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1
Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions
Published 2018“…The second technique is to solve bi-objective functions by using the BOBAT algorithm. The third technique is an integration of BOGSA with BOBAT to produce a BOGSBAT algorithm. …”
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2
An adaptively switching iteration strategy for population based metaheuristics / Nor Azlina Ab. Aziz
Published 2017“…Experiments conducted using three parent algorithms namely particle swarm optimization (PSO), which is a popular population-based optimizer with population and individual memories, gravitational search algorithm (GSA), a memoryless young optimizer, and simulated Kalman filter (SKF), a newly introduced optimization algorithm that use population’s memory to guide an agent’s search, show that iteration strategy is an algorithm dependent parameter as well as function dependent. …”
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3
Optimized processing of satellite signal via evolutionary search algorithm
Published 2000“…The PRSS algorithm is an adaptive search technique that can learn a high performance knowledge structure in reactive environments that provide information as an objective function. …”
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4
A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem
Published 2021“…To speed up the convergence, modifications of: (i) employing chaos theory in the initialisation of initial population to enhance the diversity of the initial population in the search space, (ii) replacing FPA’s local search strategy with frog leaping algorithm to improve intensification, and (iii) integrating inertia weight into FPA’s global search strategy to adjust the searching ability of the global strategy, were presented. …”
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5
Opposition-based spiral dynamic algorithm with an application to optimize type-2 fuzzy control for an inverted pendulum system
Published 2022“…They are also applied to optimize a type-2 fuzzy logic controller for an inverted pendulum system in comparison to SDA, Spotted Hyena Optimizer, Tunicate Swarm Algorithm, and Sooty Tern Optimization Algorithm. …”
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An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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10
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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11
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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12
The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
Published 2006“…Recently, the use of genetic algorithm for the optimization of technical trading strategies has been receiving a great deal of attention. …”
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13
Comparison of performances of Jaya Algorithm and Cuckoo Search algorithm using benchmark functions
Published 2022“…This paper aims to compare the performance of two metaheuristic algorithms which are Jaya Algorithm (JA) and Cuckoo Search (CS) using some common benchmark functions. …”
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14
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…The proposed population-based SKF algorithm and the single solution-based SKF algorithm use the scalar model of discrete Kalman filter algorithm as the search strategy to overcome these flaws. …”
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15
Enhancing harmony search parameters based on step and linear function for bus driver scheduling and rostering problems
Published 2018“…Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. …”
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16
Improved flower pollination optimization algorithm based on swap operator and dynamic switch probability selection
Published 2023“…Furthermore, it is hybridized with the Pattern Search algorithm to ensure the optimality of the solution. …”
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17
A new HMCR parameter of harmony search for better exploration
Published 2016“…This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. …”
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A new HMCR parameter of harmony search for better exploration
Published 2015“…This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. …”
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Comparative analysis of line search methods in the Steepest Descent algorithm for unconstrained optimization problems / Ahmad Zikri Shukeri, Puteri Qurratu Ain Megat Sulzamzamendi...
Published 2024“…The result from this study is choosing FMRI algorithm using exact line search to get faster convergence rate which means that the algorithm achieves a high level of accuracy in fewer iterations compared to using other algorithms and inexact line search.…”
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The effect of job satisfaction on the relationship between organizational culture and organizational performance
Published 2023“…Furthermore, it is hybridized with the Pattern Search algorithm to ensure the optimality of the solution. …”
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