incurring a cost of c p . We give a randomized O (log k )-competitive online algorithm for the generalized caching problem, improving the previous bound of O (log 2 k ) by Bansal, Buchbinder, and Naor (STOC’08). This improved bound is tight and of the same order as the known bounds for the classic paging problem with uniform weights and sizes. We use the same LP-based techniques as Bansal et al. but provide improved and slightly simplified methods for rounding fractional solutions online.

In the adversarial edge arrival model for maximum cardinality matching, edges of an unknown graph are revealed one-by-one in an arbitrary order, and should be irrevocably accepted or rejected. Here, the goal of an online algorithm is to maximize the number of accepted edges while maintaining a feasible matching at any point in time. For this model, the standard greedy heuristic is $$\nicefrac {1}{2}$$ -competitive, and on the other hand, no algorithm that outperforms this ratio is currently known, even for very simple graphs. We present a clean Min-Index framework for devising a family of randomized algorithms, and provide a number of positive and negative results in this context. Among these results, we present a $$\nicefrac {5}{9}$$ -competitive algorithm when the underlying graph is a forest, and prove that this ratio is best possible within the Min-Index framework. In addition, we prove a new general upper bound of $$\frac{2}{3+1/\phi ^2}\approx 0.5914$$ on the competitiveness of any algorithm in the edge arrival model. Interestingly, while this result slightly falls short of the currently best $$\frac{1}{1+\ln 2} \approx 0.5906$$ bound by Epstein et al. (Inf Comput 259(1):31–40, 2018), it holds even for an easier model in which vertices along with their adjacent edges arrive online. As a result, we improve on the currently best upper bound of 0.6252 for the latter model, due to Wang and Wong (in: Proceedings of the 42nd ICALP, 2015).

We revisit the classical online scheduling problem on parallel machines for minimizing total weighted completion time. In the problem, a set of independent jobs arriving online over time has to be scheduled on identical machines, where the information of each job including its processing time and weight is not known in advance. The goal is to minimize the total weighted completion time of the jobs. For this problem, we propose an improved 2.11-competitive online algorithm based on a kind of waiting strategy.

A reliable prediction of the soil properties mixed with recycled material is considered as an ultimate goal of many geotechnical laboratory works. In this study, after planning and conducting a series of laboratory works, some basic properties of marine clay treated with recycled tiles together with their unconfined compressive strength (UCS) values were obtained. Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. The best neuro-swarm and neuro-imperialism models were selected based on several parametric studies on the most important and effective parameters of PSO and ICA. Afterward, these models were evaluated according to several well-known performance indices. It was found that the neuro-swarm predictive model provides a higher level of accuracy in predicting the UCS of clay soil samples treated with recycled tiles. However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures.

We propose a variant of the parking permit problem, called multi parking permit problem, in which an arbitrary demand is given at each instant and one may buy multiple permits to serve it. We show how to reduce this problem to the parking permit problem, while losing a constant cost factor. We obtain a 4-approximation algorithm and, for the online setting, a deterministic O(K)-competitive algorithm and a randomized O ( lg ⁡ K ) -competitive algorithm, where K is the number of permit types. For a leasing variant of the Steiner network problem, these results imply O(lg n)-approximation and online O ( lg ⁡ K lg ⁡ | V | ) -competitive algorithms, where n is the number of requests and | V | is the size of the input metric. Also, our technique turns into polynomial-time the pseudo-polynomial algorithms by Hu, Ludwig, Richa and Schmid for the 2D parking permit problem. For a leasing variant of the buy-at-bulk network design problem, these results imply: (i) an algorithm which improves the best previous approximation, and (ii) the first competitive online algorithm.

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R 2), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS–ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS–ICA model with R 2 of 0.942 can forecast PPV better than the USBM with R 2 of 0.634, Ambraseys–Hendron with R 2 of 0.638, Langefors–Kihlstrom with R 2 of 0.637 and Indian Standard with R 2 of 0.519.

