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The timetabling problem is common to academic institutions such as schools, colleges or universities. It is a very hard combinatorial optimisation problem which attracts the interest of many researchers. The university course timetabling problem (UCTTP) is difficult to address due to the size of the problem and several challenging hard and soft constraints. Over the years, various methodologies were proposed to solve UCTTP. The purpose of this survey paper is to provide the most recent scientific review of the methodologies applied to UCTTP. The paper unveils a classification of methodologies proposed in recent years based on chronology and datasets used. Perspectives, trends, challenges and opportunities in UCTTP are also presented. It is observed that meta-heuristic approaches are popular among researchers. This is followed closely by hybrid methodologies. Hyper-heuristic approaches are also able to produce effective results. Another observation is that the state-of-art methodologies in the scientific literature are not fully utilised in a real-world environment perhaps due to the limited flexibility of these methodologies.

We address the post enrolment course timetabling (PE-CTT) problem in this paper. PE-CTT is known as an NP-hard problem that focuses on finding an efficient allocation of courses onto a finite number of time slots and rooms. It is one of the most challenging resources allocation problems faced by universities around the world. This work proposes a two-phase hybrid local search algorithm to address the PE-CTT problem. The first phase focuses on finding a feasible solution, while the second phase tries to minimize the soft constraint violations of the generated feasible solution. For the first phase, we propose a hybrid of Tabu Search with Sampling and Perturbation with Iterated Local Search. We test the proposed methodology on the hardest cases of PE-CTT benchmarks. The hybrid algorithm performs well and our results are superior compared to the recent methods in finding feasible solutions. For the second phase, we propose an algorithm called Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P). The SAR algorithm is used to minimize the soft constraint violations by exploiting information collected from the preliminary runs. We test the proposed methodology on three publicly available datasets. Our algorithm is competitive with the standards set by the recent methods. In total, the algorithm attains new best results for 3 cases and new best mean results for 7 cases. Furthermore, it is scalable when the execution time is extended.

Hypothyroidism is one of the most common chronic endocrine conditions. However, as symptoms of hypothyroidism are non-specific, up to 60% of those with thyroid dysfunction are unaware of their condition. Left untreated, hypothyroidism may contribute to other chronic health conditions. In the Arabian Gulf States, hypothyroidism is thought to be common, but is underdiagnosed, and management approaches vary. An advisory board of leading Saudi endocrinologists and policy advisers was convened to discuss and formulate recommendations for the diagnosis and management of hypothyroidism in Saudi Arabia based on their clinical expertise. The final document was shared with leading endocrinologists from the other Gulf Cooperation Council (GCC) and aconsensus report was generated and summerized in this article. While there is no consensus regarding population screening of hypothyroidism, current recommendations suggest screening patients with risk factors, including those with a history of head or neck irradiation, a family history of thyroid disease or pharmacological treatment that may affect thyroid function. Evidence from a cross-sectional study in Saudi Arabia suggests screening the elderly (> 60 years), at least in the primary care setting. In Saudi Arabia, the incidence of congenital hypothyroidism is approximately 1 in every 3450 newborns. Saudi nationwide population prevalence data are lacking, but a single-centre study estimated that the prevalence of subclinical hypothyroidism in the primary care setting was 10%. Prevalence rates were higher in other cross-sectional studies exclusively in women (13–35%). The recommendations included in this article aim to streamline the diagnosis and clinical management of hypothyroidism in the GCC, especially in the primary care setting, with the intention of improving treatment outcomes. Further study on the incidence, prevalence and risk factors for, and clinical features of, hypothyroidism in the GCC countries is required.

Generative Adversarial Networks (GAN) are well known for their capability of generating photo-realistic images or data collections that appear real. One of state-of-the-art approaches in GAN is Evolutionary GAN (E-GAN) which can outperform other GAN methods by leveraging the advantages of evolutionary computing, including population based search, mutation and elitism operators. However evolutionary search is often demanding in terms of resources, e.g. computational power and time. That limits its applicability when resource is limited. Hence we propose Partial Transfer training based E-GAN (PT-EGAN) to improve the efficiency of E-GAN. PT-EGAN aims to train the generator and the discriminator with smaller data sets and transfer the gained features across different stages of training. Our comparative experiments on the CIFAR-10 data set shows that PT-EGAN can reach better performance than E-GAN when using similar resources. Alternatively PT-EGAN requires less resources to achieve a similar performance as E-GAN. PT-EGAN is effective in speeding up generative adversarial learning.

Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to define the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. The high-level strategy uses the search performance to control the selection of which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics each use different rules to effectively explore the SVM configuration search space. To address bi-objective optimization, the proposed framework adaptively integrates the strengths of decomposition- and Pareto-based approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed framework has been evaluated on two cyber security problems: Microsoft malware big data classification and anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective, if not superior, compared with its counterparts and other algorithms.

