Modified Cuckoo Search Algorithm (MCSA) For Extracting the ODF Maxima

In this section, the modified CSA (MCSA) is presented for extracting the ODF maxima (MCSA-ODF). CSA was proposed by Yang and Deb in 2009. To date, work on this algorithm has significantly increased, and CSA currently has its rightful place among other optimization methodologies (Shehab et al. 2017). MCSA is based on replacing the random selection process with the tournament selection scheme. Thus, the probability of achieving better results is increased, thereby avoiding premature convergence. Performance is validated by applying several benchmarks (Shehab and Khader 2018). The results of the experimental indicate that MCSA performs better than the standard CSA and the other compared methods. Subsequently, MCSA is applied to extract the ODF maxima, namely, MCSA-ODF.

[1]  M. Descoteaux High angular resolution diffusion MRI : from local estimation to segmentation and tractography , 2008 .

[2]  Erik D. Goodman Introduction to genetic algorithms , 2007, GECCO '07.

[3]  Laith Mohammad Abualigah,et al.  Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering , 2017, The Journal of Supercomputing.

[4]  Mohammed Azmi Al-Betar,et al.  A survey on applications and variants of the cuckoo search algorithm , 2017, Appl. Soft Comput..

[5]  R. O. Oladele,et al.  Genetic Algorithm Performance with Different Selection Methods in Solving Multi-Objective Network Design Problem , 2013 .

[6]  Zongli Jiang,et al.  Increasing Diversity and Controlling Bloat in Linear Genetic Programming , 2016, 2016 3rd International Conference on Information Science and Control Engineering (ICISCE).

[7]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[8]  Mohammad Shehab,et al.  Modified Cuckoo Search Algorithm using a New Selection Scheme for Unconstrained Optimization Problems. , 2020, Current medical imaging.

[9]  N. Venkaiah,et al.  A modified cuckoo search algorithm to optimize Wire-EDM process while machining Inconel-690 , 2017 .

[10]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[11]  Lothar Thiele,et al.  A Mathematical Analysis of Tournament Selection , 1995, ICGA.

[12]  Thang Trung Nguyen,et al.  Adaptive Cuckoo Search Algorithm for Short-Term Fixed-Head Hydrothermal Scheduling Problem with Reservoir Volume Constraints , 2016 .

[13]  Kenneth Morgan,et al.  Modified cuckoo search: A new gradient free optimisation algorithm , 2011 .

[14]  Komal Komal,et al.  Analysis of Selection Schemes for Solving an Optimization Problem in Genetic Algorithm , 2014 .

[15]  Laith Mohammad Abualigah,et al.  Big data and E-government: A review , 2017, 2017 8th International Conference on Information Technology (ICIT).

[16]  Mohammad Shehab,et al.  Modified Cuckoo Search Algorithm for Solving Global Optimization Problems , 2017 .

[17]  A. Chowdhury,et al.  Cuckoo search algorithm for economic dispatch , 2013 .

[18]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[19]  Aboul Ella Hassanien,et al.  Modified cuckoo search algorithm with rough sets for feature selection , 2018, Neural Computing and Applications.

[20]  Essam Said Hanandeh,et al.  A novel hybridization strategy for krill herd algorithm applied to clustering techniques , 2017, Appl. Soft Comput..

[21]  Ahamad Tajudin Abdul Khader,et al.  Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization , 2018, The Journal of Supercomputing.

[22]  Mohammad Shehab,et al.  A HYBRID METHOD BASED ON CUCKOO SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS , 2018, Journal of Information and Communication Technology.

[23]  Mohammad Shehab,et al.  Hybridising cuckoo search algorithm for extracting the ODF maxima in spherical harmonic representation , 2019 .

[24]  Mohammad Shehab,et al.  Enhancing Cuckoo Search Algorithm by using Reinforcement Learning for Constrained Engineering optimization Problems , 2019, 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT).

[25]  Mohammed Azmi Al-Betar,et al.  New Selection Schemes for Particle Swarm Optimization , 2016 .