Search Results - (( programming using function algorithm ) OR ( based evaluation optimization algorithm ))

Evaluation of fast evolutionary programming, firefly algorithm and mutate-cuckoo search algorithm in single-objective optimization / Muhammad Zakyizzuddin Rosselan, Shahril Irwan S... by Rosselan, Muhammad Zakyizzuddin, Sulaiman, Shahril Irwan, Othman, Norhalida

“…In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. …”
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Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman by Azman, Alya Kauthar

“…The research adopts a Genetic Algorithm-based approach, where examination scheduling details such as date, time, course code, program code, student group, student numbers, and available spaces are encoded as chromosomes. …”
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Optimal demand response of solar energy generation using Genetic Algorithm / Muhammad Asyraaf Adlan by Adlan, Muhammad Asyraaf

“…The method involves collecting data from the solar energy project of UiTM Kampus Dungun, implementing GA for optimization, and evaluating its performance based on the fitness value and convergence trends. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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Online Programming Judge System (UOJ) by Tran , Quang Trung

“…This research conducts a study to build an Online Programming Judge system with a mechanism to generate test cases automatically using Particle Swarm Optimization (PSO) algorithm. …”
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An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

“…The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyperheuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time.…”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

“…Second task is to determine the fitness function for the genetic algorithms. The third objective is to optimize the land use map using economic benefits. …”
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Optimization Of Fuzzy Logic Controllers With Genetic Algorithm For Two-Part-Type And Re-Entrant Production Systems by Homayouni, Seyed Mahdi

“…Furthermore, genetic algorithm (GA) has been used to optimize the FLCs performance. …”
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Application of Multi-objective Genetic Algorithm (MOGA) optimization in machining processes by Nor Atiqah, Zolpakar, Lodhi, Swati Singh, Pathak, Sunil, Sharma, Mohita Anand

“…Thus, by implementing MOGA approach that based on the natural biological evaluation principle will be used to tackle this kind of problem. …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm by Banadkooki F.B., Ehteram M., Ahmed A.N., Teo F.Y., Fai C.M., Afan H.A., Sapitang M., El-Shafie A.

“…The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network�whale algorithm (WA) model, the multilayer perception (MLP�WA) model, and genetic programming (GP) were used to predict GWL. …”

Computational intelligence based power system security assessment and improvement under multi-contingencies conditions / Nor Rul Hasma Abdullah by Abdullah, Nor Rul Hasma

“…The minimizations of SVSI and transmission loss are used as two separate objective functions for the development of optimization technique. …”
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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures by Koohi, Sina Zangbari

“…For these reasons, to improve the time and accuracy of the coverage in population-based meta-heuristics and their utilization in HPAs, this thesis presents a novel optimization algorithm called the Raccoon Optimization Algorithm (ROA). …”
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Study on the microbial growth inhibition by using pineapple leaves juice by Amirah, Ya'acob

“…The minimizations of SVSJ and transmission loss are used as two separate objective functions for the development of optimization technique. …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail by Ismail, Nor Laili

“…The third algorithm was validated on IEEE 30-Bus RTS only. Comparative studies were conducted concerning the traditional Evolutionary Programming (EP) and Barnacles Mating Optimiser (BMO) for performance evaluation of HEBMO and MOHEBMO. …”
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Computational intelligence based power system security assessment and improvement under multicontigencies conditions by Nor Rul Hasma, Abdullah

“…The minimizations of SVSJ and transmission loss are used as two separate objective functions for the development of optimization technique.The effect of reactive power load variation.on transmission loss in the system is also investigated.Consequently,the EP optimization technique is extended for the evaluation of the operating generator scheduling (OGS) to be applied on reactive power control in power system.The results obtained from the study can be used by the power system operators to make a decision either to achieve minimal SVSJ, minimal transmission loss or minimal installation cost.This has also avoided all generators to dispatch power at the same time.Finally,a novel multi-objective Constrained Reactive Power Control (CRPC) algorithm using the state-of-the-art of EP for voltage stability improvement has been developed.A performance comparison with Artificial Immune System (AIS) in terms of SVSJ and loss minimization was made and it is found that the proposed algorithm has been able to produce better results as compared to AIS.The contributions of the studies among the others are the development EP and AIS engine for CPP considered multi-contingencies (N-ni),the development of EP and AIS engine for FACTS installation considered multi-contingencies(N-m) for the determination of FACTS placement using SVSI and optimal sizing of FACTS using EP and AIS, the development of new technique for OGS based on EP optimization technique and the development of multi-objective EP and AIS engines for CRPC considered multi-contingencies (N-m).…”
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A Compromise Programming to Task Assignment Problem in Software Development Project by Son, N.T., Jaafar, J., Aziz, I.A., Anh, B.N., Binh, H.D., Aftab, M.U.

“…To support the decision-maker to evaluate different decision strategies, we use compromise programming to transform multiobjective optimization (MOP) into a single-objective problem. …”
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Optimal forwarding routing protocol in ipv6-based wireless sensor network by Jamil, Ansar

“…In WSNs, TinyOS is the most widely used operating system. Thus, programming code of the OF routing protocol algorithm is written using NesC language, which is a high level programming language that emulates the syntax and functionality of hardware description languages, and provides components for communication, thread coordination, and hardware abstraction. …”
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Development of an integrated scheduling model for handling equipment in automated port container terminals by Sadeghian, Syed Hamidreza

“…In addition, results proved that the modified meta-heuristic algorithm is able to find near optimal solutions and on average, the solutions found by the GA algorithm are only 0.2% worse than the optimal solutions and in the worst case in the test cases this difference is 2.3% which is acceptable. …”
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