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Aiming at the shortcoming of easily falling into local optima in solving numeric integral using particle swarm optimization (PSO), this paper proposed a chaotic PSO (CPSO) algorithm to solve numerical integral. The method arbitrarily selects certain nodes in the integral interval and optimizes these nodes through CPSO algorithm, finally obtains more accurate integral results. The proposed algorithm...
This research deals with the hybridization of the chaos driven heuristics concept and complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the influence of different randomization types for chaos-driven Differential Evolution (DE) through the analysis of complex network as a record of population dynamics. The population is visualized as an evolving...
In this study, we will use chaotic inertia weight into the Black Hole Algorithm (BH) in order to further enhance its global search ability. This study proposes a Chaotic Inertia Weight Black Hole Algorithm (CIWBH) method by using chaotic theory into Black Hole Algorithm. In CIWBH, chaos characteristics are combined with the BH algorithm with the intention of further enhancing its performance. Twenty-three...
In many Multi Agent Systems, under-education agents investigate their environments to discover their target(s). Any agent can also learn its strategy. In multitask learning, one agent studies a set of related problems together simultaneously, by a common model. In reinforcement learning exploration phase, it is necessary to introduce a process of trial and error to learn better rewards obtained from...
A modified differential evolution (DE) algorithm based on opposition based learning and chaotic sequence named Opposition based Chaotic Differential Evolution (OCDE) is proposed. The proposed OCDE algorithm is different from basic DE in two aspects. First is the generation of initial population, which follows Opposition Based Learning (OBL) rules; and the second is: dynamic adaption of scaling factor...
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