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- Synthetic indicators (3)
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- Pena’s distance (2)
- Social Indicator research (2)
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- Benchmark functions (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Global Optimization Of Some Difficult Benchmark Functions By Cuckoo-Host Co-Evolution Meta-Heuristics, Sudhanshu K. Mishra
Global Optimization Of Some Difficult Benchmark Functions By Cuckoo-Host Co-Evolution Meta-Heuristics, Sudhanshu K. Mishra
Sudhanshu K Mishra
This paper proposes a novel method of global optimization based on cuckoo-host co-evaluation. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 96 benchmark functions (of which the results of 32 relatively harder problems have been reported). The proposed method is comparable to the Differential Evolution method of global optimization.
A Comparative Study Of Trends In Globalization Using Different Synthetic Indicators, Sudhanshu K. Mishra
A Comparative Study Of Trends In Globalization Using Different Synthetic Indicators, Sudhanshu K. Mishra
Sudhanshu K Mishra
Using the KOF data at the annual level, we construct ten different composite indices for comparing the extent of globalization of 131 countries for eleven years, 1999-2009. We compare the different indices of globalization among themselves and also with the Dreher-KOF index of globalization and find that among the different indices the Dreher-Chebyshev index is the most representative one. Among the countries, we concentrate on the trends in globalization of India and her neighboring countries, Bangladesh, China, and Pakistan.
A Maximum Entropy Perspective Of Pena’S Synthetic Indicators, Sudhanshu K. Mishra
A Maximum Entropy Perspective Of Pena’S Synthetic Indicators, Sudhanshu K. Mishra
Sudhanshu K Mishra
This paper uses mixed combinatorial-cum-real particle swarm method to obtain a heuristically optimal order in which the constituent variables can be arranged so as to yield some generalized maximum entropy synthetic indicators that represent the constituent variables in the best information-theoretic sense. It may help resolve the arbitrariness and indeterminacy of Pena’s method of construction of a synthetic indicator which at present is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged.
A Note On Construction Of Heuristically Optimal Pena’S Synthetic Indicators By The Particle Swarm Method Of Global Optimization, Sudhanshu K. Mishra
A Note On Construction Of Heuristically Optimal Pena’S Synthetic Indicators By The Particle Swarm Method Of Global Optimization, Sudhanshu K. Mishra
Sudhanshu K Mishra
Pena’s method of construction of a synthetic indicator is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged. Due to this, Pena’s method can at present give only an arbitrary synthetic indicator whose representativeness is indeterminate and uncertain, especially when the number of constituent variables is not very small. This paper uses discrete global optimization method based on the Particle Swarms to obtain a heuristically optimal order in which the constituent variables can be arranged so as to yield Pena’s synthetic indicator that maximizes the minimal absolute (or squared) correlation …
A Note On The Indeterminacy And Arbitrariness Of Pena’S Method Of Construction Of Synthetic Indicators, Sudhanshu K. Mishra
A Note On The Indeterminacy And Arbitrariness Of Pena’S Method Of Construction Of Synthetic Indicators, Sudhanshu K. Mishra
Sudhanshu K Mishra
In this paper we demonstrate that Pena’s method of construction of a synthetic indicator is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged. Since m number of constituent variables may be arranged in m-factorial ways, even a moderately large m can give rise to a very large number of synthetic indicators from which one cannot choose the one which best represents the constituent variables. Given that an analyst has too little information as to the order in which a sizeable number of constituent variables must be arranged so as …