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8 articles for “Local minima”
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A Novel Approach To Local Minima Problem In Robot Path Planning Using Artificial Potential Field
Abstract: The Autonomous mobile robots have vast applications which include industrial robots, service robots, drone-based robots for supplying customer goods, robots for space exploration and much more. Path planning is one of the important steps of using mobile robots in any application. In autonomous mobile robot navigation, local minima describe a situation in which an object gets stuck with no next possible move attainable. This paper introduces a new concept to …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 2, 2018 · pp. 69–81 Read article
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Retina Recognition System Using Wavelet based Neural Network Algorithm
Abstract: This paper presents an approach of wavelet feature-based retina recognition system using back-propagation learning neural network algorithm. After acquiring the retinal image, at first vessels were segmented from the image. Then the feature extraction was carried out by analyzing the segmented retinal image using multiresolution analysis through the wavelet-based approach. Then extracted features were fed to the back-propagation learning neural network algorithm to create the learned template which was used …
Published in Current Trends in Signal Processing · Vol. 5, Issue 2, 2015 · pp. 35–39 Read article
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Multi-layer Perceptron Ensemble for Estimation of Available Transfer Capability in Deregulated Environment
Abstract: The available transfer capability (ATC) estimation problem can be considered as a unique answered puzzle to be solved with highly nonlinear complexity of power systems and the uncertain state of the energy market. To implement the open access in deregulated markets, system operator should post ATC at a small and regular interval on openly accessible network. This forces the quick computation of ATC of transmission path. In these situations, the …
Published in Trends in Electrical Engineering · Vol. 6, Issue 1, 2016 · pp. 57–71 Read article
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Survey on Teaching-Learning-Based Optimization
Abstract: Optimization is a process for finding maximum or minimum of a function subject to set of functions. In other words optimization finds the most suitable value for a function within a given domain. Global Optimization means finding minimum value of all local minima and maxima value from all local maxima. The procedure to find out the global maxima or minima point is called a Global Optimization. Teaching-Learning-Based Optimization(TLBO) is recently …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 3, Issue 1, 2016 · pp. 7–15 Read article
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Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations
Abstract: K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 30–37 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 282–297 Read article
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ATC Calculation in OPF Environment Using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization
Abstract: Evolution of hybrid optimization provides new research direction in the real world problem solving by using more than one optimization techniques for obtaining speedy convergence and/or global optimal solution or solution near the global optimal. In this paper, a novel hybrid particle swarm optimization (PSO) and grey wolf optimizer (GWO) technique is used to calculate the available transfer capability in an optimal power flow environment. The proposed method has been …
Published in Trends in Electrical Engineering · Vol. 6, Issue 1, 2016 · pp. 84–93 Read article
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Designing an Energy Efficient Induction Motor: Advanced Computational Intelligence Technique
Abstract: Abstract—This paper presents an advanced computational intelligence technique (CIT), modified particle swarm optimization (MPSO) for designing energy efficient three-phase induction motor (DEEIM) including active power loss effect. PSO performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. Even if PSO algorithm is easy to implement and has been shown to perform well on various power system optimization problems, they may …
Published in Current Trends in Information Technology · Vol. 9, Issue 2, 2019 · pp. 5–13 Read article