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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...

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.

Online scheduling on identical machines is investigated in the setting where jobs arrive over time. The goal is to minimize the total completion time. A waiting strategy based online algorithm is designed and is proved to be $2.28$-competitive. The result improves the current best online algorithm from the worse-case prospective.

Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to balance the exploration and exploitation is harder work to the researchers improving the performance of DE. Very often, we catch one and lose another. To overcome this problem, this paper presents a novel DE variant, called heterozygous DE with Taguchi local search (THDE), in which two new proposed methods (i.e., multiple schemes heterozygous evolution and Taguchi local search) are employed, with one as enhanced exploration ability and the other enhanced exploitation ability. The experimental studies have been conducted on 27 well-known test functions, including unimodal, multimodal and shifted test functions. Experimental results have verified the quality and effectiveness of THDE. Comparison with the state-of-the-art DE variants has proved that THDE is a type of new competitive algorithm.

Nano fibers are widely used in various industries, therefor knowing the morphology is important. Thresholding is a simple but effective technique for image segmentation. The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper a novel method is proposed for performing image segmentation. The purpose of this paper proposed an imperial competitive algorithm with the objective function from Kmeans clustering algorithm for Nano fibers image thresholding. Then algorithm used, with the algorithms such as: global threshold, local threshold, Kmeans clustering algorithm and FCM methods were compared. Finally, a powerful algorithm for image thresholding is found. The comparisons and experimental results show that purposed algorithm is better than other methods particularly global and local thresholding, Kmeans and even FCM. General Terms Heuristic Algorithm, Thresholding Method.

This paper discusses about the available transfer capability by using Unified Power Flow Controller-UPFC. Flexible AC Transmission System-FACTS devices helps to reduce power flow on overloaded lines, thereby increasing the loadability of the power system, transient stability, damp out oscillations and also provide security and efficient transmission system. UPFC is one of the most versatile FACTS controllers. It is used for both shunt and series compensation. Newton Raphson method is used to calculate load flow for IEEE 30 bus system. By optimally placing the FACTS device Available Transfer Capability-ATC is improved. The ATC is calculated by using AC Power Transfer Distribution Factor- ACPTDF and this method is based on the sensitivity approach. Imperialistic Competitive Algorithm (ICA) is used to find optimal location of placing UPFC to improve ATC.

Many applications in engineering and science rely on the optimization of computationally cost functions. A successful approach in such states is to couple an evolutionary algorithm with a mathematical model which replaces the cost function. By considering of importance of advertising in the business declaration, especially billboard advertising, in this paper the authors have formulated billboard selection decision making, by using network count location approach which determine informative links in a network, to optimize cost of advertising and billboard visiting. Then, opposition based colonial competitive algorithm, which originally inspired by imperialistic competition, is used to solve mathematical model. Also, the authors implement proposed model on Sioux Falls city network.

This paper is aimed at presenting a new method on the basis of the colonial competitive algorithm to predict horizontal peak ground acceleration and spectral acceleration. The colonial competitive algorithm was developed over the last few years in an attempt to overcome inherent limitations of traditional optimize method. The proposed method employs the optimization capabilities of the colonial competitive algorithm to determine the coefficients of attenuation relationships of peak ground acceleration and spectral acceleration. This method was applied to an ensemble of earthquake records of two seismic zones, namely Zagros and Alborz-Central Iran. The obtained results clearly reveal that the colonial competitive algorithm can be viewed as a powerful and reliable tool for solving complex optimization problems such as attenuation relationship.

We consider the problem of utilizing a pack of m batteries serving n current demands in electric vehicles. When serving a demand, the current allocation might be split among the batteries in the pack. A battery’s life depends on the discharge current used for supplying the requests. Any deviation from the optimal discharge-current is associated with a penalty. Thus, the problem is to serve an online sequence of current requests in a way that minimizes the total penalty associated with the service.We show that the offline problem, for which the sequence of current demands is known in advance, is strongly NP-hard and hard to approximate within an additive gap of Ω(m) from the optimum. For the online problem, we present a competitive algorithm associated with the redundant penalty at most m. Finally, we provide a lower bound of 1.5 for the multiplicative competitive ratio of any online algorithm.

The hierarchical median problem consists of finding a hierarchical assignment function sequence of solutions to the well-known k-median problems with growing cardinality. This sequence is said to be r-competitive if the cost of each solution is at most r times of the optimum cost of median problem with a same cardinality, where r is called the competitive ratio. Previously the best algorithm available for this problem has competitive ratio 20.71. In this paper an improved algorithm is proposed with competitive ratio factor 16.