In this paper, a mass spring system is developed to simulate the draping behavior of knitted fabric. A new type of mesh based on loop structure, which is called Loop mesh, is introduced. The effective properties on fabric drape are measured using the Fast system, and the experimental data are incorporated into the mass spring model to simulate the draping behavior of knitted fabrics. 3D shape of real drape behavior of fabric samples is achieved using depth camera. Then in order to reduce error value between simulated and real results, distances between mass points in the Loop mesh are optimized using the Imperialistic Competitive Algorithm (ICA). In order to investigate validity and accuracy of the Loop mesh, drape behavior of knitted fabrics falling over a square table are also simulated using common meshes. It can be found that the Loop mesh produced a closest drape shape to the real fabric drape than other mesh models. Finally, fabrics drape is simulated in other situations and compared with it...

Mehta and Panigrahi (FOCS 2012) introduce the problem of online matching with stochastic rewards, where edges are associated with success probabilities and a match succeeds with the probability of the corresponding edge. It is one of the few online matching problems that have defied the randomized online primal dual framework by Devanur, Jain, and Kleinberg (SODA 2013) thus far. This paper unlocks the power of randomized online primal dual in online matching with stochastic rewards by employing the configuration linear program rather than the standard matching linear program used in previous works. Our main result is a 0.572 competitive algorithm for the case of vanishing and unequal probabilities, improving the best previous bound of 0.534 by Mehta, Waggoner, and Zadimoghaddam (SODA 2015) and, in fact, is even better than the best previous bound of 0.567 by Mehta and Panigrahi (FOCS 2012) for the more restricted case of vanishing and equal probabilities. For vanishing and equal probabilities, we get a better competitive ratio of 0.576. Our results further generalize to the vertex-weighted case due to the intrinsic robustness of the randomized online primal dual analysis.

Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm.

In this paper, we have proposed a new optimization algorithm, Memetic improved L-SHADE with a local search pool, MiLSHADE-LSP, a memetic algorithm that combines an improved L-SHADE with a local search pool. Improved L-SHADE modifies several important parameters during the run to encourage exploration in initial stages and to focus later the search around the most promising solutions. The local search pool is responsible to continuously improve the best solutions. MiLSHADE-LSP uses a pool of two different local search, LS, methods, the Broyden-Fletcher-Goldfarb-Shanno method with limited memory, L-BFGS-B, and the Solis-Wets algorithms, with an adaptive mechanism to choose which one of them is applied in each iteration selecting which had obtained a greater improvement last time it was applied. In order to avoid waste LS applications, the proposed algorithm stores a list of individuals that were not previously improved by each LS method. It also includes a restart mechanism to explore new areas when the search is stuck, restarting the population but maintaining the best found solution, and resetting the LS Pool parameters. In the experimental section we have tested and analyzed MiLSHADE-LSP using the proposed benchmark for the competition 100-digit challenge on Single Objective Numerical Optimization, obtaining that the LS Pool improves the algorithm, both achieving more optima and with a better performance. Results obtained show that MiLSHADE-LSP is a very competitive algorithm.

In this paper, we study the problem of exploring a translating plume with a team of aerial robots. The shape and the size of the plume are unknown to the robots. The objective is to find a tour for each robot such that they collectively explore the plume. Specifically, the tours must be such that each point in the plume must be visible from the field-of-view of some robot along its tour. We propose a recursive Depth-First Search (DFS)-based algorithm that yields a constant competitive ratio for the exploration problem. The competitive ratio is $\frac{2(S_{r}+S_{p})(R+\lfloor\log R\rfloor)}{(S_{r}-S_{p})(1+\lfloor\log R\rfloor)}$ where R is the number of robots, and S r and S p are the robot speed and the plume speed, respectively. We also consider a more realistic scenario where the plume shape is not restricted to grid cells but an arbitrary shape. We show our algorithm has $\frac{2(S_{r}+S_{p})(18R+\lfloor\log R\rfloor)}{(S_{r}-S_{p})(1+\lfloor\log R\rfloor)}$ competitive ratio under the fat condition. We empirically verify our algorithm using simulations.

This paper studies an online over-list model of the integrated allocation of berths and quay cranes (QCs) in container terminals with 1-lookahead ability. The objective is to minimize the maximum completion time of container vessels, i.e., the makespan. We focus on two different types of vessels, three berths and a small number of QCs in the hybrid berth layout, with 1-lookahead ability. We propose a $${{(1 + \sqrt{2} )/2}}$$ -competitive algorithm for the case with four cranes, a 5/4-competitive algorithm for the case with five cranes and a 4/3-competitive algorithm for the case with six cranes, respectively. All of the algorithms are proved to be optimal.

