Recent Advances in Computer Science and Communications - Volume 13, Issue 1, 2020

Background: Limitations exist in traditional optimization algorithms. Studies show that bio-inspired alternatives have overcome these drawbacks. Bio-inspired algorithm mimics the characteristics of natural occurrences to solve complex problems. Particle swarm optimization, firefly algorithm, bat algorithms, gray wolf optimizer, among others are examples of bio-inspired algorithms. Researchers make certain assumptions while designing these models which limits their performance in some optimization domains. Efforts to find a solution to deal with these challenges leads to the multiplicity of variants. Objectives: This study explores the improvement strategies in four popular swarm intelligence in the literature. Specifically, particle swarm optimization, firefly algorithm, bat algorithm, and gray wolf optimizer. It also tries to identify the exact modification position in the algorithm kernel that yielded the positive outcome. The primary goal is to understand the trends and the relationship in their performance. Methods: The best evidence review methodology approach is employed. Two ancient but valuable and two recent and efficient swarm intelligence, are selected for this study. Results: Particle swarm optimization, firefly algorithm, bat algorithm, and gray wolf optimizer exhibit local optima entrapment in their standard states. The same enhancement strategy produced effective outcome across these four swarm intelligence. The exact approach is chaotic-based optimization. However, the implementation produced the desired result at different stages of these algorithms. Conclusion: Every bio-inspired algorithm comprises two or more updating functions. Researchers need a proper guide on what and how to apply a strategy for an optimum result.

Background: In the field of IC physical design, there is a big problem in the IC floorplanning to find the early feedback to estimate the area, wire length, delay, etc. before IC fabrication. Objective: In this paper, minimization of the area and total wire length on the IC has been done using Binary Particle Swarm Optimization with sequence pair representation. Methods: Optimization of the IC floorplan works in two phases. In the first phase, the floorplan is constructed by sequence pair representation without any overlapping of the modules on IC floorplan. In the second phase, Binary Particle Swarm Optimization algorithm explores the packing of all modules in floorplan to find better optimal performances i.e. area and wire length. Results: The results obtained were compared with the solutions derived from other meta-heuristic algorithms, the area is improved maximum up to 10% and the wire length was improved maximum up to 28%. Conclusion: The Experimental results on Microelectronic Center of North Carolina benchmark circuits show that Binary Particle Swarm Optimization algorithm gives better convergence for the area and wire length optimization than other algorithms.

Background: Metaheuristic algorithms are optimization algorithms capable of finding near-optimal solutions for real world problems. Rectangle Packing Problem is a widely used industrial problem in which a number of small rectangles are placed into a large rectangular sheet to maximize the total area usage of the rectangular sheet. Metaheuristics have been widely used to solve the Rectangle Packing Problem. Objective: A recent metaheuristic approach, Binary Flower Pollination Algorithm, has been used to solve for rectangle packing optimization problem and its performance has been assessed. Methods: A heuristic placement strategy has been used for rectangle placement. Then, the Binary Flower Pollination Algorithm searches the optimal placement order and optimal layout. Result: Benchmark datasets have been used for experimentation to test the efficacy of Binary Flower Pollination Algorithm on the basis of utilization factor and number of bins used. The simulation results obtained show that the Binary Flower Pollination Algorithm outperforms in comparison to the other well-known algorithms. Conclusion: BFPA gave superior results and outperformed the existing state-of-the-art algorithms in many instances. Thus, the potential of a new nature based metaheuristic technique has been discovered.

Background: Spider monkey optimization algorithm is recently developed natureinspired algorithm. It is based on fission-fusion social structure of spider monkeys. Perturbation rate is one of the important parameter of spider monkey optimization algorithm, which affects the convergence behavior of spider monkey optimization algorithm. Generally, perturbation rate is a linearly increasing function. However, due to the availability of non-linearity in different applications, a non-linear function may affect the performance of spider monkey optimization algorithm. Objective: This paper provides a detailed study on various perturbation techniques used in spider monkey optimization algorithm and recommends a novel alternative of hyperbolic spider monkey optimization algorithm. The new approach is named as hyperbolic Spider Monkey Optimization algorithm as the perturbation strategy inspired by hyperbolic growth function. Methods: The proposed algorithm is tested over a set of 23 CEC 2005 benchmark problems. Results: The experimental outcomes illustrate that the hyperbolic spider monkey optimization algorithm effectively increase the reliability of spider monkey optimization algorithm in comparison to the considered approaches. Conclusion: The hyperbolic spider monkey optimization algorithm provides improved perturbation rate, desirable convergence precision, rapid convergence rate, and improved global search capability.

Background: In cloud computing era, large scale scientific applications process large amount of data in the data centers. Placing of this data onto a data center is a critical issue performed as part of workflow management system and aims to find the best data center to place the data. It has a direct impact on performance, cost and execution time of workflows. Methods: In this paper, a novel data placement strategy is proposed based on Crow Search Algorithm (CSA) that dynamically distributes the data sets to appropriate data centers during runtime stage of workflow. Results: The results obtained b y CSA based run time data placement algorithm are evaluated with the results of other algorithms. Simulation results acknowledge that using CSA based data placement algorithm provides appropriate results in comparison to the other algorithms.

