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008 190221t20182018my a f am 000 0 eng d
020 _aTHE0005416(Local)
039 9 _a201905151459
_bhanafiah
040 _aUMP
_beng
_cUMP
_erda
090 _aFKP .S83 2018 r Thesis
100 1 _aMohammed, Suad Khairi,
_eauthor.
245 1 3 _aAn experimental study of modified black hole algorithms /
_cSuad Khairi Mohammed
264 1 _aKuantan, Pahang :
_bUMP,
_c2018
264 4 _c© 2018
300 _axiv, 150 pages :
_billustrations ;
_c30 cm. +
_e1 CD-ROM
336 _atext
_2rdacontent
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
337 _acomputer
_2rdamedia
338 _avolume
_2rdacarrier
338 _acomputer disc
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aFaculty of Manufacturing Engineering
502 _aThesis (Doctor of Philosophy (Electrical Engineering)) -- Universiti Malaysia Pahang – 2018
504 _aIncludes bibliographical references
520 3 _aOptimization is often required in many fields of research. Some systems are harder to optimize compared to others. For this reason, many meta-heuristic optimization methodshave been devised and improved. One of the meta-heuristic optimization methods is Black Hole (BH) algorithm, which is inspired by the black hole in cosmology. The black hole has been found theoretically in the studies of the universe as a star having massive mass and gravity. The BH algorithm is a population-based method which uses more than one agent to find a solution in a search space. In BH algorithm, an agent with the best solution forms a black hole. The biggest mass is given to the agent with the best solution and other agents update their position towards the agent with the best solution. In this thesis, it is found that the BH algorithm suffers from premature convergence. Hence, three methods to prevent the premature convergence in BH algorithm are presented. The first method is the introduction of white hole operator. The white hole operator is proposed to avoid the agents from exploring the area near the worst agent. The second method is the application of a local search. Finally, a gravitational interaction, which is the essence of the gravitational search algorithm, is also applied. Ultimately, seven variants of BH algorithms are established based on the white hole, local search, and gravitational interaction. Those algorithms are Black Hole White Hole Algorithm, Gravitational Black Hole Algorithm, Gravitational Black Hole White Hole Algorithm, Black Hole Local Search Algorithm, Black Hole White Hole Local Search Algorithm, Gravitational Black Hole Local Search Algorithm, and Gravitational Black Hole White Hole Local Search Algorithm. The algorithms are evaluated based on unimodal, multimodal, hybrid, and composite functions in CEC2014 benchmark test functions. Benchmarking with particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimizer (GWO) is done as well. Results show that all the variants are as good as PSO and better than the original BH algorithm. Statistical analysis suggests that one variant called Black Hole White Hole algorithm is the best since the Black Hole White Hole algorithm is significantly better than GA, GWO, and BH algorithms.
610 2 0 _aFaculty of Manufacturing Engineering
_xDissertations
650 0 _aUniversities and colleges
_xDisertations
650 0 _aTheses
942 _2lcc
_cTHESIS