To develop a fast global optimizer, the whole iterative procedure of a tabu search algorithm is divided into two deliberately designed phases: exploration- and exploitation-phases. Stochastic approximation method is proposed to minimize the computational burdens when computing the gradient information in designing the exploitation phase. Also, some specially oriented mechanisms for enhancing the balance between exploration and exploitation searches are introduced and integrated. Numerical results are reported to showcase the merits of the proposed metaheuristic.
LowerD.A., Application of intelligent system technology to design optimization, Tutorial 1 of CEFC'2000, Milwaukee, Wisconsin, USA, 2000.
2.
YangS., HoS.L., YaoY., LiuL. and WuL., Studies on numerical methodologies for inverse problems and optimizations in China, The International Journal for Computation and Mathematics in Electrical and Electronic Engineering33 (2014), 56-64.
3.
Dos SantosC.L. and AlottoP., Multiobjective Electromagnetic Optimization based on a Nondominated Sorting Genetic Approach with a Chaotic Crossover Operator, IEEE Trans. Magn.44 (2008), 1078-1081.
4.
GuimaraesF.G., CampeloF., IgarashiH., LowtherD.A. and RamirezJ.A., Optimization of Cost Functions using Evolutionary Algorithms with Local Learning and Local Search, IEEE Trans. Magn.43 (2007), 1641-1644.
5.
GaoH., KwongS., FanB. and WangR., A Hybrid Particle-Swarm Tabu Search Algorithm for Solving Job Shop Scheduling Problems, IEEE Trans. Industrial Informatics10 (2014), 2044-2054.
6.
AnS., YangS., HoS.L., LiT. and FuW., A Modified Tabu Search Method Applied to Inverse Problems, IEEE Trans. Magn.47 (2011), 1234-1237.
7.
LiL., JafarpourB. and Mohammad-KhaninezhadM.R., A Simultaneous Perturbation Stochastic Approximation Algorithm for Coupled Well Placement and Control Optimization under Geologic Uncertainty, Comput Geosci17 (2013), 167-188.
8.
HoS.L., YangS., BaiY. and HuangJ., A direct coupled solution methodology for efficient robust optimizations of inverse problems under uncertainty, IEEE Transactions on Magnetics51 (2015), 7001004.
9.
YangS., YangJ., BaiY. and NiG., A new methodology for robust optimizations of optimal design problems under interval uncertainty, IEEE Transactions on Magnetics52 (2016), DOI:10.1109/TMAG.2015.248352, (in press).
10.
TEAM optimization benchmark problem 22 [online], available at: http://www.compumag.org/jsite/team.html.
11.
HollandJ.H., Adaption in natural and artificial system, MIT Press, 1975.
12.
HuN., Tabu search method with random moves for globally optimal design, Int. J. Num. Meth. Eng.35 (1992), 1055-1070.
13.
HoS.L., YangS.Y., NiG.Z. and WongH.C., An improved tabu search for the global optimizations of electromagnetic devices, IEEE Trans. Magn.37 (2001), 3570-3574.
14.
KirkpatrickS., GelattC.D. and VecchiM.P., Optimization by Simulated Annealing, Science220 (1983), 671-680.
15.
HajjiO., BrissetS. and BrochetP., A new tabu search method for optimization with continuous parameters, IEEE Trans. Magn.40 (2004), 1184-1187.
16.
YangS.Y., BaiY.N., ZhangG.H. and WuL., An improved population-based incremental learning method for inverse problems, Automation Congress (2008), 1-4.
