Search Results - (( solution construction method algorithm ) OR ( program implementation bee algorithm ))
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Offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm: article / Deezex Noor Ainizaa Abdullah
Published 2009“…This paper present offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm. The study involves the development of Artificial Bee Colony (ABC) algorithm to implement loss minimization in a distribution system. …”
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Article -
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Offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm / Deezex Noor Ainizaa Abdullah
Published 2009“…This paper present offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm. The study involves the development of Artificial Bee Colony (ABC) algorithm to implement loss minimization in a distribution system. …”
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Thesis -
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A study on solution of matrix riccati differential equations using ant colony programming and simulink / Mohd Zahurin Mohamed Kamali
Published 2015“…In this thesis, we implement the modified ant colony programming (ACP) algorithm for solving the matrix Riccati differential equation (MRDE). …”
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Deterministic and stochastic inventory routing problems with backorders using artificial bee colony / Huda Zuhrah Ab Halim
Published 2019“…A new MILP for DSIRPB is formulated and used within the algorithm. The DSIRPB is modeled as stochastic dynamic programming and solved using hybrid rollout algorithm. …”
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5
Sequential constructive algorithm incorporate with fuzzy logic for solving real world course timetabling problem
Published 2020“…Sequential constructive algorithm is one of the popular methods for solving timetabling problems. …”
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Conference or Workshop Item -
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Sequential Constructive Algorithm incorporate with Fuzzy Logic for Solving Real World Course Timetabling Problem
Published 2020“…Sequential constructive algorithm is one of the popular methods for solving timetabling problems. …”
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Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi M...
Published 2016“…In order produce a global optimal solution of AUC, the self-adaptive strategy was proposed to serve as a new mutation technique responsible to provide a new population for discrete artificial bee colony. The newly designed algorithm is termed as the discrete artificial bee colony associated with selfadaptive strategy (DABCSAS). …”
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Grey Wolf Optimizer for Solving Economic Dispatch Problems
Published 2014“…In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 20 generating units in economic dispatch, and the results are verified by a comparative study with Biogeography-based optimization (BBO), Lambda Iteration method (LI), Hopfield model based approach (HM), Cuckoo Search (CS), Firefly, Artificial Bee Colony (ABC), Neural Networks training by Artificial Bee Colony (ABCNN), Quadratic Programming (QP) and General Algebraic Modeling System (GAMS). …”
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Conference or Workshop Item -
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Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi M...
Published 2017“…In order produce a global optimal solution of AUC, the self-adaptive strategy was proposed to serve as a new mutation technique responsible to provide a new population for discrete artificial bee colony. The newly designed algorithm is termed as the discrete artificial bee colony associated with selfadaptive strategy (DABCSAS). …”
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Optimised Crossover Genetic Algorithms for Combinatorial Optimisation Problems
Published 2010“…A Genetic Algorithm is successful in generating near -optimal solutions if it is able to produce o®spring during crossover that is better than the parent solutions. …”
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Thesis -
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Enhanced Intelligent Water Drops Algorithm for University Examination Timetabling Problems
Published 2024“…(iii) It is a constructive-based meta-heuristic algorithm which means, the solution is incrementally constructed (step by step) until the complete solution is obtained (Noferesti and Shah-Hosseini, 2012). …”
thesis::doctoral thesis -
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Power system network splitting and load frequency control optimization using ABC based algorithms / Kanendra Naidu a/l Vijyakumar
Published 2015“…This research presents a modified optimization program for the system splitting problem in large scale power system based on Artificial Bee Colony algorithm and graph theory. …”
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15
Centered fan field region under wedge indentation by numerical solution method
Published 2019“…The stress field obtained were then compared to the analytical solution. This solution method provides a simple algorithms that is easy to be applied in deformation and this will subsequently provide a contribution in designing tools and the construction industries involving granular materials.…”
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Article -
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Development of a Negotiation Support Model for Value Management in Construction
Published 2009“…It combines simultaneous triangulation by using case studies and survey methods and sequential triangulation in which results of one method are essential in planning the next method (theoretical mapping, survey research, focus group, case study and conceptual modeling). …”
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Final Year Project -
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Enhancement of Ant System Algorithm for Course Timetabling Problem
Published 2009“…For this kind of problem, researchers believe that it will be better to find an approximate solution that can be delivered by a stochastic algorithm than waiting for an exact solution from the deterministic algorithm. …”
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Ball surface representations using partial differential equations
Published 2015“…Hence, this new algorithm is a viable alternative for constructing generalized Ball surfaces. …”
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Genetic algorithm techniques for the design of nonlinear microwave circuits
Published 2004“…By using Sample Balance's differential of current linear and nonlinear equation as an objective function, a Genetic Algorithm routine then been constructed. Genetic Algorithm will then recombine, mutate and evaluate the population of solution to find the best solution based on Sample balance equation. …”
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Fast Finite Difference Time Domain Algorithms for Solving Antenna Application Problem
Published 2008“…The objective of the thesis is to develop new sequential and parallel algorithms that are faster than the standard Finite Difference Time Domain method. …”
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