@article{scholars5901,
             doi = {10.1016/j.asoc.2015.03.016},
            note = {cited By 18},
          volume = {32},
           title = {Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution},
            year = {2015},
           pages = {293--299},
         journal = {Applied Soft Computing Journal},
       publisher = {Elsevier Ltd},
          author = {Ganesan, T. and Elamvazuthi, I. and Vasant, P.},
            issn = {15684946},
        abstract = {Engineering problems presenting themselves in a multiobjective setting have become commonplace in most industries. In such situations the decision maker (DM) requires several solution options prior to selecting the best or the most attractive solution with respect to the current industrial circumstances. The weighted sum scalarization approach was employed in this work in conjunction with three metaheuristic algorithms: particle swarm optimization (PSO), differential evolution (DE) and the improved DE algorithm (GTDE) (which was enhanced using ideas from evolutionary game theory). These methods are then used to generate the approximate Pareto frontier to the nano-CMOS voltage-controlled oscillator (VCO) design problem. Some comparative studies were then carried out to compare the proposed method as compared to the standard DE approach. Examination on the quality of the solutions across the Pareto frontier obtained using these algorithms was carried out using the hypervolume indicator (HVI). {\^A}{\copyright} 2015 Elsevier B.V. All rights reserved.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927917967&doi=10.1016\%2fj.asoc.2015.03.016&partnerID=40&md5=4eaebc0df9689e0d68aa4ea729e1e38b},
        keywords = {Algorithms; Circuit oscillations; Decision making; Evolutionary algorithms; Game theory; Integrated circuit design; Optimization; Oscillistors; Particle swarm optimization (PSO); Variable frequency oscillators, Differential Evolution; Evolutionary game theory; Hypervolume indicators; Meta heuristic algorithm; Multi objective; Multi-objective design optimization; Nano CMOS; Scalarization approach, Multiobjective optimization}
}