The harmony search algorithm has been proven to be an effective optimization method for solving diverse optimization problems. However, due to its slow convergence, the performance of HSA over constrained optimization problems is not very competitive. Therefore, many researchers have hybridized HSA with local search algorithms. However, it’s very difficult to known in advance which local search should be hybridized with HSA as it depends heavily on the problem characteristics. The question is how to design an effective selection mechanism to adaptively select a suitable local search to be combined with HSA during the search process. Therefore, this work proposes an adaptive HSA that embeds an adaptive selection mechanism to adaptively select a suitable local search algorithm to be applied. This work hybridizes HSA with five local search algorithms: hill climbing, simulated annealing, record to record, reactive tabu search and great deluge. We use the Solomon’s vehicle routing problem with time windows benchmark to examine the effectiveness of the proposed algorithm. The obtained results are compared with basic HSA, the local search algorithms and existing methods. The results demonstrate that the proposed adaptive HSA achieves very good results compared other methods. This demonstrates that the selection mechanism can effectively assist HSA to adaptively select a suitable local search during the problem solving process.

Traffic signals play a significant role in the urban transportation system. They control the movement of traffic on urban streets by determining the appropriate signal timing settings. Due to the stochastic nature of the traffic flow, deciding on the best signal timing settings is a computationally complex problem, with the result that traditional analytical methods have been found to be inadequate in dealing with real world scenarios. This issue has already been tackled using computational intelligence algorithms such as the genetic algorithm (GA). However, despite good results, GA may experience slow convergence, especially when dealing with constrained optimisation problems. To address this issue, we propose an adaptive memetic algorithm (MA) for optimising signal timings in real world urban road networks using traffic volumes derived from induction loop detectors. The proposed MA combines the strengths of GA with the exploitation power of a local search algorithm, in an adaptive manner, so as to accelerate the search process and generate high quality solutions. In this work, we propose two important techniques for improving the performance of a traditional MA. First, we use a systematic neighbourhood based simple descent algorithm as a local search to effectively exploit the search space around GA solutions. Second, to achieve a proper balance between the exploration of GA and the local search algorithm, we propose an indicator scheme to control the local search application based on the diversity and the quality of the search process. The proposed MA was tested in two different case studies for the cities of Brisbane, Australia, and Plock, Poland, using the well-known microscopic traffic simulator, AIMSUN. Results demonstrate that our MA is better than GA and traditional fixed-time traffic signal settings.

This paper investigates the Google machine reassignment problem (GMRP). GMRP is a real world optimisation problem which is to maximise the usage of cloud machines. Since GMRP is computationally challenging problem and exact methods are only advisable for small instances, meta-heuristic algorithms have been used to address medium and large instances. This paper proposes a cooperative evolutionary heterogeneous simulated annealing (CHSA) algorithm for GMRP. The proposed algorithm consists of several components devised to generate high quality solutions. Firstly, a population of solutions is used to effectively explore the solution space. Secondly, CHSA uses a pool of heterogeneous simulated annealing algorithms in which each one starts from a different initial solution and has its own configuration. Thirdly, a cooperative mechanism is designed to allow parallel searches to share their best solutions. Finally, a restart strategy based on mutation operators is proposed to improve the search performance and diversification. The evaluation on 30 diverse real-world instances shows that the proposed CHSA performs better compared to cooperative homogeneous SA and heterogeneous SA with no cooperation. In addition, CHSA outperformed the current state-of-the-art algorithms, providing new best solutions for eleven instances. The analysis on algorithm behaviour clearly shows the benefits of the cooperative heterogeneous approach on search performance.

Article free to read on journal website The traffic in a weaving segment is subject to lane-changing turbulence in excess of that normally present on basic motorway segments. Empirical studies have observed a lane-changing concentration problem, as traffic flow increases, which can cause flow break down and congestion. This paper focuses on the lane-changing concentration problem in weaving segments. A Cooperative Intelligent Transport System (C-ITS) advisory has been shown to alleviate such a lane-changing concentration problem. The advisory aims to distribute the lane-changing along the weaving segment. Unlike previous methods in the literature, where weaving vehicles are assigned according to fixed distributions, this paper proposes an algorithm to optimize the lane-changing distribution. The proposed optimization algorithm was developed based on particle swarm optimization. The optimized lane-changing distribution for a one-sided motorway weaving segment using microscopic simulation has been evaluated. The initial results show that the proposed algorithm could be used as a successful optimization technique for the lane- changing concentration problem.

Timed transfer coordination in public transit reduces passenger transfer time by providing seamless interconnected transfers. The problem arises when a Receiving Vehicle (RV) arrives to the transfer stop before a Feeding Vehicle (FV) carrying transferring passengers. Timed transfer coordination in operational control dynamically decides whether a RV is held at the transfer stop to allow transfers, or departs as scheduled. While transfer demand is essential for implementing timed transfer coordination, this variable is generally not available in public transit because of the lack of passenger transfer plans. The problem of acquiring this variable in real-time has also received limited attention in the related literature. This paper proposes a new method to dynamically predict the transfer demand. We anticipate the transferring probability from each individual passenger by examining historical travel itineraries. Three different types of models (simple analytical, statistical, and computation intelligence model) are developed to forecast the number of transferring passengers. Numerical experiments using observed Automatic Vehicle Location and Automatic Fare Collection data from South East Queensland, Australia show the accuracy and applicability of the proposed models in timed transfer coordination.

