Firefly algorithm with chaos

Gandomi, Amir H.; Yang, Xin-She; Talatahari, Siamak; Alavi, Amir H.

doi:10.1016/j.cnsns.2012.06.009

Cited by 768 publications

(322 citation statements)

References 21 publications

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“…In 2012, Gandomi et al, proposed a chaos-enhanced version of accelerated particle swarm optimization [9]. Some of other chaos-enhanced heuristic algorithms are chaotic Genetic Algorithms [10], chaotic Differential Evolution [11], chaotic Simulated Annealing [12], and chaotic Firefly Algorithm [13], [14]. The result of these studies evidences the successfulness of chaos in improving the performance of heuristic algorithms.…”

Section: Introductionmentioning

confidence: 95%

Integrating Chaos to Biogeography-Based Optimization Algorithm

Saremi¹,

Mirjalili²

2013

IJCCE

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Section: Introductionmentioning

confidence: 95%

Integrating Chaos to Biogeography-Based Optimization Algorithm

Saremi¹,

Mirjalili²

2013

IJCCE

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“…Later on, the chaos embedded PSO with inertia weigh strategy was closely investigated [9], followed by the introduction of a PSO strategy driven alternately by two chaotic systems [10]. Recently the chaos driven firefly algorithm has been introduced [11], chaotic differential bee colony [12]and the concept of chaos driven DE has become more intensively studied [13], [14].…”

Section: (Cprng)mentioning

confidence: 99%

On the adaptivity and complexity embedded into differential evolution

Šenkeřík

Pluháček

Zelinka

et al. 2016

AIP Conference Proceedings

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Abstract. This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.

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“…Chaos is a deterministic, random-like process found in nonlinear, dynamical system, which is non-period, non-converging and bounded. Moreover, it depends on its initial condition and parameters [22][23][24]. Applications of chaos in several disciplines including operations research, physics, engineering, economics, biology, philosophy and computer science [25][26][27].…”

Section: The Proposed Algorithm (Ifch) For Unconstrained Optimizationmentioning

confidence: 99%