Search Results - (( developing using meta algorithm ) OR ( java application testing algorithm ))

RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Development of I-Complain@META: a complaint management system for FKMT students using naive bayes classification algorithm by Cheah, Jia Ni

“…Development of I-Complain@META: a complaint management system for FKMT students using naive bayes classification algorithm by Cheah, Jia Ni…”
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Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization by Kamal Z., Zamli

“…Many meta-heuristic algorithms have been developed to date (e.g. …”
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Proposal of meta-requirement approach to analyze requirements specification completeness by Muhamad Idaham, Umar Ong, M. A., Ameedeen

“…The objectives of this research are to identify the major factors in validating user requirements, development of a reverse engineered meta-requirement algorithm and validating with expert panel in requirements engineering of the algorithm usefulness. …”
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Meta-requirement method towards analyzing completeness of requirements specification by Muhamad Idaham, Umar Ong, Mohamed Ariff, Ameedeen, Imran Edzereiq, Kamarudin

“…However, due to time constraint, requirements validation steps are commonly ignored by developer. The objectives of this research are to identify the major factors in validating user requirements, development of a reverse engineered meta-requirement algorithm and validating with expert panel in requirements engineering of the algorithm usefulness. …”
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An optimal LFC in two-area power systems using a meta-heuristic optimization algorithm by Najeeb M., Mansor M., Feyad H., Taha E., Abdullah G.

“…This meta-heuristic algorithm is called harmony search (HS), it is used to tune PI controller parameters (Kp, Kt) automatically. …”

Elitist hybrid migrating birds optimization and genetic algorithm based strategy for T-way test suite generation by Hasneeza Liza, Zakaria

“…Although useful, much existing meta-heuristic based strategies have not sufficiently explored the adoption of more than one meta-heuristic to perform the search (termed hybridization). …”
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Meta-heuristic structure for multiobjective optimization case study: Green sand mould system by Ganesan, T., Elamvazuthi, I., KuShaari, K.Z., Vasant, P.

“…Analysis on the solution set produced by these algorithms is carried out using performance metrics. …”
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Development of lung cancer prediction system using meta-heuristic optimized deep learning model by Mohamed Shakeel, Pethuraj

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A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch by Mohamad Ridzuan, Mohamad Radzi, Hassan, Elia Erwani, Abdullah, Abdul Rahim, Bahaman, Nazrulazhar, Abdul Kadir, Aida Fazliana

“…The proposed optimization algorithm, namely New Meta-Heuristic Evolutionary Programming (NMEP) algorithm is followed to Meta-Heuristic Evolutionary Programming (Meta-EP) approach with some modification where the cloning process embedded as a significant progress during the implementation. …”
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Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic by Sa'ad, Suleiman, Abdullah, Muhammed, Abdullah, Azizol, Ayob, Fahrul Hakim

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Comparative Performance Analysis of Bat Algorithm and Bacterial Foraging Optimization Algorithm using Standard Benchmark Functions by Mazlina, Abdul Majid, Alsariera, Yazan A., Alamri, Hammoudeh S., Nasser, Abdullah M., Kamal Z., Zamli

“…Over the last 30 years, many meta-heuristic algorithms have been developed in the literature including that of Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) to name a few. …”
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Embedded Meta evolutionary-firefly algorithm-ANN for multi dg planning in distribution system / Siti Rafidah Abdul Rahim by Abdul Rahim, Siti Rafidah

“…In this study, Meta Evolutionary–Firefly Algorithm (EMEFA) was initially developed to expedite the computational time in multi-DG installation with improved accuracy. …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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An Environmentally Energy Dispatch Using New Meta Heuristic Evolutionary Programming by Mohamad Ridzuan, Mohamad Radzi

“…Basically,one important issue in the power system network is to provide the optimal Economic Load Dispatch (ELD) solution in order to guarantee the sustainable consumer load demand.However,today ELD solution is essential to include together with the environmental aspect and known as Environmental Economic Load Dispatch (EELD).For that reason, many researchers continue in the development of new simulation tool specifically to overcome the EELD problems.Therefore,this study prepared an improved hybrid metaheuristic technique named as New Meta Heuristic Evolutionary Programming (NMEP) to provide the best possible solution in solving the identified single objective and multi objective functions for EELD solution.This new technique a merging cloning strategy that involved in an Artificial Immune System (AIS) algorithm into algorithm of Meta Heuristic Evolutionary Programming (Meta-EP).The development of NMEP technique is to minimize total cost,reduce the total emission during generator operation through the common formula in EELD and lowest total system loss.Besides that,all mentioned objective functions were also optimized together simultaneously that formulated using the weighted sum method before had been executed on the multi objective NMEP or called MONMEP.Both individual and multi objective NMEP techniques performance were verified among other two common heuristic methods known as AIS and Meta-EP techniques.In addition,the best possible solution defined using the aggregate function method.Through this method,the selection of the best MOEELD solution became effortless as compared with MO individually that required compare two or more objective function in one time manually.Among those three optimization techniques the lowest total aggregate values mostly resulted via the NMEP technique.Based upon that,the proposed technique is proving as the outstanding method compared with Meta-EP and AIS techniques in solving the EELD problem for both standard IEEE 26 bus and 57 bus systems.…”
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Comparative Analysis of Neighborhood based Meta-heuristic Algorithms for MC/DC Test Data Generation by Ariful, Haque, Kamal Z., Zamli

“…Most implementations have been focused on neighborhood-based meta-heuristics. In order to help test engineers to make informed decision on the best neighborhood based implementations, this paper investigates the size and time performance of two MC/DC test strategies re-implementation based on Simulated Annealing against two newly developed strategies based on Great Deluge and Late Acceptance Hill Climbing algorithms respectively. …”
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Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays by Prajindra�Sankar K., Kiong T.S., Siaw�Paw J.K.

Multi-state PSO GSA for solving discrete combinatorial optimization problems by Ismail, Ibrahim

“…These four algorithms can be used to solve discrete combinatorial optimization problems (COPs). …”
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Locust- inspired meta-heuristic algorithm for optimising cloud computing performance by Fadhil, Mohammed Alaa

“…The simulation results demonstrate that the proposed algorithm outperforms existing heuristic and meta-heuristic algorithms, including the benchmarking algorithm (LACE). …”
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An Experimental Study of Hyper-heuristic Selection and Acceptance Mechanism for Combinatorial T-Way Test Suite Generation by Kamal Z., Zamli, Fakhrud, Din, Kendall, Graham, Ahmed, Bestoun S.

“…Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. …”
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