BackgroundWhole body vibration exercise (WBVE) has been shown to improve muscular strength and power, and increase peripheral blood flow. The aim of this study was to evaluate the behavior of the skin temperature (Tsk) on regions of the lower limbs from an acute bout of WBVE.Methods and findingsCross-sectional study approved by local ethics committee (Certificado de Apresentação para Apreciação Ética-CAAE-19826413.8.0000.5259) and Trial registration (Registro Brasileiro de Ensaios Clínicos-REBEC-RBR-738wng). Using Infrared thermography (IRT), Tsk and thermal symmetry of the posterior lower extremities (thigh, knee, calf and heel) were examined in 19 healthy participants. IRT was assessed during 60-second WBVE exposures of 0, 30 and 50 Hz. From the adjusted linear mixed effects model, vibration frequency, time and regions of the lower extremity were significant (pConclusionsThe acute exposure of 60-second mechanical vibration has effect on the behavior of Tsk of the posterior region of the lower limbs, which is likely to be associated with a decrease on the blood flow due to WBVE. It is speculated that during WBVE a greater supply of blood is required where the body responds by shunting blood flow from the skin to working muscle in the first seconds of exercise. Further investigative work is required to verify this hypothesis.

Deep Belief Networks (DBN) have become a powerful tools to deal with a wide range of applications. On complex tasks like image reconstruction, DBN’s performance is highly sensitive to parameter settings. Manually trying out different parameters is tedious and time consuming however often required in practice as there are not many better options. This work proposes an evolutionary hyper-heuristic framework for automatic parameter optimisation of DBN. The hyper-heuristic framework introduced here is the first of its kind in this domain. It involves a high level strategy and a pool of evolutionary operators such as crossover and mutation to generates DBN parameter settings by perturbing or modifying the current setting of a DBN. Providing a large set of operators could be beneficial to form a more effective high level strategy, but in the same time would increase the search space hence make it more difficulty to form a good strategy. To address this issue, a non-parametric statistical test is introduced to identify a subset of effective operators for different phases of the hyper-heuristic search. Three well-known image reconstruction datasets were used to evaluate the performance of the proposed framework. The results reveal that the proposed hyper-heuristic framework is very competitive when compared to the state of art methods.

It has been proven that the hybridization of the harmony search algorithm with a local search algorithm (LS) is essential to compensate for the inadequacy of its exploitation. However, the success of this hybridization relies on the achievement of a proper balance between HSA exploration and LS exploitation. The question is can we obtain this balance by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures? To address these issues, this work proposes a meta-harmony search algorithm (meta-HSA) that uses two HSA algorithms, an HSA-optimizer and HSA-solver. The HSA-optimizer will adaptively adjust the components and the configurations of the HSA-solver based on the search status. The HSA-solver, which is a hybridization of HSA and LS, takes the configuration generated by the HSA-optimizer as input and then solves the given problem instance. That is, the HSA-optimizer operates on the components and the configurations of the HSA-solver, while the HSA-solver operates directly on the given problem instance (the solution search space). The proposed meta-HSA was applied to Solomon's vehicle routing problem with time windows benchmark to verify its effectiveness compared with standard HSA and the state-of-the art methods. The results of the comparison confirmed that the meta-HSA produces competitive results with respect to the other methods. Therefore, we can conclude that the meta-optimization technique does assist the hybrid HSA in obtaining the appropriate selection of its components and configurations during the search process. This demonstrates that the meta-HSA can provide a proper balance between exploration and exploitation by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures. Moreover, the meta-HSA optimizer decreases the effort exerted by the user in tuning these components and configurations.

The heterogeneous fleet vehicle routing problem with two-dimensional loading constraints (2L- HFVRP) is a complex variant of the classical vehicle routing problem. 2L-HFVRP seeks for minimal cost set of routes to serve a set of customers using a fleet of vehicles of different capacities, fixed and variable operating costs, different dimensions, and restricted loading constraints. To effectively deal with the 2L-HFVRP, we propose a two-stage method that successively calls the routing stage and the packing stage. For the routing stage, we propose an adaptive memetic approach that integrates new multi-parent crossover operators with multi-local search algorithms in an adaptive manner. A time-varying fitness function is proposed to avoid prematurity and improve search performance. An adaptive quality-and-diversity selection mechanism is devised to control the application of the memetic operators and the local search algorithms. In the packing stage, five heuristics are adopted and hybridised to perform the packing process. Experiments on a set of 36 2L-HFVRP benchmark instances demonstrate that the proposed method provides highly competitive results in comparison with state-of-the-art algorithms. In particular, the proposed method obtains the best results for several instances.

Local search algorithms have been successfully used for many combinatorial optimisation problems. The choice of the most suitable local search algorithm is, however, a challenging task as their performance is highly dependent on the problem characteristic. In addition, most of these algorithms require users to select appropriate internal neighbourhood structures to obtain desirable performance. No single local search algorithm can consistently perform well with a fixed setting, for different types of problems or even different instances of the same problem. To address this issue, we propose a hyper-heuristic framework which incorporates multiple local search algorithms and a pool of neighbourhood structures. This framework is novel in three respects. Firstly, a two-stage hyper-heuristic structure is designed to control the selection of a local search algorithm and its internal operators. Secondly, we propose an adaptive ranking mechanism to choose the most appropriate neighbourhood structures for the current local search algorithm. The proposed mechanism uses the entropy to evaluates the contribution of the local search in terms of quality and diversity. It adaptively adjusts the pool of candidate neighbourhood structures. Thirdly, we use a population of solutions within the proposed framework to effectively navigate different areas in the solutions search space and share solutions with local search algorithms. To ensure different solutions is allocated in different regions of the search space, we propose a distance-based strategy for population updating process that allowing solutions to share local search algorithms. We have evaluated the performance of the proposed framework using two challenging optimisation problems: Multi-Capacity Bin Packing benchmark instances and Google Machine Reassignment benchmark instances. The results show the effectiveness of the proposed framework, which outperformed state-of-the-art algorithms on several problem instances.

