Search Results - (( sequence optimization search algorithm ) OR ( evolution optimization modified algorithm ))

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  1. 1
    Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin

    Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin by Ong, Chung Sin

    Published 2013
    “…Reduction of the search space does not guarantee the optimal solution will be found. …”
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  2. 2
    A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising

    A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising by Al-Dabbagh, Mohanad Dawood, Al-Dabbagh, Rawaa Dawoud, Raja Abdullah, Raja Syamsul Azmir, Hashim, Fazirulhisyam

    Published 2015
    “…A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. …”
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  3. 3
    Gravitational Search Algorithm for Assembly Sequence Planning

    Gravitational Search Algorithm for Assembly Sequence Planning by Ismail, Ibrahim, Zuwairie, Ibrahim, Hamzah, Ahmad, Mohd Falfazli, Mat Jusof, Zulkifli, Md. Yusof, Sophan Wahyudi, Nawawi, Marizan, Mubin

    Published 2014
    “…In this paper, an approach using Gravitational Search Algorithm (GSA) which is a heuristic optimization algorithm that incorporates the Newton’s law of gravity and the law of motion into analytical studies of systems is proposed to solve the assembly sequence planning problem. …”
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  4. 4
    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain

    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

    Published 2018
    “…This modified algorithm called Modified Multi-Objective Particle Swarm Optimization (M-MOPSO) employs a fixed-sized external archive along with a dynamic boundary-based search mechanism to evolve the population. …”
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  5. 5
    A refined differential evolution algorithm for improving the performance of optimization process

    A refined differential evolution algorithm for improving the performance of optimization process by A. R., Yusoff, Nafrizuan, Mat Yahya

    Published 2011
    “…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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  6. 6
    Product assembly sequence optimization based on genetic algorithm

    Product assembly sequence optimization based on genetic algorithm by Yasin, Azman, Puteh, Nurnasran, Daud, Ruslizam, Omar, Mazni, Syed-Abdullah, Sharifah Lailee

    Published 2010
    “…Genetic algorithm (GA) is a search technique used in computing to find approximate solution to optimization and search problem based on the theory of natural selection. …”
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  7. 7
    An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm

    An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm by Ibrahim, I., Ibrahim, Z., Ahmad, Hamzah, Jusof, M.F.M., Yusof, Z.M., Nawawi, S.W., Mubin, M.

    Published 2015
    “…In this paper, an approach based on a new variant of the gravitational search algorithm (GSA) called the rule-based multi-state gravitational search algorithm (RBMSGSA) is used to solve the assembly sequence planning problem. …”
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  8. 8
    An Assembly Sequence Planning Approach with a Rule-based Multi-state Gravitational Search Algorithm

    An Assembly Sequence Planning Approach with a Rule-based Multi-state Gravitational Search Algorithm by Ismail, Ibrahim, Zuwairie, Ibrahim, Hamzah, Ahmad, Mohd Falfazli, Mat Jusof, Zulkifli, Md. Yusof, Sophan Wahyudi, Nawawi, Marizan, Mubin

    Published 2015
    “…In this paper, an approach based on a new variant of the gravitational search algorithm (GSA) called the rule-based multi-state gravitational search algorithm (RBMSGSA) is used to solve the assembly sequence planning problem. …”
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  9. 9
    A guided dynamic programming approach for DNA sequence similarity search

    A guided dynamic programming approach for DNA sequence similarity search by Mohd Nordin Abdul Rahman

    Published 2011
    “…DNA sequence similarity search is an important task in computational biology applications. …”
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  10. 10
    A filtering algorithm for efficient retrieving of DNA sequence

    A filtering algorithm for efficient retrieving of DNA sequence by Abdul Rahman, Mohd Nordin, Mohd. Saman, Md. Yazid, Ahmad, Aziz, Md. Tap, Abu Osman

    Published 2009
    “…The algorithm filtered the expected irrelevant DNA sequences in database from being computed for dynamic programming based optimal alignment process. …”
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  11. 11
    Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm

    Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm by Mohammed Abdullah, Abdullah Nasser

    Published 2018
    “…In line with the emerging field called Search based Software Engineering, many recently developed t-way strategies have adopted meta-heuristic algorithms as the basis of their implementations such as Simulated Annealing, Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization, Harmony Search and Cuckoo Search, owing their superior performance in term of test size reduction as compared to general computational based strategies, such as General t-way, Test Vector Generator, In Parameter Order General, Jenny, and Automatic Efficient Test Generator. …”
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  12. 12
    Improved chemotaxis differential evolution optimization algorithm

    Improved chemotaxis differential evolution optimization algorithm by Yıldız, Y. Emre, Altun, Oğuz, Topal, A. Osman

    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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  13. 13
    A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem

    A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem by M. F. F., Ab Rashid

    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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  14. 14
    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence

    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…Full search (FS), three step search (TSS), new three step search (NTSS), diamond search (DS) and hexagon based search (HS) are the most well known block-matching algorithm. …”
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  15. 15
    Design of DNA sequence alignment accelerated system using 2-dimensional array and custom instruction on FPGA / Nur Dalilah Ahmad Sabri

    Design of DNA sequence alignment accelerated system using 2-dimensional array and custom instruction on FPGA / Nur Dalilah Ahmad Sabri by Ahmad Sabri, Nur Dalilah

    Published 2018
    “…Nowadays, the (deoxyribonucleic acid) DNA sequence database has been increased linearly with the time taken to comparing with the search of DNA sequence alignment system. …”
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  16. 16
    Broadening selection competitive constraint handling algorithm for faster convergence

    Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

    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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  17. 17
    Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences

    Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences by Rajesvary Rajoo R., Salam R.A.

    Published 2024
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  18. 18
    A discrete simulated kalman filter optimizer for combinatorial optimization problems

    A discrete simulated kalman filter optimizer for combinatorial optimization problems by Suhazri Amrin, Rahmad

    Published 2022
    “…Another type of algorithm is called numerical algorithms. These algorithms were built specifically to address numerical optimization problems. …”
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  19. 19
    Sequence t-way test generation using the barnacles mating optimizer algorithm

    Sequence t-way test generation using the barnacles mating optimizer algorithm by Kamal Z., Zamli, Kader, Md. Abdul

    Published 2021
    “…More precisely, we focus on the generation of test cases due to the ordering of inputs (or sequence) using the newly developed Barnacles Mating Optimizer (BMO) Algorithm. …”
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  20. 20
    Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

    Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems by Waqas Haider Bangyal, Kashif Nisar, Ag. Asri Bin Ag. Ibrahim, Muhammad Reazul Haque, Joel J. P. C. Rodrigues, Danda B. Rawat

    Published 2021
    “…This study presents three new low-discrepancy sequences named WELL sequence, Knuth sequence, and Torus sequence to initialize the population in the search space. …”
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