Dušan Fister

Cuckoo Search: A Brief Literature Review

Cuckoo search (CS) was introduced by Xin-She Yang and Suash Deb in 2009, and it has attracted great attention due to its promising efficiency in solving many optimization problems and real-world applications. In the last few years, many papers have been published regarding cuckoo search, and the relevant literature has expanded significantly. This chapter summarizes briefly the majority of the literature about cuckoo search in peer-reviewed journals and conferences found so far. These references can be systematically classified into appropriate categories, which can be used as a basis for further research.

A comprehensive review of cuckoo search: variants and hybrids

Cuckoo search (CS) is an efficient swarm-intelligence-based algorithm and significant developments have been made since its introduction in 2009. CS has many advantages due to its simplicity and efficiency in solving highly non-linear optimisation problems with real-world engineering applications. This paper provides a timely review of all the state-of-the-art developments in the last five years, including the discussions of theoretical background and research directions for future development of this powerful algorithm.

Firefly Algorithm: A Brief Review of the Expanding Literature

Firefly algorithm (FA) was developed by Xin-She Yang in 2008 and it has become an important tool for solving the hardest optimization problems in almost all areas of optimization as well as engineering practice. The literature has expanded significantly in the last few years. Various FA variants have been developed to suit different applications. This chapter provides a brief review of this expanding and state-of-the-art literature on this dynamic and rapidly evolving domain of swarm intelligence.

Towards the novel reasoning among particles in PSO by the use of RDF and SPARQL.

The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.

Analysis of randomisation methods in swarm intelligence

Nowadays, many stochastic metaheuristics have been developed to solve various optimisation problems. The primary characteristics of these heuristics often involve the use of randomness in their search process. Essentially, randomness is useful when determining the next point in the search space and therefore has a crucial impact when exploring new solutions. In this paper, an extensive comparison is made between various probability distributions that can be used for randomising the swarm intelligence algorithms, e.g., uniform, Gaussian, Levy flights, chaotic maps, and the random sampling in turbulent fractal cloud. These randomisation methods were incorporated into the bat algorithm that is one of the newest member of this domain. In line with this, various variants of bat algorithms randomised with different randomisation methods have been developed and extensive experiments were conducted on a well-known set of 24 BBOB benchmark functions. In addition, the results of randomised bat algorithms were compared with the results of the other well-known algorithms, including the firefly algorithm, differential evolution and artificial bee colony algorithms. The results of these experiments show that the efficiencies of the distributions used during the tests depend on the problem to be solved as well as on the algorithm used.

Differential evolution strategies with random forest regression in the bat algorithm

In this paper, we present a novel solution for the hybridization of the bat algorithm with differential evolution strategies and a random forests machine learning method. Extensive experiments and tests on standard benchmark functions have shown that these hybridized algorithms improved the original bat algorithm significantly.

Parameter tuning of PID controller with reactive nature-inspired algorithms

A PID controller is an electrical element for reducing the error value between a desired setpoint and an actual measured process variable. The PID controller operates according to its input parameters, which need to be set before its run. The optimal values of these parameters must be found during the so-called tuning process. Today, this process can be automatized using stochastic, nature-inspired, population-based algorithms, such as evolutionary and swarm intelligence-based algorithms. Unfortunately, these algorithms are too time consuming, and so the reactive, nature-inspired algorithms using a limited number of fitness function evaluations are proposed in this paper. Two reactive evolutionary algorithms (differential evolution and genetic algorithm), and four reactive, swarm intelligence-based algorithms (bat, hybrid bat, particle swarm optimization and cuckoo search), were used to tune the PID controller in our comparative study. Only ten individuals and ten iterations (generations) were used in order to select the most appropriate optimization algorithm for fast tuning of controller parameters. The results were compared using statistical analysis and showed that particle swarm optimization is the best option for such a task. PSO is the most reactive nature-inspired algorithm among BA, HBA, GA, DE, CS and PSO.Population based nature-inspired algorithms (e.g.,źPSO, BA, HBA, DE and CS) can be used for online implementation of PID parameter tuning.Low population sizes in nature-inspired algorithms are sufficient for PID tuning to obtain reactive response of SCARA robot.

Modeling preference time in middle distance triathlons

Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e.g., athletes abilities, health, mental preparations and even their current sports form. So far, this process was calculated manually without any specific software tools or using the artificial intelligence. This paper presents the new solution for modeling preference time in middle distance triathlons based on particle swarm optimization algorithm and archive of existing sports results. Initial results are presented, which suggest the usefulness of proposed approach, while remarks for future improvements and use are also emphasized.

Making up for the deficit in a marathon run

To predict the final result of an athlete in a marathon run thoroughly is the eternal desire of each trainer. Usually, the achieved result is weaker than the predicted one due to the objective (e.g., environmental conditions) as well as subjective factors (e.g., athletes malaise). Therefore, making up for the deficit between predicted and achieved results is the main ingredient of the analysis performed by trainers after the competition. In the analysis, they search for parts of a marathon course where the athlete lost time. This paper proposes an automatic making up for the deficit by using a Differential Evolution algorithm. In this case study, the results that were obtained by a wearable sports-watch by an athlete in a real marathon are analyzed. The first experiments with Differential Evolution show the possibility of using this method in the future.

Generating eating plans for athletes using the particle swarm optimization

This paper presents the automatic generation of optimal eating plans for athletes. The automatic generation of the eating plans is introduced as an optimization problem, where particle swarm optimization is taken as the problem solver. Inputs for the proposed particle swarm optimization algorithm are generated training plan and list of the potential meals, while the output of the algorithm represents a list of meals that should be consumed by the athletes. The first practical experiments showed that this solution is very promising.