In addition to its nutritional value, Paullinia cupana (guarana) is commonly used in traditional medicine, known for its stimulant and medicinal properties. This investigation evaluated the effects of a guarana extract in combination with whole-body vibration (WBV) exercises on Wistar rats. Rats (male, n = 20) were divided into the following four groups: control (CON), treated with guarana (GUA), submitted to 50 Hz of WBV (WBV-E), treated with guarana and submitted to 50 Hz of WBV (GUA + WBV-E), daily for 6-weeks. Guarana altered (p &lt, 0.05) the uptake of the radiopharmaceutical sodium pertechnetate (Na99mTcO4) in muscle. Additionally, it modified the stool consistency, and in combination with WBV decreased the food intake without modifying body mass. The levels of calcium increased in the groups submitted to WBV exercises (WBV-E and GUA + WBV-E) as compared with the GUA group. The outcomes provide additional evidence of the biological effects of guarana separately or in combination with WBV and supports the use of guarana as a functional food.

Project Scheduling Problem (PSP) plays a crucial role in large-scale software development, directly affecting the productivity of the team and on-time delivery of software projects. PSP concerns with the decision of who does what and when during the software project lifetime. PSP is a combinatorial optimisation problem and inherently NP-hard, indicating that approximation algorithms are highly advisable for real-world instances which are often large in size. In this work, we propose an iterated local search (ILS) algorithm for PSP. ILS is a simple, yet effective for combinatorial optimisation problems. However, its performance highly depends on its perturbation operator which is to guide the search to new starting points. Hereby, we propose a Genetic Programming (GP) approach to evolve perturbation operators based on a range of low-level operators and rules. The evolution process will go along with the iterated search process and supply better operators continuously. The GP based ILS algorithm is tested using a set of well known PSP benchmark instances and compared with state-of-the-art algorithms. The experimental results demonstrated the effectiveness of GP generated perturbation operators as they can outperform existing leading methods.

Feature selection (FS) is an important and challenging task in machine learning. FS can be defined as the process of finding the best informative subset of features in order to avoid the curse of dimensionality and maximise the classification accuracy. In this work, we propose a FS algorithm based on binary particle swarm optimisation (PSO) and k-NN classifier. PSO is a well-known swarm intelligent algorithm that have shown to be very effective in dealing with various difficult problems. Nevertheless, the performance of PSO is highly effected by the inertia weight parameter which controls the balance between exploration and exploitation. To address this issue, we use an adaptive mechanism to adaptively change the value of the inertia weight parameter based on the search status. The proposed PSO has been tested on 12 well-known datasets from UCI repository. The results show that the proposed PSO outperformed the other methods in terms of the number of features and classification accuracy.

Objectives: The main focus of this review article was to collate up to date knowledge with regard to significance of single nucleotide polymorphisms (SNPs) in various genes associated with tooth agenesis. Failure to develop complete set of teeth also called tooth agenesis is a common developmental abnormality manifested mainly as an isolated condition. This anomaly is also associated with many developmental syndromes. Methods: We reviewed the evidence from the literature with regard to SNPs in many genes associated with this developmental anomaly. The information contained in this review deals only with non-syndromic form of tooth agenesis. This condition generally affects third molars or one or few other permanent teeth, however, in some cases its severity is relatively prevalent. Results and Conclusions: Mutations in genes such as Msh homeobox 1 (MSX1), Paired box gene 9 (PAX9), Axis inhibitor protein 2 (AXIN2) and Ectodysplasin A (EDA) have been identified that are associated with the familial form of the disease. It has been shown that the phenotypes associated with these mutations indicate the involvement of more complex mechanisms. Clinical Significance: Evidence collected so far has immense clinical significance to clinical dentists in providing comprehensive guide outlining the role of these gene mutations (SNPs) in various genes and also how the patients affected with this condition will be clinically diagnosed and managed in future.

Deep Convolutional Neural Network (CNN), which is one of the prominent deep learning methods, has shown a remarkable success in a variety of computer vision tasks, especially image classification. However, tuning CNN hyper-parameters requires expert knowledge and a large amount of manual effort of trial and error. In this work, we present the use of CNN on classifying good quality images versus bad quality images without understanding the image content. The well known data-sets were used for performance evaluation. More importantly we propose a hyper-heuristic approach for tuning CNN hyper-parameters. The proposed hyper-heuristic encompasses of a high level strategy and various low level heuristics. The high level strategy utilises search performance to determine how to apply low level heuristics to automatically find an appropriate set of CNN hyper-parameters. Our experiments show the effectiveness of this hyper-heuristic approach which can achieve high accuracy even when the training size is significantly reduced and conventional CNNs can no longer perform well. In short the proposed hyper-heuristic approach does enhance CNN deep learning.

