Planning the sports training sessions with the bat algorithm

Fister, Iztok; Rauter, Samo; Yang, Xin‐She; Ljubič, Karin

doi:10.1016/j.neucom.2014.07.034

Cited by 112 publications

(53 citation statements)

References 17 publications

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“…Büyüksaatçı [58] used BA to solve the single-row facility layout problem. Fister et al [59] proposed a modified BA for planning the sports training sessions, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel discrete bat algorithm for solving the travelling salesman problem

Saji

Riffi

2015

Neural Comput & Applic

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The travelling salesman problem (TSP) is one of the well-known NP-hard combinatorial optimization and extensively studied problems in discrete optimization. The bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. In this paper we extend a discrete bat-inspired algorithm to solve the famous TSP. Although many algorithms have been used to solve TSP, the main objective of this research is to investigate this discrete version to achieve significant improvements, not only compared to traditional algorithms but also to another metaheuristics. Moreover, this study is based on a benchmark dataset of symmetric TSP from TSPLIB library.Keywords Travelling salesman problem Á NP-hard combinatorial optimization problem Á Nature-inspired metaheuristic Á Discrete bat-inspired algorithm

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“…Büyüksaatçı [58] used BA to solve the single-row facility layout problem. Fister et al [59] proposed a modified BA for planning the sports training sessions, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel discrete bat algorithm for solving the travelling salesman problem

Saji

Riffi

2015

Neural Comput & Applic

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“…Usually, a particular competition for which an athlete is preparing, belongs to the training objective. In line with this, an off-line planning of the training sessions needs to be performed by the model [11]. This planning depends on the awareness of external factors, i.e., temperature, humidity, altitude, nutrition, and field conditions as well as internal factors like sex, age, maturity, and predispositions of an athlete.…”

Section: Computational Intelligence In Sportmentioning

confidence: 99%

Computational intelligence in sports: Challenges and opportunities within a new research domain

Ljubič

Suganthan

Perc

et al. 2015

Applied Mathematics and Computation

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a b s t r a c tComputational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain.

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“…A hybrid BA with path relinking was proposed by Zhou et al [116], where authors integrated the greedy randomized adaptive search procedure (GRASP) and path relinking into the BA, and applied to capacitated vehicle routing problem. Fister et al [76] created a hybrid BA (HBA) in order to combine the original BA with DE strategies as a local search instead of classic random walk. An extension of the SABA was done by the same authors in [31] where they hybridized the SABA (HSABA) also with ensemble DE strategies that were used as a local search for improving current best solution directing the swarm of a solution towards the better regions within a search space.…”

Section: Hybrid Si-based Algorithmsmentioning

confidence: 99%

Adaptation and Hybridization in Nature-Inspired Algorithms

Fister

Strnad

Yang³

2015

Adaptation, Learning, and Optimization

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Abstract. The aim of this chapter is to familiarize readers with the basics of adaptation and hybridization in nature-inspired algorithms as necessary for understanding the main contents of this book. Adaptation is a metaphor for flexible autonomous systems that respond to external changing factors (mostly environmental) by adapting their wellestablished behavior. Adaptation emerges in practically all areas of human activities as well. Such adaptation mechanisms can be used as a general problem-solving approach, though it may suffer from a lack of problem-specific knowledge. To solve specific problems with additional improvements of possible performance, hybridization can be used in order to incorporate a problem-specific knowledge from a problem domain. In order to discuss relevant issues as general as possible, the classification of problems is identified at first. Additionally, we focus on the biological foundations of adaptation that constitute the basis for the formulation of nature-inspired algorithms. This book highlights three types of inspirations from nature: the human brain, Darwinian natural selection, and the behavior of social living insects (e.g., ants, bees, etc.) and animals (e.g., swarm of birds, shoals of fish, etc.), which influence the development of artificial neural networks. evolutionary algorithms, and swarm intelligence, respectively. The mentioned algorithms that can be placed under the umbrella of computational intelligence are described from the viewpoint of adaptation and hybridization so as to show that these mechanisms are simple to develop and yet very efficient. Finally, a brief review of recent developed applications is presented.

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Planning the sports training sessions with the bat algorithm

Cited by 112 publications

References 17 publications

A novel discrete bat algorithm for solving the travelling salesman problem

A novel discrete bat algorithm for solving the travelling salesman problem

Computational intelligence in sports: Challenges and opportunities within a new research domain

Adaptation and Hybridization in Nature-Inspired Algorithms

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