The pollution produced by power purchased from electrical network and the extensive effort of Iranian government toward the elimination of energy subsides are the encouraging factors in Iran for em...

A redundancy allocation problem is a well-known NP-hard problem that involves the selection of elements and redundancy levels to maximize the system reliability under various system-level constraints. In many practical design situations, reliability apportionment is complicated because of the presence of several conflicting objectives that cannot be combined into a single-objective function. A stele communications, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. In this paper, a new hybrid multi-objective competition algorithm (HMOCA)based on competitive algorithm (CA) and genetic algorithm (GA) is proposed for the first time in multi-objective redundancy allocation problems. In the multiobjective formulation, the system reliability is maximized while the cost and volume of the system are minimized simultaneously. Additionally, ay RSM is employed to tune the CA parameters. The proposed HMOCA is validated by some examples with analytical solutions. It shows its superior performance compared to a NSGA-II and PAES algorithms. Finally, the conclusion is given

Purpose The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and no training. The work may answer the question of how to spend the budget of training for the employees. This investigation presents the use of fuzzy optimization and clustering hybrid model (data mining approaches) as a fuzzy imperialistic competitive algorithm (FICA) and k-means to find the employees’ categories and predict their training requirements. Design/methodology/approach Prior research that served as an impetus for this paper is discussed. The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions. Findings This paper focuses on how to find a good model for the evaluation of employee profiles. The paper introduces the use of artificial intelligence methods (fuzzy optimization (FICA) and clustering techniques (K-means)) in management. The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses. Finally, the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements. Originality/value This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges (in TCT for the first time).

In order to reduce the manufacturing process variability and improve the production yield, it is important to predict the performance of manufacturing process. The adaptive neuro-fuzzy inference system (ANFIS) is a powerful network which can predict the output parameters of manufacturing process. However, design of ANFIS needs trial and errors to select the best structure. In this study, an imperialistic competitive algorithm has been used to determine the ANFIS architecture to reach minimum values of the prediction error. In order to evaluate the performance of this combined method, two illustrative examples of manufacturing processes have been used. Results indicated that the combined method has superiority in prediction of output, rather than previous developed ANFIS models and so it can be applied for modeling of the other manufacturing processes.

In the reordering buffer management problem, a sequence of colored items arrives at a service station to be processed. Each color change between two consecutively processed items generates some cost. A reordering buffer of capacity k items can be used to preprocess the input sequence in order to decrease the number of color changes. The goal is to find a scheduling strategy that, using the reordering buffer, minimizes the number of color changes in the given sequence of items. We consider the problem in the setting of online computation with advice. In this model, the color of an item becomes known only at the time when the item enters the reordering buffer. Additionally, together with each item entering the buffer, we get a fixed number of advice bits, which can be seen as information about the future or as information about an optimal solution (or an approximation thereof) for the whole input sequence. We show that for any $$\varepsilon > 0$$ there is a $$(1+\varepsilon )$$ -competitive algorithm for the problem which uses only a constant (depending on $$\varepsilon $$ ) number of advice bits per input item. This also immediately implies a $$(1+\varepsilon )$$ -approximation algorithm which has $$2^{O(n\log 1/\varepsilon )}$$ running time (this should be compared to the trivial optimal algorithm which has a running time of $$k^{O(n)}$$ ). We complement the above result by presenting a lower bound of $$\varOmega (\log k)$$ bits of advice per request for any 1-competitive algorithm.

This paper presents a neurodynamic optimization approach for l1 minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.

In recent years, there has been increasing interest on optimizing the robust design of sewing machines in order to improve their performances. This study presents a novel approach to the multi-objective robust design optimization of sewing machine. A combined multi-objective colonial competitive algorithm (MOCCA) and the Polynomial Chaos expansion (PCE) method is developed and used for the robust multi-objective optimization of the sewing machine. This robust optimization considers simultaneously the motor current, the current fluctuation and their standard deviations. The obtained results showed that the robust design reduces significantly the sensitivity of the sewing machine performances to the design parameters (DPs) uncertainties compared to the deterministic one.

We consider the online machine minimization problem in which jobs with hard deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on a mi...