Novel emivirine analogues 6a, b were synthesized by reacting chloromethyl ethyl ether with 5-ethyl/isopropyl-6-(3,5-dimethoxybenzyl)uracils 5e, f. On the other hand, A series of new TNK-651 analogues 10a-f substituted at N-1 with phenoxyethoxymethyl moiety was prepared on treatment of the corresponding uracils 5a-f with bis(phenoxyethoxy)methane (9). The newly synthesized non-nucleosides were tested for antiviral activity against wild type HIV-1 IIIB as well as the resistant strains N119 (Y181C), A17 (K103N+Y181C), and the triple mutant EFV(R) (K103R+V179D+P225H) in MT-4 cells. Most of the tested compounds showed good activities. Among them 6-(3,5-dimethylbenzyl)-5-ethyl-1-[(2-phenoxyethyl)oxymethyl]uracil (10c) and 6-(3,5-dimethylbenzyl)-5-isopropyl-1-[(2-phenoxyethyl)oxymethyl]uracil (10d) that showed inhibitory potency higher than emivirine against both wild type HIV-1 and the tested mutant strains, as well as higher activity than efavirenz against EFV(R).

The consequences of treatment involving the use of a natural product and whole body vibration (WBV) exercise have been investigated. The aim of the present study was to evaluate the effects of the joint treatment with an aqueous extract of Chenopodium ambrosioides and WBV on physiological parameters in rats. Wistar rats (n=20) were divided equally into four groups: control group (CG), treated with C. ambrosioides (CHE) group, exposed to 50 Hz of mechanial vibration (VBR), and treated with C. ambrosioides and exposed to 50 Hz of mechanical vibration (VBR + CHE) daily for 6 weeks. The body mass of the animals was determined weekly, the feed intake and the stool consistency were measured daily. One day after the 6 weeks of treatment, samples of blood were collected and used for biochemical analysis. Along 6 weeks, there was an increase (P

Deep Belief Networks (DBN) have been successful in classification especially image recognition tasks. However, the performance of a DBN is often highly dependent on settings in particular the combination of runtime parameter values. In this work, we propose a hyper-heuristic based framework which can optimise DBNs independent from the problem domain. It is the first time hyper-heuristic entering this domain. The framework iteratively selects suitable heuristics based on a heuristic set, apply the heuristic to tune the DBN to better fit with the current search space. Under this framework the setting of DBN learning is adaptive. Three well-known image reconstruction benchmark sets were used for evaluating the performance of this new approach. Our experimental results show this hyper-heuristic approach can achieve high accuracy under different scenarios on diverse image sets. In addition state-of-the-art meta-heuristic methods for tuning DBN were introduced for comparison. The results illustrate that our hyper-heuristic approach can obtain better performance on almost all test cases.

Iterated Local Search (ILS) is a simple yet powerful optimisation method that iteratively invokes a local search procedure with renewed starting points by perturbation. Due to the complexity of search landscape, different ILS strategies may better suit different problem instances or different search stages. To address this issue, this work proposes a new ILS framework which selects the most suited components of ILS based on evolutionary meta-learning. It has three additional components other than ILS: meta-feature extraction, meta-learning and classification. The meta-feature and meta-learning steps are to generate a multi-class classifier by training on a set of existing problem instances. The generated classifier then selects the most suitable ILS setting when performing on new instances. The classifier is generated by Genetic Programming. The effectiveness of the proposed ILS framework is demonstrated on the Google Machine Reassignment Problem. Experimental results show that the proposed framework is highly competitive compared to 10 state-of-the-art methods reported in the literature.

Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.

Feature selection problems (FS) can be defined as the process of eliminating redundant features while avoiding information loss. Due to that fact that FS is an NP-hard problem, heuristic and meta-heuristic approaches have been widely used by researchers. In this work, we proposed an Exponential Monte-Carlo algorithm (EMC-FS) for the feature selection problem. EMC-FS is a meta-heuristic approach which is quite similar to a simulated annealing algorithm. The difference is that no cooling schedule is required. Improved solutions are accepted and worse solutions are adaptively accepted based on the quality of the trial solution, the search time and the number of consecutive non-improving iterations. We have evaluated our approach against the latest methodologies in the literature on standard benchmark problems. The quality of the obtained subset of features has also been evaluated in terms of the number of generated rules (descriptive patterns) and classification accuracy. Our research demonstrates that our approach produces some of the best known results.

It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm is used in this study as it is parameter free. The search problem used is the Google Machine Reassignment Problem (GMRP). GMRP is a recent real world problem proposed at ROADEF/EURO challenge 2012 competition. It consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. In this paper, the effectiveness of three neighbourhood structures and their combinations are evaluated on GMRP instances, which are very diverse in terms of number of processes, resources and machines. The results show that neighbourhood structure does have impact on search performance. A combined neighbourhood structures with SD can achieve results better than SD with single neighbourhood structure.

Architecture design is one of the most important steps in software development, since design decisions affect the quality of the final system (e.g. reliability and performance). Due to the ever-growing complexity and size of software systems, deciding on the best design is a computationally intensive and complex task. This issue has been tackled by using optimisation method, such as local search and genetic algorithms. Genetic algorithms work well in rugged fitness landscapes, whereas local search methods are successful when the search space is smooth. The strengths of these two algorithms have been combined to create memetic algorithms, which have shown to be more efficient than genetic algorithms and local search on their own. A major point of concern with memetic algorithms is the likelihood of loosing the exploration capacity because of the ‘exploitative’ nature of local search. To address this issue, this work uses an adaptive scheme to control the local search application. The utilised scheme takes into account the diversity of the current population. Based on the diversity indicator, it decides whether to call local search or not. Experiments were conducted on the component deployment problem to evaluates the effectiveness of the proposed algorithm with and without the adaptive local search algorithm.

Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.

Google Machine Reassignment Problem (GMRP) is a real world problem proposed at ROADEF/EURO challenge 2012 competition which must be solved within 5 min. GMRP consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. This paper proposes an evolutionary simulating annealing (ESA) algorithm for solving this problem. Simulating annealing (SA) is a single solution based heuristic, which has been successfully used in various optimisation problems. The proposed ESA uses a population of solutions instead of a single solution. Each solution has its own SA algorithm and all SAs work in parallel manner. Each SA starts with different initial solution which can lead to a different search path with distinct local optima. In addition, mutation operators are applied once the solution cannot be improved for a certain number of iterations. This will not only help the search avoid being trapped in a local optima, but also reduce computation time. Because new solutions are not generated from scratch but based on existing ones. This study shows that the proposed ESA method can outperform state of the art algorithms on GMRP.

Load balancing (LB) is crucial in the field of cloud computing. LB is to find the optimum allocation of services onto a set of machines so the machine usage can be maximised. This paper proposes a new method for LB, simulated annealing (SA) enhanced by grammatical evolution (GE). SA is a well-known stochastic optimisation algorithm that has good performance on a range of problems including loading balancing. However the success of SA often relies on a key parameter known as the cooling schedule and the type of the utilised neighbourhood structure. Both the parameter and the structure of SA are problem specific. They need to be manually adjusted to fit the problem in hand. In addition different stages of the search process may have different optimal parameter values. To address these issues, a grammar evolution approach is introduced to adaptively evolve the cooling schedule parameter and neighbourhood structures. The proposed method can adjust SA parameter and structure based on the landscape of the current search state so high quality solutions can be found more quickly. The effectiveness of the proposed GE method is demonstrated on the Google machine reassignment problem, which is a typical LB problem, proposed for the ROADEF/EURO 2012 challenge. Experimental results show that our GE enhanced SA is highly competitive compared to state-of-the-art algorithms.

Optimisation under dynamic environment is a well known challenge not only because of the difficulties in handling constant changes during the search progress but also because of its real-world implication as many industry environments are dynamic. To tackle the dynamic aspect, optimisation algorithms need to track the changes and adjust for the global optima simultaneously. In this paper, we propose a multi-population memetic algorithm for dynamic optimisation, specially for the dynamic shortest path routing (DSPR) problem in mobile ad-hoc networks. DSPR is to find the shortest possible path that connects a source node with the destination node under a network environment where the topology is dynamic. There are algorithms proposed for DSPR. However handling the dynamic environment while maintaining the diversity is still a major issue. Hence the multi-population memetic algorithm is designed which has four main parts so the balance between exploration and exploitation of the search space could be better maintained. They include a genetic algorithm part which focuses solely on the exploring the search space; a local search component which is to search around the local area; a multi-population mechanism which is to maintain diversity by allocating every sub-population to different search area; and an external archive which is to preserve the current best solutions. The proposed method has been evaluated on DSPR instances that are generated under both cyclic and acyclic environments. Results show that the proposed algorithm can outperform other methods reported in the literature. That indicates the effectiveness of our proposed multi-population memetic approach in dealing with dynamic optimisation problems.

Load balancing (LB) is an important and challenging optimisation problem in cloud computing. LB involves assigning a set of services into a set of machines for which the goal is to optimise machine usages. This study presents a memetic algorithm (MA) for the LB problem. MA is a hybrid method that combines the strength of population based evolutionary algorithms with local search. However the effectiveness of MA mainly depends on the local search method chosen for MA. This is because local search methods perform differently for different instances and under different stages of search. In addition, invoking local search at every generation can be computationally expensive and compromise the exploration capacity of search. To address these issues, this study proposes a variable local search based MA in the context of LB problem. The proposed MA uses multiple local search mechanisms. Each one navigates a different area in search space using a different search mechanism which can leads to a different search path with distinct local optima. This will not only help the search to avoid being trap in a local optima point, but can also effectively deal with various landscape search characteristics and dynamic changes of the problem. In addition, a diversity indicator is adopted to control the local search processes to encourage solution diversity. Our MA method is evaluated on instances of the Google machine reassignment problem proposed for the ROADEF/EURO 2012 challenge. Compared with the state of the art methods, our method achieved the best performance on most of instances, showing the effectiveness of variable local search based MA for the Load Balancing problem.

Google Machine Reassignment Problem (GMRP) is an optimisation problem proposed at ROADEF/EURO challenge 2012. The task of GMRP is to allocate cloud computing resources by reassigning a set of services to a set of machines while not violating any constraints. We propose an evolutionary parallel late acceptance hill-climbing algorithm (P-LAHC) for GMRP in this study. The aim is to improve the efficiency of search by escaping local optima. Our P-LAHC method involves multiple search processes. It utilises a population of solutions instead of a single solution. Each solution corresponds to one LAHC process. These processes work in parallel to improve the overall search outcome. These LAHC processes start with different initial individuals and follow distinct search paths. That reduces the chance of falling into a same local optima. In addition, mutation operators will apply when the search becomes stagnated for a certain period of time. This further reduces the chance of being trapped by a local optima. Our results on GMRP instances show that the proposed P-LAHC performed better than single threaded LAHC. Furthermore P-LAHC can outperform or at least be comparable to the state-of-the-art methods from the literature, indicating that P-LAHC is an effective search algorithm.

