2013 IEEE Congress on Evolutionary Computation 2013
DOI: 10.1109/cec.2013.6557759
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Competitive differential evolution applied to CEC 2013 problems

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Cited by 50 publications
(34 citation statements)
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“…It is clear from the three tables that the proposed SinDE is much more powerful than both other approaches. In fact, it significantly outperforms the calssical DE in 75 cases out of 84 and the LADE in 55 In addition to its comparison to the classical variants, the proposed SinDE is compared against stronger recent variants of the DE algorithm, namely: The Super-fit Multicriteria Adaptive Differential Evolution (SMADE) [30], the Differential Evolution with Concurrent Fitness Based Local Search (DEcfbLS) [31], a powerful variant of the competitive differential Evolution [32] called b6e6rl [33] and the Teaching and Learning Based Self-adaptive DE (TLBSade) [34].…”
Section: Numerical Resultsmentioning
confidence: 99%
“…It is clear from the three tables that the proposed SinDE is much more powerful than both other approaches. In fact, it significantly outperforms the calssical DE in 75 cases out of 84 and the LADE in 55 In addition to its comparison to the classical variants, the proposed SinDE is compared against stronger recent variants of the DE algorithm, namely: The Super-fit Multicriteria Adaptive Differential Evolution (SMADE) [30], the Differential Evolution with Concurrent Fitness Based Local Search (DEcfbLS) [31], a powerful variant of the competitive differential Evolution [32] called b6e6rl [33] and the Teaching and Learning Based Self-adaptive DE (TLBSade) [34].…”
Section: Numerical Resultsmentioning
confidence: 99%
“…ELPSO is compared with conventional PSO (CPSO) [6], harmony search (HS) [31], genetic algorithm (GA) [32], firefly swarm optimisation (FSO) [33], gravitational search algorithm (GSA) [34], brainstorm optimisation (BSOA) [35] and artificial bee colony (ABC) [36]. Moreover, ELPSO will be compared with some modified variants of PSO and differential evolution (DE) including opposition-based DE (ODE) [37,38], competitive DE (CDE) [39], fixed inertia weight PSO (FIW-PSO), chaotic inertia weight PSO, random inertia weight PSO, time varying acceleration coefficient PSO (TVACPSO) and constricted Fig. 11.…”
Section: Resultsmentioning
confidence: 99%
“…(6) SPSRDEMMS, which is structured population size reduction differential evolution with multiple mutation strategies (Zamuda et al, 2013). (7) b6e6rl-CDE, which combines 12 different differential evolution strategies and parameter settings (Tvrdik and Polakova, 2013). Table 5 shows the results of the Holm multiple comparison test between SADE/BBO-A as the control algorithm, and all other algorithms, including the other hybrid algorithms proposed in this paper and the 2013 CEC algorithms.…”
Section: Statistical Testsmentioning
confidence: 99%