Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije
Improvement by hybridization of swarm intelligence metaheuristics for solving global optimization problems.
Author
Džakula Bačanin, Nebojša V.Mentor
Tuba, Milan
Committee members
Živković, Miodrag
Dugošija, Đorđe
Yang, Xin-She
Metadata
Show full item recordAbstract
Te²ki optimizacioni problemi, nere²ivi u prihvatljivom vremenu izvr²avanja deterministi
£kim matemati£kim metodama, uspe²no se poslednjih godina re²avaju populacionim
stohasti£kim metaheuristikama, me u kojima istaknutu klasu predstavljaju
algoritmi inteligencije rojeva. U ovom radu razmatra se unapre enje metaheuristika
inteligencije rojeva pomo¢u hibridizacije. Analizom postoje¢ih metaheuristika
u odre enim slu£ajevima uo£eni su nedostaci i slabosti u mehanizmima pretrage
prostora re²enja koji pre svega proisti£u iz samog matemati£kog modela kojim se simulira
proces iz prirode kao i iz nedovoljno uskla enog balansa izme u intenzikacije
i diversikacije.
U radu je ispitivano da li se postoje¢i algoritmi inteligencije rojeva za globalnu optimizaciju
mogu unaprediti (u smislu dobijanja boljih rezultata, brºe konvergencije,
ve¢e robustnosti) hibridizacijom sa drugim algoritmima. Razvijeno je i implementirano
vi²e hibridizovanih metaheuristika inteligencije rojeva. S obzirom da dobri
hibri...di ne nastaju slu£ajnom kombinacijom pojedinih funkcionalnih elemenata i
procedura razli£itih algoritama, ve¢ su oni utemeljeni na sveobuhvatnom izu£avanju
na£ina na koji algoritmi koji se hibridizuju funkcioni²u, kreiranju hibridnih pristupa
prethodila je detaljna analiza prednosti i nedostataka posmatranih algoritma kako
bi se napravila najbolja kombinacija koja nedostatke jednih neutrali²e prednostima
drugih pristupa.
Razvijeni hibridni algoritmi verikovani su testiranjima na standardnim skupovima
test funkcija za globalnu optimizaciju sa ograni£enjima i bez ograni£enja, kao i na
poznatim prakti£nim problemima. Upore ivanjem sa najboljim poznatim algoritmima
iz literature pokazan je kvalitet razvijenih hibrida, £ime je potvr ena i osnovna
hipoteza ovog rada da se algoritmi inteligencije rojeva mogu uspe²no unaprediti hibridizacijom...
Hard optimization problems that cannot be solved within acceptable computational
time by deterministic mathematical methods have been successfully solved in recent
years by population-based stochastic metaheuristics, among which swarm intelligence
algorithms represent a prominent class. This thesis investigates improvements
of the swarm intelligence metaheuristics by hybridization. During analysis of the
existing swarm intelligence metaheuristics in some cases deciencies and weaknesses
in the solution space search mechanisms were observed, primarily as a consequence
of the mathematical model that simulates natural process as well as inappropriate
balance between intensication and diversication.
The thesis examines whether existing swarm intelligence algorithms for global optimization
could be improved (in the sense of obtaining better results, faster convergence,
better robustness) by hybridization with other algorithms. A number
of hybridized swarm intelligence metaheuristics were dev...eloped and implemented.
Considering the fact that good hybrids are not created as a random combination of
individual functional elements and procedures from dierent algorithms, but rather
established on comprehensive analysis of the functional principles of the algorithms
that are used in the process of hybridization, development of the hybrid approaches
was preceded by thorough research of advantages and disadvantages of each involved
algorithm in order to determine the best combination that neutralizes disadvantages
of one approach by incorporating the strengths of the other.
Developed hybrid approaches were veried by testing on standard benchmark sets for
global optimization, with and without constraints, as well as on well-known practical
problems. Comparative analysis with the state-of-the-art algorithms from the literature
demonstrated quality of the developed hybrids and conrmed the hypothesis
that swarm intelligence algorithms can be successfully improved by hybridization...