Search Results - (( solution deviation selection algorithm ) OR ( java application stemming algorithm ))

A Standard Deviation Selection in Evolutionary Algorithm for Grouper Fish Feed Formulation by Soong, Cai Juan, Razamin, Ramli, Rosshairy, Abdul Rahman

“…Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable. …”
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A standard deviation selection in evolutionary algorithm for grouper fish feed formulation by Soong, Cai Juan, Ramli, Razamin, Abdul Rahman, Rosshairy

“…Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment.Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price.However, the demand of grouper fish is still insufficient from the wild catch.Therefore, there is a need to farm grouper fish to cater to the market demand.In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available.Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm.Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation.Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable.The feasible and low fitness, quick solution can be obtained. …”
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Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm by Muhammad, Munir Azam, Mokhlis, Hazlie, Amin, Adil, Naidu, Kanendra, Franco, John Fredy, Wang, Li, Othman, Mohamadariff

“…Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near-optimal solution for NR and DG size. …”
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Optimal Reactive Power Dispatch Solution by Loss Minimization Using Moth-Flame Optimization Technique by Rebecca Ng, Shin Mei, Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Hamdan, Daniyal

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Efficient Time-Varying q-Parameter Design for q-Incremental Least Mean Square Algorithm with Noisy Links by Arif, M., Khan, S.S., Qadri, S.S.U., Naseem, I., Moinuddin, M.

“…Furthermore, the closed-form solutions for the steady-state mean square deviation, excess mean square deviation and mean square error are derived. …”
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An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation by Soong, Cai Juan, Abd Rahman, Rosshairy, Ramli, Razamin, Abd Manaf, Mohammed Suhaimee, Chek-Choon, Ting

“…Therefore, this paper introduces binary-standard deviation (SD) tournament selection into EA as an enhancement of BT that can lead to focus on more exploration in terms of searching for the best solutions. …”
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Variable Neighbourhood Search Algorithm for Vehicle Routing Problem with Backhaul by Siaw Ying Doreen, Sek

“…A set of local search approaches and random shaking methods are proposed to conduct a list of neighbourhood solutions. Then, the most optimum solution among the neighbourhood solution in the improvement phase is selected as the final outcome of this research. …”
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Optimal power flow solutions for power system operations using moth-flame optimization algorithm by Salman Ameen Ali, Abdullah Alabd

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Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method by Lawal, Sani Mohammed

“…PSO is among the meta-heuristics search methods like Genetic Algorithm (GA) but has been found to be computationally efficient, because it uses less number of functions for evaluation compared to GA that has genetic operators (Selection, crossover and mutation) and also the computational effort (time) required by PSO to arrive at high quality solutions is less than the effort required to the same high quality solutions by other heuristic search methods. …”
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Optimal power flow solutions for power system operations using moth-flame optimization algorithm by Alabd, Salman, Mohd Herwan, Sulaiman, Muhammad Ikram, Mohd Rashid

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Optimal distribution system reconfiguration incorporating distributed generation based on simplified network approach / Mohammad Al Samman by Mohammad , Al Samman

“…With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and voltage deviation of the EDN, this work proposes a new method based on a two-stage optimization. …”
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Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

An Improved Evolutionary Algorithm in Formulating a Diet for Grouper by Cai-Juan, Soong, Abd Rahman, Rosshairy, Ramli, Razamin

“…Subsequently, the novel selection operator embeds the concept of standard deviation in the SR-SD-EA as part of the function in minimizing the total cost of the formulated grouper fish feed. …”
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Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin by Ong, Chung Sin

“…In general this algorithm produces less than 6% deviation when compared to the best known solutions, especially in larger problems consisting of 20 jobs and 15 machines.…”
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Petroleum Refinery Planning Under Uncertainty: A Multiobjective Optimization Approach with Economic and Operational Risk Management by Van Fu Shen, Fu Shen

“…The two stage stochastic risk model is then reformulated using Mean Absolute Deviation as the risk measure. After formulating the stochastic model using Mean Absolute Deviation, the problem is then investigated using the Pareto front solution of efficient frontier of the resulting multiobjective optimization problem by using the Weighted Sum Method as well as the ε-constraint method in order to obtain the Pareto Optimal Curve which generates a wide selection of optimization solutions for our problem. …”
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Petroleum Refinery Planning Under Uncertainty: A Multiobjective Optimization Approach with Economic and Operational Risk Management by Van, Fu Shen

“…The two stage stochastic risk model is then reformulated using MeanAbsolute Deviation as the risk measure. After formulating the stochastic model using Mean Absolute Deviation, the problem is then investigated using the Pareto front solution of efficient frontier of the resulting multiobjective optimization problem by using the Weighted SumMethod as well as the e-constraint method in order to obtain the Pareto Optimal Curve which generates a wide selection of optimization solutions for our problem. …”
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A genetic algorithm to minimise the maximum lateness on a single machine family scheduling problem by Lee, Lai Soon, Nazif, Habibeh

“…Various techniques are also introduced to further enhance the solution quality. The OCGA is compared with other well known local search method namely dynamic length tabu search, randomised steepest descent method, and other variants of genetic algorithms using extensive data sets collected from the literatures. …”
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Optimization of process parameters for rapid adsorption of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite using wavelet neural network by Darajeh, Negisa, Masoumi, Hamid Reza Fard, Kalantari, Katayoon, Ahmad @ Ayob, Mansor, Shameli, Kamyar, Basri, Mahiran, Khandanlou, Roshanak

“…After minimizing this error, the topologies of the algorithms were compared based on the coefficient of determination and absolute average deviation. …”
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Enhancing high-dimensional streaming data analysis: optimizing Online Feature Selection for handling drift using optimization technique and ensemble learning by Kamaru-Zaman, Ezzatul Akmal

“…In the era of data-driven decision-making, managing dynamic data streams characterized by evolving data distributions and high dimensionality presents a formidable challenge for online feature selection. This research addresses the challenge by devel-oping innovative solutions in optimizing Online Feature Selection (OFS) to manage feature irrelevancy and redundancy, tackling the issues of Feature Drift, and rigor-ously validating the proposed algorithms in high-dimensional dynamic data streams. …”
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Multi-objective portfolio selection with skewness preference: An application to the stock and electricity markets / Karoon Suksonghong by Karoon, Suksonghong

“…The superiority of this method is its ability to generate a set of MVS efficient portfolios within a single run of algorithm. The non-dominated sorting genetic algorithm II (NSGA-II), the improved strength Pareto evolutionary algorithm II (SPEA-II), and the compressed objective genetic algorithm II (COGA-II) were applied. …”
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