ǂA ǂcomprehensive review of bat algorithms and their hybridization

M.Sc. Thesis

Iztok Fister (Author), Marjan Mernik (Mentor), Xin-She Yang (Co-mentor)

Abstract

Swarm intelligence is a modern and efficient mechanism for solving hard problems in computer science, engineering, mathematics, economics, medicine and optimization. Swarm intelligence is the collective behavior of decentralized and self-organized systems. This research area is a branch of artificial intelligence and could be viewed as some kind of family relationship with evolutionary computation because both communities share a lot of common characteristics. To date, a lot of swarm intelligence algorithms have been developed and applied to several real-world problems. The main focus of this thesis is devoted to the bat algorithm which is a member of the swarm intelligence community, as developed recently. In line with this, a comprehensive analysis of papers was performed tackling this algorithm. Some hybridizations of the original algorithm were proposed because the preliminary results of this algorithm regarding the optimization of benchmark functions with higher dimensions had not too promising. Extensive experiments showed that the hybridizing the original bat algorithm has beneficial effects on the results of the original bat algorithm. Finally, an experimental study was performed during which we researched for the dependence of an applied randomized method on the results of the original bat algorithm. The results of this study showed that selecting the randomized method had a crucial impact on the results of the original bat algorithm and that the bat algorithm using Levy flights is also suitable for solving the harder optimization problems.

Keywords

inteligenca roja;evolucijsko računanje;bat algoritmi;hibridizacija;pregled;

Data

Language:	Slovenian
Year of publishing:	2013
Typology:	2.09 - Master's Thesis
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	[I. Fister Jr.]
UDC:	004.5:004.89(043)
COBISS:	17357078
Views:	2791
Downloads:	280
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	OBŠIRNI PREGLED ALGORITMOV BAT IN NJIHOVA HIBRIDIZACIJA
Secondary abstract:	Inteligenca roja je moderni in zelo učinkovit mehanizem za reševanje težkih problemov v računalništvu, inženiringu, matematiki, ekonomiji, medicini in optimizaciji. Inteligenca roja je kolektivno obnašanje decentraliziranih in samoorganiziranih sistemov. To raziskovalno področje je veja umetne inteligence in je, zaradi podobnih karakteristik, zelo podobna evolucijskemu računanju. Po domače bi lahko povedali tudi, da je inteligenca roja sestra evolucijskemu računanju. V preteklosti je bilo razvitih veliko algoritmov inteligence rojev, kateri so bili uporabljeni na problemih iz realnega sveta. Glavni del te magistrske naloge je posvečen algoritmu netopirjev, ki je bil razvit nedavno, in je pokazal dobre rezultate pri zvezni optimizaciji funkcij manjših dimenzij. Magistrska naloga sestoji iz šestih večjih poglavij. V prvem poglavju je na kratko predstavljena teorija evolucijskih algoritmov. Dodan je tudi krajši uvod v inteligenco rojev. V naslednjem poglavju se osredotočamo na biološke značilnosti inteligence rojev. Tretje poglavje obsega natančne opise in prikaze najbolj popularnih algoritmov inteligence rojev. V četrtem je narejena obširna študija celotnega področja algoritmov netopirjev. V petem poglavju je predstavljen praktični del, kjer prikažemo hibridizacijo algoritma netopirjev z diferencialno evolucijo in naključnimi gozdovi. Tretji del petega poglavja zavzema primerjalna študija uporabe različnih naključnih distribucij v algoritmu netopirjev. Magistrsko nalogo zaključimo s povzetkom opravljenega dela in napovemo smeri njenega razvoja v prihodnosti.
Secondary keywords:	swarm intelligence;evolutionary computation;bat algorithm;hybridization;review;
URN:	URN:SI:UM:
Type (COBISS):	Master's thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko
Pages:	VII, 100 f.
ID:	8727422