We consider a frequency allocation problem, in which we are given a cellular telephone network whose geographical coverage area is divided into cells in which phone calls are serviced by frequencies assigned to them, so that none of the pairs of calls emanating from the same or neighboring cells is assigned the same frequency. The problem is to use the frequencies efficiently, i.e., to minimize the span of frequencies used. The frequency allocation problem can be regarded as a multicoloring problem on a weighted hexagonal graph. In this paper, we present a 1-local 4/3-competitive distributed algorithm for multicoloring a hexagonal graph without certain forbidden configuration (introduced in Sparl and Zerovnik (2010) [7]).

Origin-destination (OD) matrix estimation largely depends on the quality and quantity of the input data, which in turn depends on the number and sites of count locations. In this paper, we focus on the network count location problem (NCLP), namely the identification of informative links in the road network. Also we employ opposition based colonial competitive algorithm (OCCA), which originally inspired by imperialistic competition, to determine the desirable number and locations of counting points satisfying location rules. The model and algorithm is illustrated with numerical examples.

We study the problem of leasing facilities over time, following the general infrastructure leasing problem framework introduced by Anthony and Gupta (2007). If there are K different lease types, Anthony and Gupta give an O ( K ) -approximation algorithm for the problem. We are able to improve this to a 3-approximation algorithm by using a variant of the primal–dual facility location algorithm of Jain and Vazirani (2001). We also consider the online version of the facility leasing problem, in which the clients to be served arrive over time and are not known in advance. This problem generalizes both the online facility location problem (introduced by Meyerson, 2001) and the parking permit problem (also introduced by Meyerson, 2005). We give a deterministic algorithm for the problem that is O ( K log n ) -competitive. No previous result was known for this problem. To achieve our result, we modify an O ( log n ) -competitive algorithm of Fotakis (2007) for the online facility location problem. We also reanalyze his algorithm via the dual-fitting technique to prove that it achieves the O ( log n ) competitive ratio.

Timetabling is the problem of scheduling a set of events while satisfying various constraints. In this paper, we develop and study the performance of an evolutionary algorithm, designed to solve a specific variant of the timetabling problem. Our aim here is twofold: to develop a competitive algorithm, but more importantly, to investigate the applicability of evolutionary operators to timetabling. To this end, the introduced algorithm is tested using a benchmark set. Comparison with other algorithms shows that it achieves better results in some, but not all instances, signifying strong and weak points. To further the study, more comprehensive tests are performed in connection with another evolutionary algorithm that uses strictly group-based operators. Our analysis of the empirical results leads us to question single-level selection, proposing, in its place, a multi-level alternative.

This paper presents a memetic multiobjective optimization algorithm based on NNIA for examination timetabling problems. In this paper, the examination timetabling problem is considered as a two-objective optimization problem while it is modeled as a single-objective optimization problem generally. Within the NNIA framework, the special crossover operator is utilized to search in the solution space; two local search techniques are employed to optimize these two objectives and a diversity-keeping strategy which consists of an elitism group operator and an extension optimization operator to ensure a sufficient number of solutions in the pareto front. The proposed algorithm was tested on the most widely used uncapacitated Carter benchmarks. Experimental results prove that the proposed algorithm is a competitive algorithm.

In this paper we consider a basic scheduling problem called the busy time scheduling problem - given n jobs, with release times \(r_j\), deadlines \(d_j\) and processing times \(p_j\), and m machines, where each machine can run up to g jobs concurrently, our goal is to find a schedule to minimize the sum of the “on” times for the machines. We develop the first correct constant factor online competitive algorithm for the case when g is unbounded, and give a \(O(\log P)\) approximation for general g, where P is the ratio of maximum to minimum processing time. When g is bounded, all prior busy time approximation algorithms use an unbounded number of machines; note it is NP-hard just to test feasibility on fixed m machines. For this problem we give both offline and online (requiring “lookahead”) algorithms, which are O(1) competitive in busy time and \(O(\log P)\) competitive in number of machines used.