This study investigates the dynamic shortest path routing (DSPR) problem in mobile ad-hoc networks. The goal is to find the shortest possible path that connects a source node with the destination node while effectively handling dynamic changes occurring on the ad-hoc networks. The key challenge in DSPR is how to simultaneously keep track changes and search for the global optima. A multi-memory based multi-population memetic algorithm is proposed for DSPR in this paper. The proposed algorithm combines the strength of three different strategies, multi-memory, multi-population and memetic algorithm, aiming to effectively explore and exploit the search space. It divides the search space by multiple populations. The distribution of solutions in each population is kept in the associated memory. The multimemory multi-population approach is to capture dynamic changes and maintain search diversity. The memetic component, which is a hybrid Genetic Algorithm (GA) and local search, is to find high quality solutions. The performance of the proposed algorithm is evaluated on benchmark DSPR instances under both cyclic and acyclic environments. Our method obtained better results when compared with existing methods in the literatures, showing the effectiveness of the proposed algorithm in handling dynamic optimisation.

In this work, we propose a variant of the honey-bee mating optimization algorithm for solving educational timetabling problems. The honey-bee algorithm is a nature inspired algorithm which simulates the process of real honey-bees mating. The performance of the proposed algorithm is tested over two benchmark problems; exam (Carter’s un-capacitated datasets) and course (Socha datasets) timetabling problems. We chose these two datasets as they have been widely studied in the literature and we would also like to evaluate our algorithm across two different, yet related, domains. Results demonstrate that the performance of the honey-bee mating optimization algorithm is comparable with the results of other approaches in the scientific literature. Indeed, the proposed approach obtains best results compared with other approaches on some instances, indicating that the honey-bee mating optimization algorithm is a promising approach in solving educational timetabling problems.

The shortest path routing (SPR) problem is a well-known challenge in the field of mobile network routing. The aim is to find the least cost path that connect a specific source node with a specific destination node. Although there are numerous algorithms to solve SPR, most of them consider only static environments in which the network topology and link-cost never change. A network with dynamic topologies and cost are indeed more challenging but more practical in real world applications. This paper presents a memetic algorithm for dynamic SPR (DSPR) problems in a mobile network. The proposed approach consists of three stages: genetic algorithm, local search and elitism-based immigrants procedure. Genetic algorithm (GA) is applied in the first stage to explore the search space and generate a new set of solutions. The generated solutions are further improved in the second stage by a local search algorithm. In third stage, an elitism-based immigrants procedure is activated to handle the dynamic changes by maintaining the diversity of the search process. The performance of the proposed algorithm has been evaluated on dynamic shortest path routing problem instances under both cyclic and acyclic environments. The study shows that, on both circumstances, the proposed algorithm is very stable with regards to dynamic network changes. This method is highly competitive compared to state-of-the-art algorithms in the literature as it outperformed these algorithms on all instances of dynamic routing during evaluation.

Background and Aim: Breast cancer is one of the most common cancers and most deadly malignancies among the women all over the world. The diagnosis, treatment, and complications arising from this cancer bring about numerous physical, mental, and social complications as well reduced quality of life in these patients. The present study aims at investigating the effect of training problem-based learning on the quality-of-life symptom scales in patients suffering from breast cancer. Materials and Methods: In a randomized controlled clinical trial, 50 breast cancer patients hospitalized in the oncology ward of Tohid Hospital of Sanandaj, Iran. The patients were randomly divided into two groups: experimental and control. Experimental group patients were trained according to problem-based learning, but no specific measure was conducted on control group patients. Right after the training course, the patients’ quality of life was measured once more by using the abovementioned questionnaires, and the data collected were analyzed by using statistical tests. Results: The mean age of the samples was 44.22 ± 10.32. The findings of the study indicate that there was a significant statistical difference between the two groups in the quality-of-life symptom scales before the intervention and after conducting it (P < 0.05). Moreover, as for the breast cancer-specific quality-of-life symptom scales, there was no significant statistical difference between the two groups both before and after the intervention (P < 0.05). Conclusion: The findings obtained in the present study indicate that training problem-based learning had a positive effect on the quality-of-life symptom scales in women suffering from breast cancer. This training method can be applied as a part of normal measures taken for the patients alongside other medical measures.

Background. Appropriate management, including pulmonary rehabilitation, associated with correct diagnosis of chronic obstructive pulmonary disease (COPD) in patients can contribute to improving clinical conditions of these patients. Physical activity is recommended for COPD patients. Whole-body vibration (WBV) is a modality of physical activity. Putting together the biological effects and safe use of WBV, it may be a potentially feasible intervention to add to pulmonary rehabilitation. The purpose of this investigation was to systematically review studies regarding the effects of WBV, as a component of the pulmonary rehabilitation, in patients with COPD.Results. A total of six publications met inclusion for review. There was evidence to support the beneficial use of WBV to improve functional performance of the lower limbs and quality of life. However, the appropriateness of and descriptors of WBV methods were poorly described.Conclusions. The results of this review support the use of WBV as a component of pulmonary rehabilitation to assist management of patients with COPD. However, future research should examine the dose-response curve and optimal dosing regimen of WBV according to standard reporting recommendations for people with COPD. Such an approach will allow comparison among studies and the potential of meta-analysis of randomized controlled trials.

