000			02871nam a2200385Ka 4500
001			00011788
003			WSP
007			cr cnu|||unuuu
008			200708s2020 si ob 001 0 eng d
040			_aWSPC _beng _cWSPC
010			_z 2020027891
020			_a9789811218996 _q(ebook)
020			_a9811218994 _q(ebook)
020			_z9789811218989 _q(hbk.)
020			_z9811218986 _q(hbk.)
050	0	4	_aTA347.E96 _bL53 2020
072		7	_aCOM _x051300 _2bisacsh
082	0	4	_a006.3823 _223
100	1		_aLi, Juan _c(Mathematician) _9178394
245	1	0	_aDecomposition-based evolutionary optimization in complex environments _h[electronic resource] / _cby Juan Li, Bin Xin, Jie Chen.
260			_aSingapore ; _aHoboken : _bWorld Scientific, _c[2020]
300			_a1 online resource (xvii, 229 p.)
504			_aIncludes bibliographical references and index.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
520			_a"Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of 'making things simple' and 'divide and conquer' to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization"--Publisher's website.
650		0	_aEvolutionary computation. _94099
650		0	_aMultiple criteria decision making. _98834
655		0	_aElectronic books. _93294
700	1		_aXin, Bin. _9178395
700	1		_aChen, J. _q(Jie). _9178396
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/11788#t=toc _zAccess to full text is restricted to subscribers.
880	0		_6505-00/aIntroduction -- Decomposition-based multi-objective evolutionary algorithm with the (Sf(B-constraint framework -- Decomposition-based many-objective evolutionary algorithm with the (Sf(B-constraint framework -- An A posteriori decision-making framework and subproblems co-solving evolutionary algorithm for uncertain optimization -- Noise-tolerant techniques for decomposition-based multi-objective evolutionary algorithms -- The bi-objective critical node detection problem with minimum pairwise connectivity and cost : theory and algorithms -- Solving bi-objective uncertain stochastic resource allocation problems by the cvar-based risk measure and decomposition-based multi-objective evolutionary algorithm.
942			_cEBK
999			_c97768 _d97768