In urban public transport, the traffic flow prediction is a classical non-linear complicated optimization problem, which is very important for public transport system. With the rapid development of the big data, Smart card data of bus which is provided by millions of passengers traveling by bus across several days plays a more and more important role in our daily life. The issue that we address is whether the data mining algorithm and the intelligent optimization algorithm can be applied to forecast the traffic flow from big data of bus. In this paper, a novel algorithm which called mixed support vector regression with sub-space orthogonal pigeon-Inspired Optimization (SOPIO-MSVR) is used to predict the traffic flow and optimize the algorithm progress. Results show the SOPIO-MSVR algorithm outperforms other algorithms by a margin and is a competitive algorithm. And the research can make the significant contribution to the improvement of the transportation.

This paper proposes a complex system optimization method to obtain an optimal battery charging strategy. First, a real-world lithium-ion battery charging model is built as a complex system problem, which includes electric subsystem and thermal subsystem. The optimization objectives of electric subsystem includes battery charging time and energy loss, and the optimization objectives of thermal subsystem includes battery internal temperature rise and surface temperature rise. Then a called biogeography-based complex system optimization (BBO/Complex) algorithm is introduced, which is a heuristic method for complex system optimization. Finally, BBO/Complex is applied to the complex system of battery charging strategy, and the results show that the proposed method is a competitive algorithm for solving batter charging problem studied in this paper.

This work deals with the multi-objective design optimization of NBTTL mechanism. The needle jerk (NJ) and the maximum angular velocity ratio (MAVR) are minimized simultaneously. The goal is to minimize the machine input torque and vibration. The multi-objective imperialistic competitive algorithm (MOICA) is used to find the optimal link lengths of the NBTTL mechanism. Compared to Juki 8700, the obtained results show that the optimized mechanism can reduce the MAVR by 80 % and the NJ by 70 %.

This paper proposes a complex system optimization method to solve economic emission load dispatch. First, a real-world economic emission load dispatch is modeled as a complex system problem, and the optimization objectives include economic load dispatch, emission load dispatch and transmission network loss. Then a called BBO/complex method is introduced, which extends biogeography-based optimization to a multi-archipelago environment to suit the structure of complex systems. Finally, the proposed method is applied to the economic emission load dispatch, and the results show that it can obtain good performance for economic emission load dispatch studied in this paper, and it is a competitive algorithm for solving complex system optimization problem.

The high latencies for access to background memory like hard disks or flash memory can be reduced by caching or hidden by prefetching. We consider the problem of scheduling the resulting I/Os when the available fast cache memory is limited and when we have real-time constraints where for each requested data block we are given a time interval during which this block needs to be in main memory. We give a near linear time algorithm for this problem which produces a feasible schedule whenever one exists. Another algorithm additionally minimizes I/Os and still runs in polynomial-time. For the online variant of the problem, we give a competitive algorithm that uses lookahead and augmented disk speed. We show a tight relationship between the amount of lookahead and the speed required to get a competitive algorithm.

The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm (LCA). Among the cost functions we examine are approximate [Formula: see text] norms ([Formula: see text]), modified [Formula: see text]-norms, block-[Formula: see text] norms, and reweighted algorithms. Of particular interest is that we show significantly increased performance in reweighted [Formula: see text] algorithms by inferring all parameters jointly in a dynamical system rather than using an iterative approach native to digital computational architectures.

SUMMARY The active control of engineering structures is one of the best methods to reduce structural responses under seismic excitation for the best performance of structures. This study presents an effective approach for the optimal control of structures under strong ground motion using the colonial competitive algorithm. The colonial competitive algorithm was developed over the last few years in an attempt to overcome the inherent limitations of traditional optimize method. The colonial competitive algorithm has been applied due to its ideal performance in optimal control problem. The effectiveness and performance of the proposed method have been investigated through two numerical examples for the response control of earthquake-excited structures. The obtained results have been compared with the Linear Quadratic Regulator (LQR) control algorithm, and the performance of the proposed control approach has been found to be better than the LQR controller. Copyright © 2012 John Wiley & Sons, Ltd.

Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic alg...

We consider the semi-online multiprocessor scheduling problem with m identical, parallel machines to minimize the makespan, where the jobs arrive in decreasing order of processing times. The famous Longest Processing Time ( LPT ) algorithm by Graham (1966) [4] for the classical offline multiprocessor scheduling problem schedules the jobs in decreasing order of processing times and has a worst-case bound of 4 / 3 − 1 / ( 3 m ) . So far, no algorithm with a better competitive ratio than the LPT algorithm has been given for the semi-online scheduling problem with decreasing processing times. In this note, we present a 5 / 4 -competitive algorithm for m ≥ 3 and an algorithm that is the best possible for m = 3 , i.e. an algorithm with competitive ratio ( 1 + 37 ) / 6 .