Search Results - (( using function method algorithm ) OR ( global optimization modified algorithm ))
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1
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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2
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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Thesis -
3
An improved artificial immune system based on antibody remainder method for mathematical function optimization
Published 2023“…Artificial immune system (AIS) is one of the nature-inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. …”
Conference paper -
4
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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5
New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems
Published 2024“…However, the complexity significantly rises when dealing with multi objective functions. The Modified Adaptive Bats Sonar Algorithm (MABSA), initially designed for single objective optimization and inspired by colony bats' echolocation, has demonstrated efficiency with its simple structure and reduced computation time. …”
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6
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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7
An Improved Artificial Immune System Based On Antibody Reminder Method For Mathematical Function Optimization
Published 2010“…Artificial immune system (AIS) is one of the nature inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. …”
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8
Mathematical function optimization using AIS antibody remainder method
Published 2011“…Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. …”
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Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization
Published 2011“…Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. …”
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10
A Descent Three-Term Conjugate Gradient Direction For Problems Of Unconstrained Optimization With Application
Published 2026journal::journal article -
11
Modified quasi-Newton type methods using gradient flow system for solving unconstrained optimization
Published 2016“…Other than that, we also analyse the global convergence properties of the modified methods. …”
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Thesis -
12
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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Combined heat and power (CHP) economic dispatch solved using Lagrangian relaxation with surrogate subgradient multiplier updates
Published 2023“…The higher level is the optimization of the surrogate dual function for the relaxed global constraints in which the surrogate subgradient is used to update the Lagrangian multipliers. …”
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15
Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling
Published 2022“…The discrete MOPSO also includes the modified maximin fitness function to promote solution diversity in the selection of global best solutions. …”
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16
Modelling of multi-robot system for search and rescue
Published 2023“…The PSO algorithm functions as a global path planner, determining the complete path for each robot, whereas a sensor-based obstacle avoidance algorithm serves as a local planner to avoid collision with dynamic obstacles during navigation. …”
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Final Year Project / Dissertation / Thesis -
17
Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…The objectives are to enhance the performance of crossover operator for GA, simulate SI using GA with the SPM technique, and compare the performance of the modified GA to traditional GA in terms of prediction accuracy, convergence to global optimum, and model parsimony. …”
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18
A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis
Published 2025“…To further enhance COVID-19 lesion estimation, novel optimization strategies, including a hybrid simulated annealing-cuckoo search (SA-CS) algorithm, are introduced alongside the original SA method. …”
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Analysis of the effect of search step size on the accuracy and convergence properties of electromagnetism-like mechanism algorithm
Published 2023“…Benchmarking; Global optimization; Convergence performance; Convergence properties; Electromagnetism-like mechanism algorithms; Global optimization problems; Meta-heuristic search algorithms; Metaheuristic; Modified algorithms; Search steps; Optimization…”
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Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm
Published 2021“…This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. …”
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