Search Results - (( global optimization _ algorithm ) OR ( based application search algorithm ))*
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A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems
Published 2018“…However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithm and Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. …”
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An adaptive gravitational search algorithm for global optimization
Published 2023“…Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. …”
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Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy
Published 2014“…To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. …”
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Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
Published 2013“…To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. …”
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A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
Published 2017“…Purpose – This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). …”
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A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
Published 2019“…Metaheuristic algorithms have received much attention recently for solving different optimization and engineering problems. …”
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An improved electromagnetism-like mechanism algorithm for the optimization of maximum power point tracking / Tan Jian Ding
Published 2017“…The Electromagnetism-Like Mechanism algorithm (EM) is a meta-heuristic algorithm designed to search for global optimum solutions using bounded variables. …”
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A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
Published 2024“…Additionally, a rank-based selection scheme is adopted to choose best half of population for subsequent global and local search modes. …”
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Development of heuristic methods based on genetic algorithm (GA) for solving vehicle routing problem
Published 2008“…Genetic Algorithm as population-based methods are better identifying promising areas in the search space, while Tabu Search and Simulated Annealing as trajectory methods are better in exploring promising areas in search space. …”
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Monograph -
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Improved particle swarm optimization by fast annealing algorithm
Published 2019“…The proposed algorithm is meant to solve high dimensional optimization problems based on two strategies, which are utilizing the particle swarm optimization to define the global search area and utilizing the fast-simulated annealing to refine the visited search area. …”
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Proceeding Paper -
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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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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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An Improved Hybrid of Particle Swarm Optimization and the Gravitational Search Algorithm to Produce a Kinetic Parameter Estimation of Aspartate Biochemical Pathways
Published 2017“…The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. …”
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Exponentially adaptive sine-cosine algorithm for global optimization
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Conference or Workshop Item -
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Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization
Published 2011“…Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. …”
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Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems
Published 2016“…The performance of the proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms,namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA). …”
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A modified conjugate gradient coefficient with inexact line search for unconstrained optimization
Published 2016“…Conjugate gradient (CG) method is a line search algorithm mostly known for its wide application in solving unconstrained optimization problems. …”
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Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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