Search Results - (( using optimization modified algorithm ) OR ( global optimization method algorithm ))
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1
Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…A set of modified and novel optimization algorithms are proposed in this thesis to deal with different single and multi-objective OPF problems. …”
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2
A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
Published 2024“…Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. …”
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3
A modified discrete filled function algorithm for solving nonlinear discrete optimization problems
Published 2012“…The discrete filled function method is a global optimization tool for searching for best solution amongst multiple local optima.This method has proven useful for solving large-scale discrete optimization problems.In this paper, we consider a standard discrete filled function algorithm in the literature and then propose a modification to increase its efficiency.…”
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4
Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…Moreover, interpretability also recorded better results on testing problems, where most of the number of rules were fewer than 33. A clustering algorithm based on MOPSO-CD with a modified archive update mechanism (MCPSO-CD) was used to estimate the optimal number of clusters. …”
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5
Memoryless modified symmetric rank-one method for large-scale unconstrained optimization
Published 2009“…In this study, we present a scaled memoryless modified Symmetric Rank-One (SR1) algorithm and investigate the numerical performance of the proposed algorithm for solving large-scale unconstrained optimization problems. …”
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6
New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems
Published 2024“…In the fourth phase, the newly developed algorithm undergoes testing on the formulated ROOPs and compared to several contemporary optimizer algorithms. …”
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7
Performance analysis of a modified conjugate gradient algorithm for optimization models
Published 2021“…The Conjugate gradient (CG) algorithms is very important and widely used in solving optimization models. …”
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8
An improved artificial immune system based on antibody remainder method for mathematical function optimization
Published 2023“…Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. …”
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New Quasi-Newton Equation And Method Via Higher Order Tensor Models
Published 2010“…By using a new equation, the BFGS method is modified. …”
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10
CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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11
Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization
Published 2011“…Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. …”
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12
Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization
Published 2011“…Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. …”
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13
An Improved Artificial Immune System Based On Antibody Reminder Method For Mathematical Function Optimization
Published 2010“…Alternatively, Genetic Algorithms (GAS) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. …”
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14
Mathematical function optimization using AIS antibody remainder method
Published 2011“…Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. …”
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15
Combined heat and power (CHP) economic dispatch solved using Lagrangian relaxation with surrogate subgradient multiplier updates
Published 2023“…In addition this paper illustrates the ear clipping method used to modify the common nonconvex feasible region of CHP benchmark problems to a convex region which subsequently enhances the search for an optimal solution. …”
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Modified quasi-Newton type methods using gradient flow system for solving unconstrained optimization
Published 2016“…We investigate the possible use of control theory, particularly theory on gradient ow system to derive some modified line search and trust region methods for optimization. …”
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17
Artificial immune system based remainder method for multimodal mathematical function optimization
Published 2023“…Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. …”
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Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling
Published 2022“…This research first proposes an improved continuous MOPSO to address the rapid clustering problem that exists in the basic PSO algorithm using three improvement strategies: re-initialization of particles, systematic switch of best solutions and mutation on global best selection. …”
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19
Extending the decomposition algorithm for support vector machines training
Published 2003“…Numerical problems will cause the training to give non- optimal decision boundaries. Using a conventional optimizer to train SVM is not the ideal solution. …”
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