"A simplex differential evolution algorithm: Development and applications" . "GB - Spojen\u00E9 kr\u00E1lovstv\u00ED Velk\u00E9 Brit\u00E1nie a Severn\u00EDho Irska" . "Pant, M." . . "0142-3312" . "A simplex differential evolution algorithm: Development and applications"@en . . "000306556100004" . . "Abraham Padath, Ajith" . . "14"^^ . "1"^^ . . "27240" . "A simplex differential evolution algorithm: Development and applications"@en . "[3100A3D1E367]" . . "Transactions of the Institute of Measurement and Control" . "RIV/61989100:27240/12:86092945!RIV15-MSM-27240___" . . "Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The conventional method for generating the initial population is the use of computer-generated pseudo-random numbers, which may not be very effective. In the present study, we have investigated the potential of generating the initial population by integrating the non-linear simplex method of Nelder and Mead with pseudo-random numbers in a DE algorithm. The resulting algorithm named the non-linear simplex DE is tested on a set of 20 benchmark problems with box constraints and two real life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. The Author(s) 2011."@en . "10.1177/0142331211403032" . . "3"^^ . "Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The conventional method for generating the initial population is the use of computer-generated pseudo-random numbers, which may not be very effective. In the present study, we have investigated the potential of generating the initial population by integrating the non-linear simplex method of Nelder and Mead with pseudo-random numbers in a DE algorithm. The resulting algorithm named the non-linear simplex DE is tested on a set of 20 benchmark problems with box constraints and two real life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. The Author(s) 2011." . . . . . "stochastic optimization; random numbers; initial population; differential evolution; Crossover"@en . "Abraham Padath, Ajith" . . "RIV/61989100:27240/12:86092945" . "Ali, M." . . "S" . "120614" . "A simplex differential evolution algorithm: Development and applications" . "34" . "6" . .