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Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim by Hashim, Siti Zuraifah

“…It makes use of three basic operations in order to optimize this problem. …”
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An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP by Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, Angela Amphawan

“…Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. …”
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Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images by Ali Hussein Aboali, Maged Yahya

“…Disparity depth map estimation of stereo matching algorithm is one of the most active research topics in computer vision.In the field of image processing,many existing stereo matching algorithms to obtain disparity depth map are developed and designed with low accuracy.To improve the accuracy of disparity depth map is quite challenging and difficult especially with uncontrolled dynamic environment.The accuracy is affected by many unwanted aspects including random noises,horizontal streaks,low texture,depth map non-edge preserving, occlusion,and depth discontinuities.Thus,this research proposed a new robust method of hybrid stereo matching algorithm with significant accuracy of computation.The thesis will present in detail the development,design, and analysis of performance on Multistage Hybrid Median Filter (MHMF).There are two main parts involved in our developed method which combined in two main stages.Stage 1 consists of the Sum of Absolute Differences (SAD) from Basic Block Matching (BBM) algorithm and the part of Scanline Optimization (SO) from Dynamic Programming (DP) algorithm.While,Stage 2 is the main core of our MHMF as a post-processing step which included segmentation,merging, and hybrid median filtering.The significant feature of the post-processing step is on its ability to handle efficiently the unwanted aspects obtained from the raw disparity depth map on the step of optimization.In order to remove and overcome the challenges unwanted aspects, the proposed MHMF has three stages of filtering process along with the developed approaches in Stage 2 of MHMF algorithm.There are two categories of evaluation performed on the obtained disparity depth map: subjective evaluation and objective evaluation.The objective evaluation involves the evaluation on Middlebury Stereo Vision system and evaluation using traditional methods such as Mean Square Errors (MSE),Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM).Based on the results of the standard benchmarking datasets from Middlebury,the proposed algorithm is able to reduce errors of non-occluded and all errors respectively.While,the subjective evaluation is done for datasets captured from MV BLUE FOX camera using human's eyes perception.Based on the results,the proposed MHMF is able to obtain accurate results, specifically 69% and 71% of non-occluded and all errors for disparity depth map, and it outperformed some of the existing methods in the literature such as BBM and DP algorithms.…”
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Mean of correlation method for optimization of affective states detection in children by Rusli, Nazreen, Sidek, Shahrul Na'im, Md Yusuf, Hazlina, Ishak, Nor Izzati

“…This paper proposes an effective algorithm of texture analysis based on novel technique using Gray Level Co-occurrence Matrix approach to be applied so as to identify blood-flow region. …”
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Even-odd scheduling based energy efficient routing for wireless sensor network (WSN) / Muhammad Zafar Iqbal Khan by Khan, Muhammad Zafar Iqbal

“…In addition, the network performance has also increased by using the proposed routing algorithm with a throughput of 147.8207 as compared to just 46.0472 with the original LEACH. …”
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Towards large scale unconstrained optimization by Abu Hassan, Malik

“…The main difficulty in dealing with large scale problems is the fact that effective algorithms for small scale problems do not necessarily translate into efficient algorithms when applied to solve large scale problems. …”
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A phonetically rich and balanced lexical corpus using zipfian distribution for an under resourced language / Aminath Farshana by Aminath , Farshana

“…Since the similarity of the two distributions is close, it means that the optimized corpus can perform as efficient as the larger corpus. …”
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Mobile First-Person Shooter (FPS) Game Using Basic Theta* Algorithm / Muhammad Syurahbil Abd Rohaman by Abd Rohaman, Muhammad Syurahbil

“…The downside is the computation time on Basic Theta* using grid is way higher compared to A* using NavMesh. …”
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Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

“…Clustering technique is able to find hidden patterns and to extract useful information from huge data. Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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A near-optimal centroids initialization in K-means algorithm using bees algorithm by Mahmuddin, Massudi, Yusof, Yuhanis

“…The K-mean algorithm is one of the popular clustering techniques.The algorithm requires user to state and initialize centroid values of each group in advance. …”
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Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques by Lipu M.S.H., Hussain A., Saad M.H.M., Hannan M.A.

An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem by S. W. Su, Stephanie, Kek, Sie Long

“…Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.…”
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Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

“…A comparison was done against particle swarm optimization, genetic algorithm, and sine–cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.…”
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Impact of evolutionary algorithm on optimization of nonconventional machining process parameters by B V, Raghavendra, R Annigiri, Anandkumar, Srikatamurthy, JS

“…Using a Python environment, three evolutionary algorithms such as, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Firefly Algorithm (FA), were simulated to evaluate their effectiveness in minimizing surface roughness (Ra). …”
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Design Of Robot Motion Planning Algorithm For Wall Following Robot by Ali Hassan, Muhamad Khairul

“…Computer A will be sent the data to computer B through the internet/LAN using JAVA program. …”
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Form Finding And Shape Change Analysis Of Spine Inspired Bio-Tensegrity Model by Oh, Chai Lian

“…The shape change of SBS models towards target state is achieved by means of forced elongation of cable. Computational strategies for the shape change consist of two stages: the derivation of incremental equilibrium equations and optimization of the cables forced elongation by sequential quadratic programming. …”
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Optimized speed controller for induction motor drive using quantum lightning search algorithm by Ali J.A., Hannan M.A., Mohamed A.

Cluster optimization in VANET using MFO algorithm and K-Means clustering by Ramlee, Sham Rizal, Hasan, Sazlinah, K. Subramaniam, Shamala

“…Proven to be an effective and efficient method for solving optimization problem. To design K-Means algorithm that portion nodes based on their proximities by optimize the distance between nodes within same cluster by assigning them to the closet cluster center. …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering by Rashed, Alwatben Batoul

“…For optimum results in performance analysis, the optimal value of the MOPSO-CD was evaluated using (ZDT), (WFG), and (DTLZ) with two or three objectives over D2MOPSO, AgMOPSO, MMOPSO, and EMOSO algorithms. …”
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Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction by Tan, James Yiaw Beng

“…The proposed KGA model combines greedy algorithm withk-means++ clustering in this research to assist users in automating the finding of the optimal number of new-ons inside the hidden layer of the BP network. …”
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