scholarly journals Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

2020 ◽  
Vol 10 (24) ◽  
pp. 8961
Author(s):  
Peng-Yeng Yin ◽  
Po-Yen Chen ◽  
Ying-Chieh Wei ◽  
Rong-Fuh Day
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Pattern Search ◽  
Experimental Result ◽  
Adaptive Memory ◽  
Glowworm Swarm Optimization ◽  
Metaheuristic Methods ◽  
Swarming Behavior ◽  
Better Than

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

scholarly journals Firefly Algorithm based on Euclidean Metric and Dimensional Mutation

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Time Complexity ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Computational Time ◽  
Local Optimum ◽  
Euclidean Metric ◽  
Oscillation Phenomenon ◽  
Attraction Model ◽  
Better Than

Firefly algorithm is a meta-heuristic stochastic search algorithm with strong robustness and easy implementation. However, it also has some shortcomings, such as the "oscillation" phenomenon caused by too many attractions, which makes the convergence speed is too slow or premature. In the original FA, the full attraction model makes the algorithm consume a lot of evaluation times, and the time complexity is high. Therefore, In this paper, a novel firefly algorithm (EMDmFA) based on Euclidean metric (EM) and dimensional mutation (DM) is proposed. The EM strategy makes the firefly learn from its nearest neighbors. When the firefly is better than its neighbors, it learns from the best individuals in the population. It improves the FA attraction model and dramatically reduces the computational time complexity. At the same time, DM strategy improves the ability of the algorithm to jump out of the local optimum. The experimental results show that the proposed EMDmFA significantly improves the accuracy of the solution and better than most state-of-the-art FA variants.

Catechize Global Optimization through Leading Edge Firefly Based Zone Routing Protocol

2020 ◽  
Vol 13 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Neha Sharma ◽  
Sherin Zafar ◽  
Usha Batra
Keyword(s):  
Global Optimization ◽  
Routing Protocol ◽  
Firefly Algorithm ◽  
Leading Edge ◽  
Packet Delivery Ratio ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Zone Routing Protocol ◽  
End To End Delay ◽  
End To End

Background: Zone Routing Protocol is evolving as an efficient hybrid routing protocol with an extremely high potentiality owing to the integration of two radically different schemes, proactive and reactive in such a way that a balance between control overhead and latency is achieved. Its performance is impacted by various network conditions such as zone radius, network size, mobility, etc. Objective: The research work described in this paper focuses on improving the performance of zone routing protocol by reducing the amount of reactive traffic which is primarily responsible for degraded network performance in case of large networks. The usage of route aggregation approach helps in reducing the routing overhead and also help achieve performance optimization. Methods: The performance of proposed protocol is assessed under varying node size and mobility. Further applied is the firefly algorithm which aims to achieve global optimization that is quite difficult to achieve due to non-linearity of functions and multimodality of algorithms. For performance evaluation a set of benchmark functions are being adopted like, packet delivery ratio and end-to-end delay to validate the proposed approach. Results: Simulation results depict better performance of leading edge firefly algorithm when compared to zone routing protocol and route aggregation based zone routing protocol. The proposed leading edge FRA-ZRP approach shows major improvement between ZRP and FRA-ZRP in Packet Delivery Ratio. FRA-ZRP outperforms traditional ZRP and RA-ZRP even in terms of End to End Delay by reducing the delay and gaining a substantial QOS improvement. Conclusion: The achievement of proposed approach can be credited to the formation on zone head and attainment of route from the head hence reduced queuing of data packets due to control packets, by adopting FRA-ZRP approach. The routing optimized zone routing protocol using Route aggregation approach and FRA augments the QoS, which is the most crucial parameter for routing performance enhancement of MANET.

