Search Results - (( using optimization (problems OR problem) algorithm ) OR ( using function a algorithm ))
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1
Optimization of mycelium growth using genetic algorithm for multi-objective functions
Published 2019“…Mathematical optimization was typical use for such problem, in which it was supposed to maximizing or minimizing a function. …”
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Undergraduates Project Papers -
2
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…Applications and improvements to the HKA algorithm suggest that optimization algorithm based on estimation principle has a huge potential in solving a wide variety of optimization problems. …”
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3
An efficient method for determining all the extreme points of function with one variable
Published 2014“…The algorithm is directly suitable for a class of problems of commonly used in solving the global optimization problems. …”
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4
Levy slime mould algorithm for solving numerical and engineering optimization problems
Published 2022“…One classical engineering problem known as the welded beam structure problem is used to test the proposed LSMA algorithm's efficacy. …”
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5
A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization
Published 2020“…However, it is impossible to fnd an optimization algorithm that can obtain the global optimum for every optimization problem. …”
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Article -
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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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7
Levy tunicate swarm algorithm for solving numerical and real-world optimization problems
Published 2022“…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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A discrete simulated kalman filter optimizer for combinatorial optimization problems
Published 2022“…Two types of analysis are used to evaluate the proposed algorithm. First, the DSKFO algorithm is used to solve the travelling salesman problem (TSP), and then the algorithm's execution time is measured. …”
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10
Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems
Published 2023“…Stochastic gradient descent (SGD) is one of the most common algorithms used in solving large unconstrained optimization problems. …”
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Article -
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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
Published 2024journal::journal article -
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Vector Evaluated Gravitational Search Algorithm (VEGSA) for multi-objective optimization problems
Published 2012“…This paper presents a novel algorithm, which is based on Gravitational Search Algorithm (GSA), for multiobjective optimization problems. …”
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13
Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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Undergraduates Project Papers -
14
Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application
Published 2024“…By employing such a function, simulation results show that the total resource used by the agents and total communication cost can be optimized. …”
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15
Hybrid harmony search algorithm for continuous optimization problems
Published 2020“…The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. …”
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16
Optimised Crossover Genetic Algorithms for Combinatorial Optimisation Problems
Published 2010“…Most of the current methods of crossover determine o®spring by using a stochastic approach and without reference to the objective function with the result being that a potentially optimal solution could be lost through incorrect choices being made in the randomisation process. …”
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17
Solving 0/1 Knapsack Problem Using Hybrid HS and Jaya Algorithms
Published 2018“…In this research, a new hybrid algorithm of Harmony search and Jaya search algorithms applied on 0/1 Knapsack problem to find a near optimal results. …”
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Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints
Published 2020“…In this study, an assessment of metaheuristic algorithms to optimize MMALBP was conductedby using four popular metaheuristics , namely particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization (ACO),and genetic algorithm (GA). …”
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19
Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid
Published 2012“…In computational grid, job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. …”
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Monograph -
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Hybrid evolutionary optimization algorithms: A case study in manufacturing industry
Published 2014“…This membership function is applied for its useful performance through industrial production problems by employing hybrid evolutionary optimization algorithms. …”
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