Background: Bug reports are considered as a reference document, during the maintenance phase of the software development process. The developer's counsel them at whatever point they have to know about the reported bug and need to explore past bug solution. This process requires a sustainable amount of time due to a large number of comments. Therefore, the best solution to prevent the developers from reading the whole bug report is to summarize the entire discussion in a couple of sentences. Bug report summarization is the extraction of some important part of bug reports that are useful for investigation, resolving the bug with similar problems, reproducing the bug and checking the status of the bug. If the bug reports have huge volume, variety and velocity information as big data, extractive bug report summarization would be emerging as an issue. However, the examiners, find out the bug report summaries do not meet the expectation of the developer; there is a still need for reading the entire discussion. Objective: (1) To generate generalized unsupervised extractive bug report summarization system, which is easily applicable on any dataset without the need of effort and cost of manually creating summaries for training dataset (2) To handle the extensive number of comments and generate short summaries. (3) To reduce the data sparsity, reduction of information, redundancy and convergence issue for short and lengthy data set. (4) To achieve the semantic summarization solution and explore the large search space. (5) To provide the facility of adjusting the summary percentage. Methods: Particle swarm optimization and Hybridization of Ant Colony Optimization and Particle Swarm Optimization approaches are used with the advantage of feature weighting technique. Conclusion: The efficiency of the proposed approaches are compared with the best existing supervised and unsupervised approaches. The result shows that the Hybrid swarm intelligence approach provided better.

Background: Structural alignment of ribose nucleic acid (RNA) is one of the most challenging multi-objective real world problems from the field of bioinformatics. Objective: RNA molecules are less stable; hence they require inclusion of most stable secondary structure during their alignment. Therefore, the structural alignment requires the consideration of similarity score and structure score, as two objectives. Trade-off between these two objectives exists since obtaining optimum similarity score at concurrent optimum structure score is not possible. This paper presents artificial bee colony algorithm based three level multi-objective approach for performing structural alignment of RNA sequences, namely MO-3LABC. Methods: Algorithm firstly builds the secondary structure of all sequences at minimum free energy (MFE). Then sequence alignment is performed in level one at average percent sequence identity (APSI) score based gap length, optimized by ABC algorithm. Level two now builds the secondary structure of these aligned sequences based on base-pair probability and co-variation. Now the scores of level one and level two move towards level three for multi-objective optimization at Pareto optimality criteria with few additional strategies. Results: The results of MO-3LABC are compared with an already established efficient strategy MO-TLPSO; multi-objective two level strategy based on particle swarm optimization. The outputs are compared for pairwise and multiple sequence alignment datasets at prediction accuracy and solution quality criteria. Conclusion: MO-3LABC is found significantly better than MO-TLPSO at all the four evaluation criteria for both the datasets.

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Result: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

Background: Real-time object detection has been attracting much attention recently due the increasing market need of such systems. Therefore, different detection algorithms and techniques have been evaluated to create a reliable detection system. The main challenge to implement a realtime reliable detection system relies on the algorithm training phase. During this phase, a large number of object image database needs to be prepared for each object to be detected. Objective: In this work, we implement a simultaneous object detection system based on local Edge Orientation Histograms (EOH) as feature extraction method with a smaller objects image database. Then, we evaluate the performance of this detection system in two separate platforms. Methods: We evaluated the performance of the detection of Ede Orientation Histograms against HAAR and Local Binary Patterns (LBP) algorithms using two different objects. After that, we discussed the evaluation of the detection systems on the standard platform in addition to the porting process into the embedded platform. Results: We achieved excellent results on both face and hands objects using less than 300 samples. This number is really negligible compared to the size of the image database used by state-of-the-art solutions. In terms of quality of detection, we have achieved more than 93% detection accuracy for the standard platform and 91.8% in the embedded platform for both face and hand objects. Conclusion: In this work, we demonstrated how Edge Orientation Histograms-based detection system gives better performance results than the algorithms evaluated against with less than 300 images database in two separate platforms.

Background: Advance communication systems require new techniques for FIR filters with resource efficiency in terms of high performance and low power consumption. Lowcomplexity architectures are required by FIR filters for implementation in field programmable gate Arrays (FPGA). In addition, FIR filters in multistandard wireless communication systems must have low complexity and be reconfigurable. The coefficient multipliers of FIR filters are complicated. Objective: The implementation and application of high tap FIR filters by a partial product reduce this complexity. Thus, this article proposes a novel digital finite impulse response (FIR) filter architecture with FPGA. Methods: The proposed technique FIR filter is based on a new architecture method and implemented using the Quartus II design suite manufactured by Altera. Also, the proposed architecture is coded in Verilog HDL and the code developed from the proposed architecture has been simulated using Modelsim. This efficient FIR filter architecture is based on the shift and add method. Efficient circuit techniques are used to further improve power and performance. In addition, the proposed architecture achieves better hardware requirements as multipliers are reduced. A 10-tap FIR filter is implemented on the proposed architecture. Results: The design’s example demonstrates a 25% reduction in resource usage compared to existing reconfigurable architectures with FPGA synthesis. In addition, the speed of the proposed architecture is 37% faster than the best performance of existing methods. Conclusion: The proposed architecture offers low power and improved speed with the lowcomplexity design that gives the best architecture FIR filter for both reconfigurable and fixed applications.

Background: Significant work has been conducted in the direction of an intelligent interface development through Human-Computer Interaction (HCI). Various forms of information, such as video, audio, or in the written form, have been proposed either separately or in combination. Methods: This paper proposes an interactive contact solution based on the distinct characteristics of contract distribution and the spatial and temporal consistency to establish a multiple display system. Result: The correspondence between the user’s arm position information and the virtual scene was established by utilizing a virtual 3D interactive rectangular parallelepiped. An estimation technique of the arm motion was designed, in conjunction with the employment of the Fuzzy Predictive Control Mamdani Algorithm (FPCMA) using Robust Tracking (RT) for the user’s arm position and for validating the efficiency and accuracy, Kalman filter algorithm (VEA) was used to test the stability. Conclusion: For RT, using FPCMA is 1.21 for 17fps while 0.83 for 35fps. For the output, the VEA accuracy rate is 0.97.