Whole body vibration (WBV) is increasingly being used to improve balance and motor function and reduce the secondary complications associated with cerebral palsy (CP). The purpose of this study was to systematically appraise published research regarding the effects of static and/or dynamic exercise performed on a vibrating platform on gait, strength, spasticity and bone mineral density (BMD) within this population.Systematic searches of six electronic databases identified five studies that met our inclusion criteria (2 at Level II and 3 at Level III-2). Studies were analysed to determine: (a) participant characteristics; (b) optimal exercise and WBV treatment protocol; (c) effect on gait, strength, spasticity and BMD; and (d) the outcome measures used to evaluate effect. As data was not homogenous a meta-analysis was not possible.Several design limitations were identified and intervention protocols are poorly described. The effects on strength, gait, spasticity and BMD in persons with CP remain inconclusive with weak evidence that WBV may improve selected muscle strength and gait parameters and that prolonged exposure may improve BMD; there is currently no evidence that WBV can reduce spasticity.The evidence for exercise performed on a vibrating platform on mobility, strength, spasticity and BMD in CP remains scant and further larger scale investigations with controlled parameters to better understand the effects of WBV exercises in this population is recommended.

This work presents the design and characteristics of a new compact ultraviolet (UV) irradiator used in a biological onboard space flight experiment. The experiment, called DRM, took place in the International Space Station research facility (ISS-13 expedition), during the Centenary Mission (Russian-Brazil) in March-April 2006. The DRM main objective was to correlate the DNA repair mechanism and mutagenesis with microgravity. A compact irradiator apparatus was designed for DRM to allow in situ induced radiation in space. This apparatus, called CIM, uses UV-A Light-emitting diodes (LEDs) with 375 nm wavelength as molecular lesions inducers on four bacterial E. coli strains. The manned space mission restrictions were focused on during the CIM main parts design. The ultraviolet dosimetry is also described in this document as DRM experiment results and the CIM operational data are reported to certify the CIM design and DRM protocol compatibility in space application.

Electromagnetic algorithm is a population based meta-heuristic which imitates the attraction and repulsion of sample points. In this paper, we propose an electromagnetic algorithm to simultaneously tune the structure and parameter of the feed forward neural network. Each solution in the electromagnetic algorithm contains both the design structure and the parameters values of the neural network. This solution later will be used by the neural network to represents its configuration. The classification accuracy returned by the neural network represents the quality of the solution. The performance of the proposed method is verified by using the well-known classification benchmarks and compared against the latest methodologies in the literature. Empirical results demonstrate that the proposed algorithm is able to obtain competitive results, when compared to the best-known results in the literature.

Portfolio selection is an important problem in the financial markets that seeks to distribute an amount of money over a set of assets where the goal is to simultaneously maximize the return and minimize the risk. In this work, we propose a harmony search algorithm (HSA) for this problem. HSA is a population based algorithm that mimics the musician improvisation process in solving optimization problems. At each iteration, HSA generates a new solution using a memory procedure which considers all existing solutions and then perturbs them using a pitch adjustment operator. To deal with different instances, and also changes in the problem landscape, we propose an improved HSA that utilizes multiple pitch adjustment operators. The rationale behind this is that different operators are appropriate for different stages of the search and using multiple operators can enhance the effectiveness of HSA. To evaluate and validate the effectiveness of the proposed HSA, computational experiments are carried out using portfolio selection benchmark instances from the scientific literature. The results demonstrate that the proposed HSA is capable of producing high quality solutions for most of the tested instances when compared with state of the art methods.

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains.

Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving attribute reduction problems. At the high level, the hyper-heuristics (at each iteration) adaptively select the most suitable low level heuristics using roulette wheel selection mechanism. Whilst, at the underlying low level, four low level heuristics are used to gradually, and indirectly construct the solution. The proposed hyper-heuristics has been evaluated on a widely used UCI datasets. Results show that our hyper-heuristic produces good quality solutions when compared against other metaheuristic and outperforms other approaches on some benchmark instances.

Hyper-heuristic can be defined as a “heuristics to choose heuristics” that intends to increase the level of generality in which optimization methodologies can operate. In this work, we propose a scatter search based hyper-heuristic (SS-HH) approach for solving examination timetabling problems. The scatter search operates at high level of abstraction which intelligently evolves a sequence of low level heuristics to use for a given problem. Each low level heuristic represents a single neighborhood structure. We test our proposed approach on the un-capacitated Carter benchmarks datasets. Experimental results show the proposed SS-HH is capable of producing good quality solutions which are comparable to other hyper-heuristics approaches (with regarding to Carter benchmark datasets).

The influence of material time dependency and anisotropy in the context of two specific flywheel designs-preload and multi-directional composite (MDC)--is investigated. In particular, we focus on the following aspects: 1) geometric constraints, 2) material constraints, 3) loading type, and 4) the fundamental character of the time-dependent response, i.e., reversible or irreversible. The bulk of the results presented were obtained using a composite (PMC IM7/8552 at 135 C) material system. The material was characterized using a general multimechanism hereditary (viscoelastoplastic) model. As a general conclusion, the results have clearly shown that both the preload and the MDC rotor designs are significantly affected by time-dependent material behavior, which may impact the state of rotor balance and potentially reduce its operating life. In view of the results of the parametric studies and predictions made in the present study, the need for actual experimentation focusing on the time-dependent behavior of full-scale flywheel rotors is self-evident.