Self Adaptive Hybridization of Quadratic Approximation with Real Coded Genetic Algorithm

Das, Kedar Nath; Singh, Tapan Kumar

doi:10.1007/978-81-322-1041-2_43

Cited by 1 publication

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“…Till date, for solving complex non-linear optimization problems, many a number of evolutionary methods have been designed. Few popular algorithms are Diversity Guided Evolutionary Programming (DGEP) [1], Self-Adaptive DE (jDE) [2], Modified ABC (ABC/best) [3], Structural Optimization (SO) [4], Self Adaptive Hybridized GA (S-LXPM) [5], Swallow Swarm Optimization (SSO) [6], Evolutionary Programming, etc. However, most of them suffer either with computational burden or with fine tuning of many parameters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Search Technique for Global Optimization

Das

Singh

2014

Advances in Intelligent Systems and Computing

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Solving non-linear optimization with more accuracy has become a challenge for the researchers. Evolutionary global search techniques today are treated as the alternate paradigm over the traditional methods for their simplicity and robust nature. However, if an evolutionary problem is computationally burdened both the human efforts and time will be wasted. In this paper a much simpler and more robust optimization algorithm called Drosophila Food-Search Optimization (DFO) Algorithm is proposed. This new technique is based on the food search behavior of the fruit fly called 'Drosophila'. In order to evaluate the efficiency and efficacy of the DFO-algorithm, a set of 20 unconstrained benchmark problems have been used. The numerical results confirms the supremacy of DFO over the algorithms namely Hybrid Ant Colony-Genetic Algorithm (GAAPI), Level-Set evolution and Latin squares Algorithm (LEA), which are reported as the most efficient algorithms in the recent literature.

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