Search Results - (( evolution optimization problems algorithm ) OR ( its implementation using algorithm ))
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Efficient radio resource management algorithms for downlink long term evolution networks
Published 2018“…The algorithm is based on the idea of the optimization problem in which resource allocation problem is formulated as an optimization problem. …”
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Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity
Published 2018“…The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. …”
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Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). …”
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Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game
Published 2015“…After that, a bi-objectives algorithm is tested for comparing purposes and this contributed for the next two sub-objectives that is 3) to test the feasibility for implementing the PDE hybrid FFNN. 4) to compare single objective and multi-objective optimization algorithms performances. …”
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Task scheduling in cloud computing using hybrid genetic algorithm and bald eagle search (GA-BES)
Published 2022“…The natural evolution optimization algorithm which is genetic algorithm can be improve by combining the nature meta-heuristic algorithms which is bald eagle search to improve the makespan of genetic algorithm using cloudsim that need to be implement on the eclipse platform. …”
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Academic Exercise -
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Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin
Published 2013“…Job Shop Scheduling Problem (JSSP) is one of the well-known hard combinatorial scheduling problems and one of the most computationally difficult combinatorial optimization problems considered to date. …”
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A hybrid optimization technique for solving economic emission load dispatch problems
Published 2023text::Final Year Project -
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A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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Conference or Workshop Item -
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Multi-objective optimization problems are also addressed by proposing a modified multi-criterion optimization algorithm based on a Pareto-based Particle Swarm Optimization (PSO) algorithm called Multi-Objective Particle Swarm Optimization (MOPSO). …”
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Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
Published 2019“…This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. …”
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Performance comparison of differential evolution and particle swarm optimization in constrained optimization
Published 2012“…There are quite numbers of modern optimization algorithms proposed in the last two decades to solve optimization problems. …”
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Empirical Evaluation of Mutation Step Size in Automated Evolution of Non-Target-Based 3D Printable Objects
Published 2015“…Evolutionary algorithms (EA) currently play a central role in solving complex, highly non-linear problems such as in engineering design, computational optimization, bioinformatics and many more diverse fields. …”
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Reliably optimal PMU placement using disparity evolution-based genetic algorithm
Published 2017“…Optimal PMU Placement (OPP) problem as the combinatorial optimization problem has been formulated to determine the minimum PMU location in the power system. …”
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Two level Differential Evolution algorithms for ARMA parameters estimatio
Published 2013“…An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. …”
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Proceeding Paper -
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Improved chemotaxis differential evolution optimization algorithm
Published 2015“…The social foraging behavior of Escherichia coli has recently received great attention and it has been employed to solve complex search optimization problems.This paper presents a modified bacterial foraging optimization BFO algorithm, ICDEOA (Improved Chemotaxis Differential Evolution Optimization Algorithm), to cope with premature convergence of reproduction operator.In ICDEOA, reproduction operator of BFOA is replaced with probabilistic reposition operator to enhance the intensification and the diversification of the search space.ICDEOA was compared with state-of-the-art DE and non-DE variants on 7 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014). …”
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Conference or Workshop Item -
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Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. …”
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