A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

Zheng, Genrang; Lin, ZhengChun

doi:10.1016/j.phpro.2012.03.292

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…• Hybrid ASFA (Neshat et al 2011;Zheng and Lin 2012;Wei et al 2010;Huang et al 2009;Zhang et al 2011;Wang and Ma 2011;Zhou and Huang 2009;Gao et al 2009;Yazdani et al 2010a;Jiang and Cheng 2010;Wu et al 2011b). …”

Section: Discussionmentioning

confidence: 99%

Fish Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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Section: Discussionmentioning

confidence: 99%

Fish Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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“…In the last two decades, researchers have proposed other algorithms inspired from the social and biological behaviors of species. In the literature we talk about swarm based intelligence [6][7][8]. The swarm bioinspired algorithms tend to imitate a given social behavior of some insects, weed, and birds.…”

Section: Introductionmentioning

confidence: 99%

Rate learning-based fish school search algorithm for global optimization

Aguercif

Tighzert

Mendil

et al. 2017

2017 6th International Conference on Systems and Control (ICSC)

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In this paper, we propose a new variant of fish school search algorithm, called rate-learning based fish school search algorithm (RL-FSSA), that uses a new refinement strategy to guide the population of fishes towards the best solutions. The fish motion is based on a collective sensorimotor behavior. Their learning ability determines the fish with the best position (i.e., leader). In each cycle, the best fish ever found is refined by a rate learning based process. This refinement allows a local search and decreases the effect of the hazard through time, and the whole algorithm goes from diversification towards intensification. In order to evaluate its performance, the proposed algorithm is tested under a set of benchmark functions. The numerical tests include unimodal and multimodal functions. The results show the high performance of RL-FSSA compared to the standard version FSSA. Furthermore, the computational time of the fish school seach algorithm is reduced. For the validation, the algorithm is used for the trajectory planning and control of the mobile robot in an environment containing obstacles.I.

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“…In this context, parameter estimation in engineering systems (Li et al, 2004), feed forward neural networks (Wang et al, 2005), combinatorial optimization problem (Cai, 2010), Augmented Lagrangian fish swarm based method for global optimization (Rocha et al, 2011), forecasting stock indices using radial basis function neural networks (Shen et al 2011. ), hybridization of the FSA with the Particle Swarm Algorithm to solve engineering systems (Tsai and Lin, 2011), determination of cluster number using average information entropy and density function based on fuzzy C-means cluster analysis and FSA (Zhu et al, 2005), combinatorial problems (Zheng and Lin, 2012), parameter estimation in control problems (Lobato et al, 2012), and other applications (Neshat et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Fish swarm optimization algorithm applied to engineering system design

Lobato

Steffen

2014

Lat. Am. j. solids struct.

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Nowadays, optimization techniques based on the analogy with swarming principles and collective activities of social species in nature have been used in the development of methodologies for solving a variety of real-world optimization problems. In this context, the social behavior of fish colonies has been recently explored to develop a novel algorithm, the so-called Fish Swarm Optimization Algorithm (FSOA), based on the behavior of fish swarm in search for food. In this paper, the FSOA is applied to four engineering systems, involving typical structural design and distillation column design. The results obtained are then compared with those obtained from other classical evolutionary approaches.

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A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

Cited by 12 publications

References 3 publications

Fish Inspired Algorithms

Fish Inspired Algorithms

Rate learning-based fish school search algorithm for global optimization

Fish swarm optimization algorithm applied to engineering system design

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