Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms by Harmanani, H. M.

“…The method maximizes concurrent testing of modules while performing the allocation of functional units, test registers, and interconnects. The method is based on a genetic algorithm that efficiently explores the testable design space and finds a sub-optimal test registers assignment for each k-test session. …”
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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …”
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Heuristics. (c2006) by Kallab, Chadi

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Intelligent Fusion of Deep Features for Improved Waste Classification by Kashif Ahmad (12592762)

Nonlinear Control of Brushless Dual-Fed Induction Generator With a Flywheel Energy Storage System for Improved System Performance by Mohammed Hamidat (3722086)

“…In the first stage, a robust Sliding Mode Control (SMC)-based nonlinear decoupled control algorithm is designed to efficiently regulate BDFIG operation. …”

A new multiobjective evolutionary algorithm forenvironmental/economic power dispatch by Abido, A.A.

“…A new nondominated sorting genetic algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. …”
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Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach by Haitham Osman (11737057)

“…<p>This paper presents the design, modeling, and multi-objective optimization of an advanced solar energy system based on concentrated solar power technology, aimed at sustainable electricity generation in urban environments. …”

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems by Bilal Khurshid (16715865)

“…The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. …”

A modified ant colony algorithm for evolutionary design of digital circuits by Sait, Sadiq M.

“…In this paper, a multiobjective optimization of logic circuits based on a modified ant colony (ACO) algorithm is presented. …”
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Evolutionary algorithms for state justification in sequential automatic test pattern generation by El-Maleh, Aiman H.

“…Evolutionary algorithms have been effective in solving many search and optimization problems. …”
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Chapter 19 - Metaheuristics for optimizing weights in neural networks by Abu Doush, Iyad

“…To avoid these problems, the process of the gradient-based mechanism is replaced by a recent metaheuristic swarm-based method called horse herd optimization algorithm (HOA). …”
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Hybrid Cooperative Co-evolution for Large Scale Optimization by El-Abd, Mohammed

“…In CC, multiple instances of the same evolutionary algorithm work in parallel, each optimizes a different subset of the problem in hand. …”
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Metaheuristic algorithm for testing web 2.0 applications. (c2012) by Zeitunlian, Hratch Michel

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A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models by Azar, Danielle

“…In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. …”
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An Optimization Approach For The Computation Of The Minimum Destabilizing Uncertainty Volume by AL-Sunni, Fouad

“…In this paper we propose an non-linear optimization based algorithm for the computation of the stability region for uncertain polynomials. …”
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Particle swarm optimization for multimachine power systemstabilizer design by Abido, A.A.

“…In this paper, a novel evolutionary algorithm based approach to optimal design of multimachine power system stabilizers (PSSs) is proposed. …”
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A stochastic iterative learning control algorithm with application to an induction motor by Saab, Samer S.

“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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Stochastic P-type/D-type iterative learning control algorithms by Saab, Samer S.

“…The optimal algorithm is based on minimizing the trace of the input error covariance matrix. …”
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On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise by Saab, Samer S.

“…Furthermore, a suboptimal second-order ILC is proposed for a class of nonlinear systems. Based on a numerical example, it is shown that a compatible suboptimal first-order ILC yields better performance than the proposed suboptimal second-order ILC algorithm.…”
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