Enhanced Differential Evolution using center-based sampling

Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms

“…The closeness of center-based candidates to solutions via Monte Carlo simulations for high dimensional problems is studied in [26]. Empirical studies given in [27][28][29] show the convergence speed gains that population-based algorithms achieve via this method. This algorithm is generalized in [30].…”

Micro-differential evolution: Diversity enhancement and a comparative study

“…Due to reliability and simplicity of the DE algorithm, it has been employed in many science and engineering areas, such as, solving large capacitor placement problem [17] and synthesis of spaced antenna arrays [18]. Many research works have been conducted to enhance the DE algorithm, such as opposition-based differential evolution (ODE) [14], enhanced differential evolution using center-based sampling [15], and opposition-based adaptive differential evolution [16]. Some approaches toward reducing computational cost of DE-based algorithms by reducing the population size have been proposed, [8]- [9], [11]- [13].…”

Enhanced differential evolution using random-based sampling and neighborhood mutation

“…These methods are various degree of enhancement the performance of DE. These improved methods include DE with neighborhood mutation (Qu et al 2012), center-based sampling mutation for DE (Esmailzadeh and Rahnamayan 2011), DE with a mix of different mutation operators (Elsayed et al 2013), learning-enhanced DE (Cai et al 2012), DE with proximitybased mutation operators Epitropakis et al (2011), DE with strategy adaptation ), fitness-based adaptation of the control parameters for DE (Ghosh et al 2011). There are also work have done on initial population generation methods (Ali et al 2013;De Melo and Delbem 2012;.…”

“…This new algorithm is enhanced DE using randombased sampling and neighborhood mutation, referred to as NRDE. Random-based sampling is motivated by centerbased sampling (Esmailzadeh and Rahnamayan 2011). In this paper, random-based sampling is used to search the promising area between the centers of the subpopulation and the center of the population.…”