Currently, visual inspections for damage identification of structures are broadly used. However, they have two main drawbacks; time limitation and qualified manpower accessibility. Therefore, more precise and quicker technique is required to monitor the condition of structures. To aid the aim, a data mining based damage identification approach can be utilized to solve these drawbacks. In this study, to predict the damage severity of single-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and Imperial Competitive Algorithm (ICA), called ICA-ANN method, is proposed. ICA is employed to determine the initial weights of ANN. The efficiency coefficient and mean square error (MSE) are used to evaluate the performance of the ICA-ANN model. Moreover, the proposed model is compared with a pre-developed ANN approach in order to verify the efficiency of the proposed methodology. Based on the obtained results, it is concluded that the ICA-ANN indicates a better performance in detection of damage severity over the ANN method used only.

Reliable estimation of rock fragmentation is an important issue in the blasting operations in order to predict quality of the production. Since rock fragmentation is affected by various parameters such as blast pattern and rock mass characteristics, it is very difficult to have an appreciate prediction of it. This paper describes a new hybrid imperialism competitive algorithm (ICA)-artificial neural network (ANN) in order to solve shortcomings of ANN itself for prediction of rock fragmentation. In fact, the influence of ICA on ANN results was studied in this research. By investigating the related studies, the most important parameters of ICA were identified and a series of parametric studies for their determination were conducted. All models were built using 8 inputs and one output which is rock fragmentation. To have a fair comparison and show the capability of the new hybrid model, a pre-developed ANN model was also considered and constructed. Evaluation of the obtained results demonstrated that a higher ability of rock fragmentation prediction is received developing a hybrid ICA-ANN model. Coefficient of determination (R2) values of (0.949 and 0.813) and (0.941 and 0.819) were obtained for training and testing of ICA-ANN and ANN models, respectively which indicated that the proposed ICA-ANN model can be implemented better in improving performance capacity of ANN model in estimating rock fragmentation.

In traditional methods for modelling the behaviour of manufacturing processes by using artificial neural networks, design of optimal architecture of these networks needs many trials and errors. Sometimes it may not achieve the best architecture of networks because of the time-consuming and tedious procedure of these methods. In this paper, the failure behaviour of magnesium alloy sheet forming at warm temperature is modelled by using the adaptive neuro-fuzzy inference systems (ANFIS) network. In order to reduce the time of finding the best architecture of ANFIS network, an optimisation algorithm, imperialistic competitive algorithm, was used. By combining this optimisation algorithm with the ANFIS, the best architecture of ANFIS network can be proposed to model the accurate behaviour of the manufacturing process in the least time possible.

We give the first polylogarithmic-competitive randomized online algorithm for the k -server problem on an arbitrary finite metric space. In particular, our algorithm achieves a competitive ratio of Õ(log 3 n log 2 k ) for any metric space on n points. Our algorithm improves upon the deterministic (2 k -1)-competitive algorithm of Koutsoupias and Papadimitriou [Koutsoupias and Papadimitriou 1995] for a wide range of n .

Memristors promise a means for high-density neuromorphic nanoscale architectures that leverage in situ learning algorithms. While traditional learning algorithms commonly assume analog values for synaptic weights, actual physical memristors may have a finite set of achievable states during online learning. In this paper, we simulate a learning algorithm with limitations on both the resolution of its weights and the means of switching between them to explore how these properties affect classification performance. For our experiments, we use the locally competitive algorithm (LCA) by Rozell et al. in conjunction with the MNIST dataset and a set of natural images. We investigate the effects of both linear and non-linear distributions of weight states. Our results show that as long as the weights are distributed roughly close to linear, the algorithm is still effective for classifying digits, while reconstructing images benefits from non-linearity. Further, the resolution required from a device depends on its transition function between states; for transitions akin to round-to-nearest, synaptic weights should have around 16 possible states (4-bit resolution) to obtain optimal results. We find that lowering the threshold required to change states or adding stochasticity to the system can reduce that requirement down to four states (2-bit resolution). The outcomes of our research are relevant for building effective neuromorphic hardware with state-of-the-art memristive devices.

In this paper, an Arabic statistical learning-based text classification system has been developed using Polynomial Neural Networks. Polynomial Networks have been recently applied to English text classification, but they were never used for Arabic text classification. In this research, we investigate the performance of Polynomial Networks in classifying Arabic texts. Experiments are conducted on a widely used Arabic dataset in text classification: Al-Jazeera News dataset. We chose this dataset to enable direct comparisons of the performance of Polynomial Networks classifier versus other well-known classifiers on this dataset in the literature of Arabic text classification. Results of experiments show that Polynomial Networks classifier is a competitive algorithm to the state-of-the-art ones in the field of Arabic text classification.

The traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems. In this paper, we present the new idea of combining the imperial competitive algorithm with a policy-learning function for solving the TSP problems. All offspring of each country are defined as representing feasible solutions for the TSP. All countries can grow increasingly strong by learning the effective policies of strong countries. Weak countries will generate increasingly excellent offspring by learning the policies of strong countries while retaining the characteristics of their own country. Imitating these policies will enable the weak countries to produce improved offspring; the solutions generated will, therefore, acquire a favorable scheme while maintaining diversity. Finally, experimental results for TSP instances from the TSP library have shown that our proposed algorithm can determine the salesman's tour with more effective performance levels than other known methods.

Measurement of magnitude and frequency ofthe voltage flicker is very important for monitoring and controlling voltage flicker efficiently to improve the network power quality. This paper presents two new methods for measurement of flicker signal parameters using Shuffled Frog Leaping Algorithm (SFLA) and Imperialist Competitive Algorithm (ICA). This paper estimates fundamental voltage and flicker magnitudes and frequencies with proposed methods. The goal is to minimize the error ofthe estimated magnitudes and frequencies via a designed fitness function. At first we introduce voltage flicker and its measuring techniques. Then voltage flicker model is analyzed. At the next part a review of SFLA and ICA is presented these methods are applied to a test voltage signal and the results are given and analyzed.

The resource-constrained project scheduling problem (RCPSP) has received wide attention. In this paper, an activity-list-based nested partitions algorithm (ALNP) is developed for solving the RCPSP and a P-ALNP is proposed to improve ALNP with local adjustment. In the algorithms, to improve the search efficiency, a partial double justification is employed as local search mechanism. The computational experiments on the PSPLIB and analysis on robustness of the algorithms show that ALNP outperforms the traditional serial scheduling scheme for solving the large-scale, complex RCPSPs, and P-ALNP can improve ALNP and obtain better results. P-ALNP is a competitive algorithm for solving the RCPSP.

An efficient chemical comparator, a computer application facilitating searching and comparing chemical libraries, is useful in drug discovery and other relevant areas. The need for an efficient and user-friendly chemical comparator prompted us to develop ChemCom (Chemical Comparator) based on Java Web Start (JavaWS) technology. ChemCom provides a user-friendly graphical interface to a number of fast algorithms including a novel algorithm termed UnionBit Tree Algorithm. It utilizes an intuitive stepwise mechanism for selecting chemical comparison parameters before starting the comparison process. UnionBit has shown approximately an 165% speedup on average compared to its closest competitive algorithm implemented in ChemCom over real data. It is approximately 11 times faster than the Open Babel FastSearch algorithm in our tests. ChemCom can be accessed free-of-charge via a user-friendly website at http://bioinformatics.org/chemcom/.

We design and analyze an online reordering buffer management algorithm with improved O (log k /log log k ) competitive ratio for nonuniform costs, where k is the buffer size. This improves on the best previous result (even for uniform costs) of Englert and Westermann (2005) giving O (log k ) competitive ratio, which was also the best (offline) polynomial time approximation guarantee for this problem. Our analysis is based on an intricate dual fitting argument using a linear programming relaxation for the problem that we introduce in this article.

This work presents an optimally-competitive algorithm for the problem of maximum weighted online perfect bipartite matching with i.i.d. arrivals. In this problem, we are given a known set of workers, a distribution over job types, and non-negative utility weights for each pair of worker and job types. At each time step, a job is drawn i.i.d. from the distribution over job types. Upon arrival, the job must be irrevocably assigned to a worker and cannot be dropped. The goal is to maximize the expected sum of utilities after all jobs are assigned.

—The generation scheduling of different units in the power system is one of the procedures that affects the cascading blackout. Generators are usually scheduled based on the solution of the unit commitment problem, which should minimize the running cost of the power system. In this article, a new objective function is presented to consider the load-shedding effect on the power generation scheduling. The simulations are carried out in MATLAB software (The MathWorks, Natick, Massachusetts, USA), showing that it may lead to high risk of blackout in some conditions if the generation scheduling is done only by considering the cost of generation.