Identification of Essential Proteins in Yeast Using Mean Weighted Average and Recursive Feature Elimination

2019 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Sivagnanam Rajamanickam Mani Sekhar ◽  
Siddesh Gaddadevara Matt ◽  
Sunilkumar S. Manvi ◽  
Srinivasa Krishnarajanagar Gopalalyengar
Keyword(s):  
Drug Design ◽  
Weighted Average ◽  
Living Organism ◽  
Experimental Result ◽  
Recursive Feature Elimination ◽  
Protein Interaction Data ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Result Show ◽  
Better Than

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

scholarly journals Global optimization of an encapsulated Si/SiO$$_2$$ L3 cavity with a 43 million quality factor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. P. Vasco ◽  
V. Savona
Keyword(s):  
Global Optimization ◽  
Photonic Crystal ◽  
Quality Factor ◽  
State Of The Art ◽  
Optimization Strategy ◽  
High Quality ◽  
Optimal Value ◽  
Out Of Plane ◽  
Fabrication Tolerances ◽  
Structural Imperfections

AbstractWe optimize a silica-encapsulated silicon L3 photonic crystal cavity for ultra-high quality factor by means of a global optimization strategy, where the closest holes surrounding the cavity are varied to minimize out-of-plane losses. We find an optimal value of $$Q_c=4.33\times 10^7$$ Q c = 4.33 × 10 7 , which is predicted to be in the 2 million regime in presence of structural imperfections compatible with state-of-the-art silicon fabrication tolerances.

A new approach based on graph matching and evolutionary approach for sport scheduling problem

2020 ◽  
pp. 1-16
Author(s):  
Meriem Khelifa ◽  
Dalila Boughaci ◽  
Esma Aïmeur
Keyword(s):  
Graph Matching ◽  
State Of The Art ◽  
Travel Cost ◽  
Round Robin ◽  
New Approach ◽  
Traveling Tournament Problem ◽  
Significant Interest ◽  
National League ◽  
Better Than

The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing

2021 ◽  
Vol 13 (2) ◽  
pp. 50
Author(s):  
Hamed Z. Jahromi ◽  
Declan Delaney ◽  
Andrew Hines
Keyword(s):  
Web Application ◽  
Web Applications ◽  
State Of The Art ◽  
Influencing Factor ◽  
The State ◽  
Experimental Result ◽  
Web Page ◽  
To Come ◽  
Web Quality ◽  
The Web

Content is a key influencing factor in Web Quality of Experience (QoE) estimation. A web user’s satisfaction can be influenced by how long it takes to render and visualize the visible parts of the web page in the browser. This is referred to as the Above-the-fold (ATF) time. SpeedIndex (SI) has been widely used to estimate perceived web page loading speed of ATF content and a proxy metric for Web QoE estimation. Web application developers have been actively introducing innovative interactive features, such as animated and multimedia content, aiming to capture the users’ attention and improve the functionality and utility of the web applications. However, the literature shows that, for the websites with animated content, the estimated ATF time using the state-of-the-art metrics may not accurately match completed ATF time as perceived by users. This study introduces a new metric, Plausibly Complete Time (PCT), that estimates ATF time for a user’s perception of websites with and without animations. PCT can be integrated with SI and web QoE models. The accuracy of the proposed metric is evaluated based on two publicly available datasets. The proposed metric holds a high positive Spearman’s correlation (rs=0.89) with the Perceived ATF reported by the users for websites with and without animated content. This study demonstrates that using PCT as a KPI in QoE estimation models can improve the robustness of QoE estimation in comparison to using the state-of-the-art ATF time metric. Furthermore, experimental result showed that the estimation of SI using PCT improves the robustness of SI for websites with animated content. The PCT estimation allows web application designers to identify where poor design has significantly increased ATF time and refactor their implementation before it impacts end-user experience.

scholarly journals Cache-efficient sweeping-based interval joins for extended Allen relation predicates

2021 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer ◽  
Anton Dignös ◽  
Fabio Persia
Keyword(s):  
Data Structure ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Temporal Databases ◽  
Access Method ◽  
Wide Range ◽  
Interval Relation ◽  
Cache Efficient ◽  
Join Algorithms ◽  
Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

Sign in / Sign up

Export Citation Format

Share Document